semvec/pypi-packages huggingface dataset:
nali-scraper 2025.7.21
대학별 공지사항 크롤링을 위한 통합 스크래핑 도구
Requires Python: >=3.10

<div style="text-align: center">
  <img src="1.png"  alt="1"/>
  <h3>nali</h3>
  <p>대학별 공지사항 크롤링을 위한 통합 스크래핑 도구</p>
</div>

## Legal Disclaimer

이 프로젝트는 비공식 프로젝트이며, 특정 대학교 또는 기관과 아무런 공식적인 관련이 없습니다. 모든 데이터는 공개된 웹페이지를 기반으로 수집되며, 수집된 정보의 정확성이나 최신성은 보장되지 않습니다.

사용자는 본 소프트웨어를 사용할 때 발생할 수 있는 모든 책임을 스스로 부담하며, 개발자는 이로 인해 발생하는 어떠한 직접적, 간접적 손해에 대해서도 책임지지 않습니다.

# nali

## Overview

nali 패키지(이하 '날리')는 대학별 공지사항을 스크래핑하기 위한 Python 라이브러리입니다.

모듈화되고 추상화된 설계 방식을 사용하여 새로운 대학교를 쉽게 확장할 수 있도록 하였으며, 외부 의존성은 `requests`와 `beautifulsoup4`만으로 최소화했습니다.

예외 처리, 명확한 인터페이스, 그리고 기여자가 쉽게 확장할 수 있는 구조를 보장하도록 노력하고 있습니다.

## Quick start

```pycon
>>> from nali import Nali, School
>>> n = Nali(School.SOME_UNIV)
>>> notices = n.get_notices(page=1, search="장학금")
```

## Installation

```shell
$ pip install nali
```

## Package Structure

```
nali/
 ├── src/  # 실제 패키지 소스
 │    └── nali/
 │         ├── scrapers/
 │         │    ├── __init__.py
 │         │    ├── base.py
 │         │    ├── some_univ.py
 │         │    └── ...
 │         ├── __init__.py
 │         ├── core.py
 │         ├── enums.py
 │         ├── exceptions.py
 │         └── models.py
 ├── tests/  # 테스트 코드
 │    ├── __init__.py
 │    └── test_scrapers.py
 └── pyproject.toml
```

## Contributing

> 이걸 보고 계시는 여러분의 도움이 필요합니다!
>
> 오픈소스 프로젝트에 거리낌 없이 기여해주세요. 늘 기다리고 있습니다. :D

다음과 같은 기여를 하실 수 있습니다:

- 대학교 스크래퍼 신규 추가 ⭐
- 웹페이지 구조 변경에 따른 스크래핑 로직 수정
- 모듈의 소스코드 오류 수정

[기여 가이드라인](https://github.com/wizley9999/nali/blob/main/CONTRIBUTING.md)을 확인해주세요.

## License

MIT 라이선스를 따르고 있습니다. 자세한 내용은 `LICENSE`를 확인하세요.
vavacars-data-utils 1.5.11
Package with utils
Requires Python: >=3.6

# Vavacars

Vavacars utils library.

## Installation

To use the library, simply install it with pip or add into requirements.txt

```
python3 -m pip install --upgrade vavacars_data_utils
```

## Usage

The library includes a series of common utilities we are using in Vavacars

#### SIQ
- Wrapper to get quotations from SIQ
- Helper_v2 adds querying Redis Cache before asking SmartIQ

#### Camunda DMN
- Wrapper to query Camunda DMN for initial offer (strategies)

#### SQL
- Wrapper to run querys against a MySQL/SQL Server
- SQL Server requires Microsfot OBDC Driver to be installed, for Ubuntu 20.04 use the following comamnds:
```
curl https://packages.microsoft.com/config/ubuntu/20.04/prod.list | tee /etc/apt/sources.list.d/msprod.list
apt-get update && ACCEPT_EULA=y DEBIAN_FRONTEND=noninteractive apt-get install -y mssql-tools unixodbc-dev && pip install pymssql
```

#### Azure Helper
- Wrapper to around Azure ML SDK for registering and reploying models

#### BigQuery Helper
- Wrapper to run queries against BigQuery

#### SalesForce Helper
- Wrapper to retrieve data from SalesForce

#### Email
- Wrapper to send emails via Email Communication Resource in Azure

#### Turkey specifics:
- turkish_to_datetime(str): Date conversion from turkish dates

#### Other:
- get_best_match(value, list_values): Find closest string in a list (used for bodytypes, trimlevels, ...)
- deep_get(dictionary, nested_keyss)
- return_on_failure(f,v): Run function f and return v if f yields any exception in other case it will retun f() result
- extract_json_objects(t): Look Json objects in the text t

## Building

First, update at least the version number in setup.cfg

Next, install build package in your environment:

```
python3 -m pip install --upgrade build
```

Then you can run `python3 -m build` to generate the distribution, this will generate the distribution files under dist/ folder. Check the folder as it may contain the files from the previous build.

