semvec/pypi-packages huggingface dataset:
vissuAlize 0.1.3
High-level visualization library built on top of matplotlib and seaborn

# vissuAlize

`vissuAlize`, Python iÃ§in yÃ¼ksek seviyeli bir veri gÃ¶rselleÅŸtirme kÃ¼tÃ¼phanesidir. Matplotlib ve Seaborn'un gÃ¼Ã§lÃ¼ Ã¶zelliklerini bir araya getirerek, kullanÄ±cÄ± dostu bir arayÃ¼z sunar. Veri bilimi projelerinizde hÄ±zlÄ± ve etkili gÃ¶rselleÅŸtirmeler oluÅŸturmanÄ±za olanak tanÄ±r.

## Ã–zellikler

- Ã‡eÅŸitli gÃ¶rselleÅŸtirme tÃ¼rleri: bar plotlar, scatter plotlar, line plotlar ve daha fazlasÄ±.
- Seaborn ve Matplotlib tabanlÄ± estetik ve interaktif grafikler.
- Kolay kullanÄ±m ve esnek API.
- Ã‡oklu veri seti desteÄŸi ve kategorik veri analizi iÃ§in Ã¶zel gÃ¶rselleÅŸtirmeler.

## Kurulum

`vissuAlize`'Ä± pip kullanarak kolayca kurabilirsiniz:

```bash
pip install vissuAlize
```

## KullanÄ±m Ã–rnekleri

`vissuAlize` ile gÃ¶rselleÅŸtirme yapmak basit ve doÄŸrudandÄ±r. Ä°ÅŸte bazÄ± temel kullanÄ±m Ã¶rnekleri:

Bar Plot OluÅŸturma:

```python
import vissuAlize as viss
import pandas as pd

# Veri setini yÃ¼kle
data = pd.read_csv('your_dataset.csv')

# Bar plot oluÅŸtur
viss.bar(x='category', y='value', data=data, title="Ã–rnek Bar Plot")
```

Scatter Plot OluÅŸturma:

```python
# Scatter plot oluÅŸtur
viss.scatter(x='x_column', y='y_column', data=data, title="Ã–rnek Scatter Plot")
```

Bu basit Ã¶rnekler, `vissuAlize`'Ä±n nasÄ±l kullanÄ±lacaÄŸÄ±na dair bir fikir vermektedir. Daha fazla bilgi ve Ã¶rnek iÃ§in dokÃ¼mantasyonumuzu ziyaret edin.

## KatkÄ±da Bulunma

`vissuAlize`'a katkÄ±da bulunmak istiyorsanÄ±z, lÃ¼tfen KatkÄ±da Bulunma Rehberi'ni okuyun. Her tÃ¼rlÃ¼ katkÄ±, bÃ¼yÃ¼k ya da kÃ¼Ã§Ã¼k, takdirle karÅŸÄ±lanÄ±r!

## Lisans

`vissuAlize`, MIT LisansÄ± altÄ±nda lisanslanmÄ±ÅŸtÄ±r. Detaylar iÃ§in LICENSE dosyasÄ±na bakÄ±n.
#\x00 \x00v\x00i\x00s\x00s\x00u\x00A\x00l\x00i\x00z\x00e\x00
\x00
\x00
leaf-eval-tools 0.1.1
A project that provides eval tools for leaf-playground based projects
Requires Python: >=3.9,<4.0

# <center> leaf-eval-tools </center>


## Introduction

A project that provides eval tools that can be used by [leaf-playground](https://github.com/LLM-Evaluation-s-Always-Fatiguing/leaf-playground) based projects.

## Tools Introduction
- `leaf_eval_tools.oai_eval_worker.OaiEvalWorker`: using openai client to evaluate
- `leaf_eval_tools.ragas_eval_worker.RagasEvalWorker`: a wrapper to `ragas.evaluate`
- `leaf_eval_tools.regex_answer_extractor.RegexAnswerExtractor`: a helper to extract answers that match the specified patterns from text

## Installation

`pip install leaf-eval-toos`

## Usage
TBD;

## Maintainers

[@PanQiWei](https://github.com/panqiwei); [@Pandazki](https://github.com/pandazki).

## Roadmap
TBD;
pydantic_br_validator 0.1.0
Uma biblioteca python com modelos de validação para os principais documentos brasileiros
Requires Python: >=3.11,<4.0

# Pydantic BR Validator

Uma biblioteca python com modelos de validação para os principais documentos brasileiros.

