semvec/pypi-packages huggingface dataset:
flaskapi4 0.1.8
flaskapi4
Requires Python: <4.0,>=3.9

## Requirements

Python 3.9+

flaskapi4 is dependent on the following libraries:

- [Flask](https://github.com/pallets/flask) for the web app.
- [Pydantic](https://github.com/pydantic/pydantic) for the data validation.

## Installation

```bash
pip install -U flaskapi4
```

## A Simple Example

Here's a simple example, further go to the [Example](https://luolingchun.github.io/flask-Flaskapi3/latest/Example/).

```python
from pydantic import BaseModel

from flaskapi4 import Info, Tag
from flaskapi4 import Flaskapi

info = Info(title="book API", version="1.0.0")
app = Flaskapi(__name__, info=info)

book_tag = Tag(name="book", description="Some Book")


class BookQuery(BaseModel):
    age: int
    author: str

class ResultData(BaseModel):
    code: int
    message: str
    data: dict
    
@app.get("/book", summary="get books", tags=[book_tag])
def get_book(query: BookQuery) -> ResultData:
    """
    to get all books
    """
    return {
        "code": 0,
        "message": "ok",
        "data": [
            {"bid": 1, "age": query.age, "author": query.author},
            {"bid": 2, "age": query.age, "author": query.author}
        ]
    }


if __name__ == "__main__":
    app.run(debug=True)
```
dotfile-sync 0.0.6
dotfile sycn auxiliary tool for scp
Requires Python: >=3.8

# dotfile-sync

An auxiliary tool for syncing dotfiles using `scp`.

The only usage is:

```bash
dotfile-sync <remote-host> <remote-path> <local-path>
```

Example:

![example](assets/example.gif)

which is roughly equivalent to:

```bash
# Make tmpdirs
tmp=$(mktemp -d)
mkdir -p $tmp/local
mkdir -p $tmp/remote

# We work on tmps
cp ~/.zshrc $tmp/local/.zshrc
scp <remote-host>:~/.zshrc $tmp/remote/.zshrc

# Show diff
git diff --no-index $tmp/local/.zshrc $tmp/remote/.zshrc

# If you choose to edit interactively
git difftool --no-index $tmp/local/.zshrc $tmp/remote/.zshrc

# If you choose to sync with local tmp
#   local backup
cp ~/.zshrc ~/.zshrc.dsbak
#   sync
cp $tmp/local/.zshrc ~/.zshrc
```

## Install

```bash
pipx install dotfile-sync
```
cus-lib 0.0.2
custom common fun
Requires Python: >=3.8

# `__init__.py`中写法
1. 使用方式
2. 导包，可选

```
cus_lib

Usage:

    from cus_log import setup_logger

    logger = setup_logger("test.log")
    logger.info('test cus_lib!')
"""

# after you launch code,can see 'test.log' file. 
```

# build

```bash
py -m build
```

# 分发

```bash
twine upload dist/*
# 输入token
```
bpcells 0.3.0rc2
BPCells python bindings
Requires Python: >=3.8

# BPCells

BPCells is a package for high performance single cell analysis on RNA-seq and ATAC-seq datasets. It can analyze a 1.3M cell dataset with 2GB of RAM in around 10 minutes (benchmarks). This makes analysis of million-cell datasets practical on a laptop.

The main BPCells interface is in R and the python bindings are still in an 
experimental phase, so the API is subject to change.

The existing functionality is mainly focused on allowing read/write access to BPCells
file formats for integer matrices and scATAC fragments. Future updates will add the
data-processing functions present in the R interface (e.g. streaming normalization, PCA,
or ATAC-seq peak/tile matrix creation). This will provide Python access to the shared
C++ core code.

Notably, plotting functionality is not currently planned for implementation, as it is 
written primarily in R and relies on R plotting libraries not present in Python. There
are a few other helper functions in R BPCells that are implemented in pure R and thus
are unlikely to be added in Python in the near future. If any of this functionality
is of interest to you, we would welcome your contributions -- you would be able
to write most of the code in pure Python. Reach out via github/email if interested.

