semvec/pypi-packages huggingface dataset:
lagoo 0.2.3
Lagoo
Requires Python: >=3.6

# **Lagoo : Enjoy Algo as Lego**

[使用文档 for简易使用][2]

[开发文档 for开发者][1]

[GitLab][4]

[PyPi官方主页][3]

`tensorflow-2.5.0
python-3.8`

`pip install lagoo`




[1]: https://bytedance.feishu.cn/docs/doccnoqrCHEwjXAGwSZFTU4Wyab

[2]: https://bytedance.feishu.cn/docs/doccnAiHNMDpKCm04IzhbNlc5Pf#

[3]: https://pypi.org/project/lagoo/

[4]: https://code.byted.org/growth/lago
cgai-DD 0.0.1
Python API for DingDing Group Robot.

# cgai-DD

#### 介绍
钉钉群机器人的python API接口



#### 安装教程

```python
pip install cgai-DD
```

#### 注意  

1.仅支持python3  
2.发送的图片路径必须是网络路径  
3.发送的图片格式图必须是jpg格式

#### 使用案例

```python
import cgai_DD as dd
```

```python
WEBHOOK = 'https://oapi.dingtalk.com/robot/send?access_token=xxxxxx'
```

###### 发送文本信息
```python
dd.sendMessage(WEBHOOK,'发送的内容')
```


###### 发送连接
```python
message_url = r'https://zhuanlan.zhihu.com/p/391348944'
title = 'CGAI-time'
text = 'cgai-time一个简单又实用的时间日期处理python库'
pic_url = 'https://obohe.com/i/2021/08/06/ikclyn.jpg'

dd.sendLink(WEBHOOK,message_url,title,text,pic_url)
```


###### 发送markdown
```python
pic_url = 'https://obohe.com/i/2021/08/06/e1qrsr.png'
markdown = """
## cgai-time一个简单又实用的时间日期处理python库
体育老师再也不用担心我算不出时间了
![img](https://obohe.com/i/2021/08/06/ikclyn.jpg)
###### 帮助说明 [链接](https://zhuanlan.zhihu.com/p/391348944)
"""
title = 'cgai-time分享'

dd.sendMarkdown(WEBHOOK,markdown,title)
```


###### 发送ActionCard
```python
markdown = """
![img](https://obohe.com/i/2021/08/06/ikclyn.jpg)
### cgai-time
"""
showTitle = 'cgai-time'
singleTitle = '阅读文章'
url = 'https://zhuanlan.zhihu.com/p/391348944'
dd.sendActionCard(WEBHOOK,markdown,showTitle,singleTitle,url,btnOrientation=1)


btns = [{"title":'链接1',"actionURL":url},{"title":'链接2',"actionURL":url}]
dd.sendActionsCard(WEBHOOK,markdown,showTitle,singleTitle,url,btns,btnOrientation=0)

```

###### 发送Freecard
```python
url = 'https://zhuanlan.zhihu.com/p/391348944'
pic_url = 'https://obohe.com/i/2021/08/06/ikclyn.jpg'

links = [{"title":"标题1","messageURL":url,"picURL":pic_url},
         {"title":"标题2","messageURL":url,"picURL":pic_url}]

dd.sendFreeCard(WEBHOOK,links)
```
cgspan-mining 1.0.1
Implementation of close frequent subgraph mining algorithm cgSpan

# <div align = center>cgSpan</div>

**cgSpan** is an algorithm for mining closed frequent subgraphs. This implementation of cgSpan
is built using an existing implementation for gSpan.

**gSpan** is an algorithm for mining frequent subgraphs.

This program implements cgSpan with Python. The repository on GitHub is [https://github.com/NaazS03/cgSpan](https://github.com/NaazS03/cgSpan)

The gSpan implementation referenced by this program can be found on GitHub at [https://github.com/betterenvi/gSpan](https://github.com/betterenvi/gSpan).

### Undirected Graphs
This program supports undirected graphs.

### How to install

This program supports **Python 3**.

##### Method 1

Install this project using pip:
```sh
pip install cgspan-mining
```

##### Method 2

First, clone the project:

```sh
git clone https://github.com/NaazS03/cgSpan.git
cd cgSpan
```

You can ***optionally*** install this project as a third-party library so that you can run it under ***any*** path.

```sh
python setup.py install
```

### How to run

The command is:

```sh
python -m cgspan_mining [-s min_support] [-n num_graph] [-l min_num_vertices] [-u max_num_vertices] [-v True/False] [-p True/False] [-w True/False] [-h] database_file_name 
```


##### Some examples

- Read graph data from ./graphdata/graph.data, and mine closed subgraphs given min support is 5000

```
python -m cgspan_mining -s 5000 ./graphdata/graph.data
```

- Read graph data from ./graphdata/graph.data, mine closed subgraphs given min support is 5000, and visualize these frequent subgraphs(matplotlib and networkx are required)

```
python -m cgspan_mining -s 5000 -p True ./graphdata/graph.data
```

- Print help info

```
python -m cgspan_mining -h
```

### Reference
- [CloseGraph Paper](https://sites.cs.ucsb.edu/~xyan/papers/CloseGraph.pdf)

CloseGraph: Mining Close Frequent Graph Patterns, by X. Yan and J.Han.

- [gSpan Paper](http://www.cs.ucsb.edu/~xyan/papers/gSpan-short.pdf)

gSpan: Graph-Based Substructure Pattern Mining, by X. Yan and J. Han. 
Proc. 2002 of Int. Conf. on Data Mining (ICDM'02).
pypaxtor 0.1.0
Cross-platfom provisioning of home directory storage

## pypaxtor: Package Storage provisioning

Simple module that provides a path to a storage location
under the user home directory in a cross-platform manner.

Under MacOS this will be in `~/Library/Application Support`;
under Windows it will be in `$HOME/AppData/Local`;
and under Linux it will be in `~/.local/share`.

Packages using this module supply a package name when requesting a storage location.
A directory with that name is created under the directory named `PyPaxtor`
in the os-appropriate location.

### Usage:

    >>> import pypaxtor
    >>> savedir = pypaxtor.get_storage_location('my_package')
    >>> savedir  # if on Mac as user `rlyacht`
    PosixPath('/Users/rlyacht/Library/Application Support/pypaxtor/my_package')
    >>> with open(savedir / 'SaveFile.txt') as savefile:
    ...     # do stuff

If the above code is executed on a linux system,
the saved file will be located at `/home/rlyacht/.local/share/pypaxtor/my_package/SaveFile.txt`.