# Instalação

```bash
pip install pydantic-br-validator
```

# Campos disponíveis

## CPF

```python
from pprint import pprint

from pydantic import BaseModel

from pydantic_br_validator import CPF, CPFDigits, CPFMask


class Cliente(BaseModel):
    nome: str
    cpf: CPF  # aceita CPF válidos com ou sem máscara
    cpf_mask: CPFMask  # aceita CPF válido apenas com máscara
    cpf_digits: CPFDigits  # aceita CPF válido apenas com dígitos


cliente = Cliente(
    nome="Hudson", cpf="04120039021", cpf_mask="041.200.390-21", cpf_digits="04120039021"
)


pprint(cliente.dict())
```

## CNPJ

```python
from pprint import pprint

from pydantic import BaseModel

from pydantic_br_validator import CNPJ, CNPJDigits, CNPJMask


class Cliente(BaseModel):
    nome: str
    cnpj: CNPJ  # aceita CNPJ válidos com ou sem máscara
    cnpj_mask: CNPJMask  # aceita CNPJ válido apenas com máscara
    cnpj_digits: CNPJDigits  # aceita CNPJ válido apnas com dígitos


cliente = Cliente(
    nome="Hudson", cnpj="47895328000187", cnpj_mask="47.895.328/0001-87", cnpj_digits="47895328000187"
)


pprint(cliente.dict())
```

# Contribua 🚀
context-aware-chunker 0.0.2
Context aware chunking using perplexity
Keywords: python,rag

# Context Aware Chunker
When performing semantic search using vector similarity, one of the key issues that arises is the size of the chunk you are using.

The size of the chunk affects a lot of things, including the accuracy of your result, the amount of contextual information retained at inference time, and accuracy of retrieval.

One of the easiest ways to boost accuracy is to retain highly correlated information in a single atomic chunk as opposed to creating multiple, since this might be missed when performing semantic search. 

## How does this package work?
The idea is quite simple. Language models are extremely good at knowing when two pieces of text belong together.

When they do, the perplexity remains low, but when they aren't, the perplexity is much higher. 

Based on this, we can merge two groups of text together, creating the perfect chunk of highly correlated information

## Usage

> WARNING: Please note that this is an alpha release and is only suitable for testing, not for production

### Installation
```
pip install context_aware_chunking
```
### Python Code
```python
text = "<INSERT TEXT HERE>"

from context_aware_chunker.chunking_models import T5ChunkerModel
from context_aware_chunker.text_splitter import SentenceSplitter

#This module will help you in finding relevant sentences from unstructured text
splitter = SentenceSplitter()

'''
Responsible for determining which sentence segments to merge or separate
If you have more GPU power you can try using larger models
'''
chunking_agent = T5ChunkerModel('t5-small')

'''
Here, merge_sentences decides how many sentences will be in one split of the sentences
Default is 1, you can increase and see
'''
split_content = splitter.split_text(text, merge_sentences = 1)

chunks = chunking_agent.chunk(split_content)

for chunk in chunks:
  print(chunk)
```
voluptuous-errors 0.0
report voluptuous errors
Requires Python: >=3.6

Voluptuous Errors — Error Reporting for Voluptuous with NestedText and Inform
=============================================================================

.. ignore for now:

    .. image:: https://pepy.tech/badge/voluptuous_errors/month
        :target: https://pepy.tech/project/voluptuous_errors

    ..  image:: https://github.com/KenKundert/voluptuous_errors/actions/workflows/build.yaml/badge.svg
        :target: https://github.com/KenKundert/voluptuous_errors/actions/workflows/build.yaml

    .. image:: https://coveralls.io/repos/github/KenKundert/voluptuous_errors/badge.svg?branch=master
        :target: https://coveralls.io/github/KenKundert/voluptuous_errors?branch=master

    .. image:: https://img.shields.io/pypi/v/voluptuous_errors.svg
        :target: https://pypi.python.org/pypi/voluptuous_errors

    .. image:: https://img.shields.io/pypi/pyversions/voluptuous_errors.svg
        :target: https://pypi.python.org/pypi/voluptuous_errors/

:Author: Ken Kundert
:Version: 0.0.0
:Released: 2024-02-25


A convenience function used for reporting voluptuous_ errors from nestedtext_ 
with inform_.  Here is a typical use of this function::

    >>> from voluptuous import Schema, Invalid, MultipleInvalid, Required
    >>> from voluptuous_errors import report_voluptuous_errors
    >>> from inform import error, os_error, terminate
    >>> import nestedtext as nt

    >>>  try:
    ...      settings_path = Path('settings.nt')
    ...      settings = nt.load(
    ...          settings_path,
    ...          keymap = (keymap:={}),
    ...      )
    ...      settings = schema(settings)
    >>>  except nt.NestedTextError as e:
    ...      e.report()
    >>>  except MultipleInvalid as e:
    ...      report_voluptuous_errors(e, keymap, settings_path)
    >>>  except OSError as e:
    ...      error(os_error(e))
    >>>  terminate()

This code will report all errors found by Voluptuous when reading the settings 
file.