The current BPCells python bindings provide limited access to BPCells functionality. This
mainly focuses on integer matrix slicing / import, and pseudobulk fragment insertion counts.
The API is subject to change somewhat once a full-featured port of the BPCells R 
functionality is completed.

For more information, see our [github](https://github.com/bnprks/BPCells), [python docs](https://bnprks.github.io/BPCells/python), and [R docs](https://bnprks.github.io/BPCells/).
restfx 0.32.0
A RESTful framework for Python3.
Requires Python: >=3.6

# restfx

Python3 的 RESTful 服务框架。

> 底层基于 [werkzeug](https://werkzeug.palletsprojects.com/)

## 为什么要使用此框架

开发此框架的目标是 **提升开发效率**。

此框架解决了以下问题：

- 没有繁锁的路由配置，免去路由注册。仅仅需要对模块根进行注册，模块下的所有路由都会自动被收集调用
- 不需要对路由 url 进行显示配置，完全自动解析 
- 自动解析/校验请求参数，并填充到路由函数，省去繁锁的参数获取/类型校验。需要做的仅仅是编写一个函数，并添加函数参数的类型声明 
- 提供 **接口列表页面** 以及接口测试支持，让接口随码更新，不用手动维护API文档。 见[截图](#截图)
- 提供 [路由注入][2] 支持，以通过参数方式向路由指定请求参数外的数据/函数，从而避免一些频繁的 `import` 和重复代码

**此框架的弊端: 不支持将参数作为 url 路径的一部分**

## 安装

```shell script
pip install restfx
```

`Since 0.7.1` 安装后，可以通过 CLI 工具 `restfx` 命令创建基本项目结构:

```shell script
restfx create projectname
```

> 使用此命令，可能需要将 `restfx` 安装到全局环境中。

## 文档

使用文档见 [Gitee Wiki][1]

## 创建应用

```python
import os

import restfx

if __name__ == '__main__':
    root = os.path.dirname(__file__)
    app = restfx.App(root, api_prefix='any/prefix', debug=True)
    app.map_routes({
        'x': 'test'
    })
    app.map_static(static_map={})
    app.startup(host='127.0.0.1', port=9127)
```

### 编写路由

*test/api/demo.py*

```python
from restfx import route
from restfx.http import HttpRequest, HttpFile


@route(module='测试名称-模块', name='测试名称-GET')
def get(request, param1, param2=None, param3: int = 5):
    # request 会是 HttpRequest
    return {
        'param1': param1,
        'param2': param2,
        'param3': param3,
    }


@route(module='测试名称-模块', name='测试名称-POST_PARAM')
def get_param(param1, req: HttpRequest, from_=None, param3=5):
    # req 会是 HttpRequest
    return {
        'param1': param1,
        'from': from_,
        'param3': param3,
    }


@route(module='测试名称-模块', name='测试名称-PUT_PARAM')
def put(request: str, param1, file: HttpFile, param3=5):
    # request 会是请求参数，参数列表中没有 HttpRequest
    return {
        'request': request,
        'param1': param1,
        'param3': param3,
    }


@route(module='测试名称-模块', name='测试名称-DELETE_PARAM')
def delete(request, param1, from_=None, param3=5, **kwargs):
    # 未在函数的参数列表中声明的请求参数，会出现在 kwargs 中
    return {
        'param1': param1,
        'from': from_,
        'param3': param3,
        'variable_args': kwargs
    }

```

## 截图

以下截图为接口列表，对应的路由声明源码见

- [test/test/api/__init__.py][11]
- [test/test/api/demo.py][12]


以下截图仅在 [Gitee仓库目录][3] 可见

![list](assets/1.png)

![test](assets/2.png)

[1]: https://gitee.com/wangankeji-fe/restfx/wikis
[2]: https://gitee.com/wangankeji-fe/restfx/wikis/0B.%20%E8%B7%AF%E7%94%B1%E6%B3%A8%E5%85%A5?sort_id=3519061
[3]: https://gitee.com/wangankeji-fe/restfx#%E6%88%AA%E5%9B%BE
[11]: https://gitee.com/wangankeji-fe/restfx/blob/master/test/test/api/__init__.py
[12]: https://gitee.com/wangankeji-fe/restfx/blob/master/test/test/api/demo.py