## Publishing

To publish it we are using twine, so first install it:

```
python3 -m pip install --upgrade twine
```

And then you can publish it running (from this folder), it will ask you for credentials in the registry:

```
twine upload -r pypi dist/*
```

For more details, check:
https://packaging.python.org/en/latest/tutorials/packaging-projects/
mkdocstrings-twincat 0.3.6
A Twincat handler for mkdocstrings.
Requires Python: >=3.11

# mkdocstrings-twincat

[![documentation](https://img.shields.io/badge/docs-mkdocs-708FCC.svg?style=flat)](https://drehstromer.github.io/mkdocstrings-twincat/)
[![pypi version](https://img.shields.io/pypi/v/mkdocstrings-twincat.svg)](https://pypi.org/project/mkdocstrings-twincat/)

A Twincat handler for mkdocstrings.

## Installation

```bash
pip install mkdocstrings-twincat
```

This handler is using pytwincatparser to parse Twincatfiles. Currently supported are:

- .plcproj
- .tcio
- .tcpou
- .tcgvl
- .tcdut

The handler can load recursivly all objects. You just have to add the .plcproj file, and it generates the documentation for all Function Blocks etc.


## Demo
At the project page you can find a demo of this handler. It parses the source code of the famous [TcUnit](https://github.com/tcunit/TcUnit) TwinCAT unit testing framework and displays it. The documentation is not optimal, but this is because the tags are not used by tcunit.


## Used packages
- mkdocstrings
- mkdocstrings-python (heavily as inspiration. really great code!)
- pytwincatparser


## Be aware
I am a plc programmer. I usually dont code in python. if you find bugs, flaws, mistakes or if you have an idea how to improve or make a new feature, do not hesitate to contact me!
libanoncai 1.2.1
Libanoncai
Requires Python: >=3.10

# Libanoncai
Libanoncai is an extension to PyCharacterAI, that adds a way for you to anonymously use their tRPC api.

> [!CAUTION]
> We are NOT responsible for any IP blocks caused by Libanoncai.

Libanoncai is NOT a monkeypatch, but has a very simple API anyways.

V1.2.0 fixes multiget with many characters at once by limiting multiget internally to 10 ids per request. This makes more requests internally but lets you request a lot more at once.

V1.2.1 fixed broken multiget and now v1.2.0 is yanked.

## Usage:

Example Code
```py
import libanoncai
client = libanoncai.Client()
bot = client.get_anonymous_search("Blank")[0] # Why not?
print(bot.name)
```
jupyter-slurm-http-provisioner 0.3.0
Start a remote Kernel via http in a slurm job, and connect to it with ssh tunnels

# Slurm HTTP Provisioner

## What is this package

This package is a Jupyter Kernel Provisioner.

It uses an http(s) api to launch a remote kernel in a slurm job and ssh tunnels to connect to it.

Once configured, on the Jupyter Notebook / Lab interface, you can just select a kernel using this provisioner and it will set up everything accordingly.

## Setup

### 1 - Build a remote wrapper script

This remote wrapper will be called when we want to launch the kernel.
Thus, it should:

- Start a slurm job that will start a kernel (the connection file should be named .../kernel-{slurm_job_id}.json, thus the command inside a batch script could be `python -m ipykernel_launcher -f=/tmp/kernel-${SLURM_JOB_ID}.json`)
- Wait for the job to start
- Return the slurm job id

See the example file

### 2 - Setup a kernel

This is the kernel that will use the slurm-http-provisioner. It will be displayed in the Notebook / Lab interface.

```
#~/.local/share/jupyter/kernels/slurm-http/kernel.json
{
  "display_name": "Python 3 (Slurm HTTP)",
  "language": "python",
  "metadata": {
    "kernel_provisioner": {
      "provisioner_name": "slurm-http-provisioner",
      "config": {
        "url": "http://example.com",
        "api_key": "1234",
        "secret": "123456789",
        "username": "debian",
        "hostname": "xxx.xxx.xxx.xxx"
      }
    }
  }
}

```

See the example file

### 3 - Voilà

You should now be able to use this new kernel.

Use `jupyter kernelspec list` to check your kernel is detected.
Use `jupyter kernelspec provisioners` to check that slurm-http-provisioner is installed.