Releases
--------

**Latest development release**:
    | Version: 0.5.0
    | Released: 2022-09-02

**0.0 (2024-02-25)**:
    Initial version.

.. _voluptuous: https://github.com/alecthomas/voluptuous
.. _nestedtext: https://nestedtext.org
.. _inform: https://readthedocs.io/inform
ke-apollo-client 2.1.2.3
a client for apollo 2.0, it's been reformatted!

ke-apollo-client forke自apollo-client，变更内容包括：
* 修改部分接口url，适配贝壳apollo server
* 修改api返回结果解析，适配贝壳apollo server

apollo-client - Python Client for Ctrip's Apollo
================

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

方便Python接入配置中心框架 [Apollo](https://github.com/ctripcorp/apollo) 所开发的Python版本客户端。
Tested with python 3.5+

基于https://github.com/filamoon/pyapollo/ 修改

Installation
------------

``` shell
pip install apollo-client
```

# Features
* 实时同步配置
* 灰度配置
* 客户端容灾

# Usage

- 启动客户端长连接监听

``` python
client = ApolloClient(app_id=<appId>, cluster=<clusterName>, config_server_url=<configServerUrl>)
client.start()
```

- 获取Apollo的配置
  ```
  client.get_value(Key, DefaultValue, namespace)
  ```

# Contribution
  * Source Code: https://github.com/BruceWW/pyapollo
  * Issue Tracker: https://github.com/BruceWW/pyapollo/issues
  * Original Source Code: https://github.com/filamoon/pyapollo
  
# License
The project is licensed under the [Apache 2 license](https://github.com/zouyx/agollo/blob/master/LICENSE).

# Reference
Apollo : https://github.com/ctripcorp/apollo

# Contributor
[Bruce](https://github.com/BruceWW)<br/>
[prchen](https://github.com/prchen) <br/>
[xhrg](https://github.com/faicm)<br/>


# Version log
11/24/2019  Bruce  0.8.2   优化本地缓存的存储方式<br/>
1/4/2020    Bruce  0.8.4   修复文件读取异常的bug<br/>
3/24/2020   [prchen](https://github.com/prchen) 0.8.5   修复安装过程中requests模块依赖的问题<br/>
7/5/2020    Bruce  0.9     主线程退出时，关闭获取配置的子线程<br/>
25/5/2020   [xhrg](https://github.com/faicm)    0.9.1   修复文件名称读取异常<br/>
13/7/2020   Bruce  0.9.2    【bugfix】[修复当namespace不存在时，服务器挂起导致get_value无响应](https://github.com/BruceWW/pyapollo/issues/7)<br/>
18/10/2020   Bruce  2.0     重构 | 优化数据获取方式 ｜ 优化定时任务  |  新增authorization传入   <br/>
report-vp 0.0.6
A report generation package for results of Monaco 2018 Racing
Requires Python: >=3.11.4

# Formula 1 Monaco 2018 Racing Report Generator

This Python package provides a tool for generating reports based on the start and end data of the best lap for each racer of Formula 1 - Monaco 2018 Racing during the first stage of the qualification (Q1).

## Installation

To use the package, you need to install it first. You can do this using `pip`:

```UPDATE LATER
```

## Usage

The package provides a command-line interface with several options.

### Generating a Report

You can generate a report for the first 20 minutes of the Monaco 2018 Racing by providing the path to the folder containing the data files. The `--asc` or `--desc` flag allows you to specify the sorting order (default is ascending).

```bash (update later)
report.py --files <folder_path> [--asc | --desc]
```

### Driver Statistics

To get statistics about a specific driver, use the `--driver` option along with the folder path.

```bash
report.py --files <folder_path> --driver "Sebastian Vettel"
```

## Package Structure

The package consists of the following components:

- **`Record` Class:** Represents a racer record with start and end times.
- **`FileNotFound` Exception:** Raised when a specified file is not found.
- **`ReportInvalidOrder` Exception:** Raised for an invalid sorting order in the report.
- **`build_report` Function:** Compiles a report summarizing valid and invalid records.
- **`print_report` Function:** Prints a formatted report based on ordered results.
- **`cli` Function:** Parses command-line arguments and orchestrates the report generation process.

## Example

```python
import report.py
#(update later)
# Command-line interface
args = formula1_report.Record.cli()

# Build the report
good_records, bad_records = formula1_report.Record.build_report(args.files, args.sort)

# Filter records if a specific driver is provided
if args.driver:
    good_records = [rec for rec in good_records if rec.driver == args.driver]
    bad_records = [rec for rec in bad_records if rec.driver == args.driver]

# Print the report
print(report.Record.print_report(good_records, bad_records))
```

## License

This package is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.