semvec/pypi-packages huggingface dataset:
data-stream-kit 0.0.3
数据处理工具

### data-stream-kit

数据流处理工具包

主要功能：

1. **data_stream_kit.sqla_decorator**: sqlalchemy的高度封装，使用线程数据，实现修饰器的方式调用session。
2. **data_stream_kit.sqla_asyncio**: 简单封装了异步sqlalchemy的session。
3. **data_stream_kit.kafka_lib**: 封装了kafka的生产者KafkaProducer和消费者KafkaConsumer。
4. **data_stream_kit.kafka_asyncio**: 异步的kafka生产者AIOProducer和消费者AIOConsumer。



打包流程：

1. 生成wheel文件和源代码压缩包：`python setup.py sdist bdist_wheel`。
2. 上传生成的文件：`twine upload dist/*`。之后输入账号密码。
mobi-header 0.0.4
A python library for reading/editing mobi/azw3 metadata.
Requires Python: >=3.7

# mobi_header
A python library for reading/editing mobi/azw3 metadata.

**Install:**  `pip install mobi_header`

# class

## MobiHeader

**variables:**

* file_path: str, path of mobi/azw3 file.
* palm_doc: a PalmDoc object, contain palmdoc's header and records.
* metadata: a python dict, use offset as key, the metadata of file.
* exth_value: a python list, the exth metadata of file.

**method:**

* change_title(title : str):
  
    ​	change the title of metadata.

* change_metadata(id : int, value):
  
    ​	change the mobi metadata. the value will be convert to origin dtype.

* change_exth_metadata(id : int, value):
  
    ​	change the mobi exth metadata. the value will be convert to origin dtype.

* get_exth_value_by_id(id : int):
  
    ​	return the value of id in exth metadata.
  
* get_exth_value_by_name(name : str):
  
    ​	return the value of name in exth metadata.

* to_file(file : [str, None] = None):
  
    ​	write metadata to file. Default write to origin file.

About metadata and exth-metadata, the detail see [here](https://wiki.mobileread.com/wiki/Mobi#EXTH_Header)

## Example

```python
from mobi_header import MobiHeader
foo = MobiHeader("example.azw3")
foo.change_exth_metadata(501, "PDOC")
foo.to_file()
```
toori-server 1.1.10
Server for a minimal layer 3 tunnel over http(s).
Requires Python: >=3.7

# Iro 

[![GHCR](https://github.com/kokseen1/toori-server/actions/workflows/ghcr.yml/badge.svg)](https://github.com/kokseen1/toori-server/actions/workflows/ghcr.yml)
[![PyPI Release](https://github.com/kokseen1/toori-server/actions/workflows/release.yml/badge.svg)](https://github.com/kokseen1/toori-server/actions/workflows/release.yml)
[![PyPI Version](https://img.shields.io/pypi/v/toori-server.svg)](https://pypi.python.org/pypi/toori-server/)

Server for a minimal layer 3 tunnel over http(s).

## Deploying with Docker

Instead of installing toori-server on the host, it can be deployed as a Docker container with much convenience.

Pull from GHCR:

```shell
sudo docker pull ghcr.io/kokseen1/toori-server:latest
```

Run the container:

```shell
sudo docker run -d --cap-add=NET_ADMIN -p 80:80 ghcr.io/kokseen1/toori-server
```

#### HTTPS

To deploy with HTTPS, run the container with a bind mount and pass the port and certs directory as arguments:

```shell
sudo docker run -d -v /etc/letsencrypt:/etc/letsencrypt --cap-add=NET_ADMIN -p 443:443 ghcr.io/kokseen1/toori-server 443 /etc/letsencrypt/live/toori.server/
```

## Installation

```shell
pip3 install toori-server --no-binary :all:
```

## Prerequisites

- [Libtins](http://libtins.github.io/download/) (optional, will fallback to Scapy (slow) if not installed)

### RST Packets

Because the Linux kernel sends a `RST` to connections it did not establish, use the following command to ensure that outgoing packets are sent successfully:

```shell
sudo iptables -A OUTPUT -p tcp --tcp-flags RST RST -s <local address> -j DROP
```

[See here](https://stackoverflow.com/questions/9058052/unwanted-rst-tcp-packet-with-scapy) for more information.

## Usage

Run with root permissions:

```shell
iro <port>
```

Example with HTTPS:

```shell
iro 443 -c "/etc/letsencrypt/live/toori.server/"
```
daemonless-queuing 0.0.6
todo
Requires Python: >=3.11

# Daemonless Queuing

Simple and reliable Redis queuing utilities.

## Install

```sh
$ pip install daemonless-queuing
```

## Usage

### Queuing

```python
import redis
from daemonless_queuing import setup, shutdown

instance = redis.Redis(
    host='localhost',
    port=6379
)

enqueue = setup(instance, {
    'queues': [
        'TESTCHAN_1',
        'TESTCHAN_2'
    ]
})

# the third positional parameter is a integer (or None) representing the timeout in seconds
# after the timeout is reached the function subprocess will be killed
enqueue('TESTCHAN_1', 'my_package.my_module.func_name' 0)
enqueue('TESTCHAN_2', 'my_package.my_module.func_name', 0, 'positional argument', 42, named_arg='Hey!')
# ...

# do some blocking stuff

shutdown()
```

### Locking

```python
import redis
from daemonless_queuing import make_lock

instance = redis.Redis(
    host='localhost',
    port=6379
)

lock = make_lock(instance, 'SOMETHING_ONGOING')

with lock():
    # will raise if you try to run this scope again before the lock gets released
    ...
```

## Why?

I couldn't find a better suiting solution for what I needed (enqueuing stuff in multiple queues then running the jobs in parallel). Existing solutions ([rq](https://python-rq.org/), [Celery](https://docs.celeryq.dev/en/stable/)) are hard to setup and require a daemon/broker to work. Of course this is a much simpler version of these libraries, but some don't need more than that.


## Caveats

- Functions must be provided as Python import paths
- Function parameters must be JSON-serializable
- Return values can't be accessed

## Race condition

The function returned by `make_lock` is susceptible to race condition at the moment.
Make sure your asynchronous function won't be called twice at the same time.


## License

This library is [MIT licensed](https://github.com/capsulbrasil/normalize-json/tree/master/LICENSE).
opsramp-analytics-utils-server-status 1.1.2
OpsRamp Analytics SDK
Requires Python: >=3.6

## OpsRamp Analytics Utilities

This is the SDK for writing OpsRamp analytics apps. It is based on [dash](https://plotly.com/dash/), and it has a number of utility functions.

It contains [analysis wrapper project](https://github.com/opsramp/analysis-wrapper).

It is published on [Pypi](https://pypi.org/project/opsramp-analytics-utils/)

#### How to publish on Pypi

After make updates on SDK, modify the version in _setup.py_.

```
python setup.py sdist bdist_wheel
python -m twine upload dist/*

Note: if above command not works, then use below command
python -m twine upload --skip-existing dist/*
```

- To upgrade the sdk for your app
```
pip install --no-cache-dir --upgrade opsramp-analytics-utils
```