semvec/pypi-packages huggingface dataset:
crepex-pyutil 0.18.0
CrepeX python utilities
Requires Python: <4.0,>=3.9

# CrepeX python util

## 개발환경
- `poetry` 를 사용해서 개발
- 가상환경은 `poetry shell`
- 패키지 설치는 `poetry install`
- 최조 pre-commit 셋팅 `pre-commit install`

## 배포

### 테스트
- TestPypi 계정준비, API 토큰 준비

```shell
# test pypi repository 등록
poetry config repositories.testpypi https://test.pypi.org/legacy/
# poetry 에 토큰 등록
poetry config http-basic.testpypi __token__ pypi-your-api-token-here

# Build & Publish
poetry build
poetry publish -r testpypi

# Get package
pip install -i https://test.pypi.org/.../your-package-name
```

### 프로덕션
- Pypi 계정준비, API 토큰 준비
```shell
# poetry 에 토큰 등록
poetry config pypi-token.pypi pypi-your-token-here

# Build & Publish
poetry publish --build
```
digitalcircuit 0.1.0
A package for converting Boolean expressions to circuit diagrams
Requires Python: >=3.6

# DigiCircuit

DigiCircuit is a Python library to convert Boolean expressions to circuit diagrams.

## Installation

```bash
pip install digicircuit
```

## Usage

1. Create an input file with Boolean expressions (e.g., `data.txt`):
```
1 f(a,b,c,d) = a'b'cd' + abc'd + ab'c'd
2 f(a,b,c,d) = acd
2 f(a,b,c,d) = (a+b+c+d)(a'+b'+d')
```

2. Run the command to create circuit diagrams:
```bash
digicircuit data.txt
```

The circuit diagrams will be created in the `circuits` directory in the form of LaTeX files.

### Options

- `--output_dir` or `-o`: Specify the output directory (default: `circuits`)
- `--scale` or `-s`: The scale of the circuit diagrams (default: 1.0)

Example:
```bash
digicircuit data.txt --output_dir my_circuits --scale 1.5
```

## Requirements

- Python >= 3.6
- numpy
- pandas
- openpyxl

## License

MIT License
ncep-gfs-req 0.1.0
Download and process NCEP GFS 0.25° data via ASCII interface
Requires Python: >=3.6

# ncep-data-req

Download and preprocess NCEP GFS 0.25Â° atmospheric forecast data from NOAA using ASCII interface.

## Overview

This package provides utility functions to directly download and preprocess Global Forecast System (GFS) 0.25-degree resolution data from the NOAA NOMADS server. It supports extraction of pressure-level and surface-level variables for specific forecast hours, structured into usable `xarray.Dataset` objects.

## Features

- Access GFS 0.25Â° forecast data from NOAA
- Support for both pressure-level and surface variables
- Handles multiple or single forecast hours
- Outputs structured `xarray.Dataset` objects for easy analysis

## Installation

You can install the package from PyPI (after publishing):

```bash
pip install ncep-gfs-req

from ncep_data_req import get_data_preprocess, get_data_preprocess_s

# Example: Download temperature on pressure levels for 6 forecast hours
ds = get_data_preprocess(
    yy=2025, mm=5, dd=23, utc=0, ft=6, var='tmpprs', pvar='yes'
)

# Example: Download surface variable at one forecast hour
ds_surface = get_data_preprocess_s(
    yy=2025, mm=5, dd=23, utc=0, ft=3, var='tmp2m', pvar='no'
)


Parameters
yy, mm, dd: Year, month, day

utc: Initialization hour (0, 6, 12, or 18 UTC)

ft: Forecast hour (0 to 384 depending on GFS run)

var: GFS variable name (e.g., tmpprs, rhprs, ugrdprs, etc.)

pvar: 'yes' if pressure-level variable, 'no' for surface

Output
Returns an xarray.Dataset containing:

Dimensions: time/levels, lat, lon

Coordinates: pressure levels, lat/lon grid

Data variables: selected GFS variable


contact:subhrjitrath17@gmail.com
gfs-req 0.1.0
Download and process NCEP GFS 0.25° data via ASCII interface
Requires Python: >=3.6

# ncep-data-req

Download and preprocess NCEP GFS 0.25Â° atmospheric forecast data from NOAA using ASCII interface.

## Overview

This package provides utility functions to directly download and preprocess Global Forecast System (GFS) 0.25-degree resolution data from the NOAA NOMADS server. It supports extraction of pressure-level and surface-level variables for specific forecast hours, structured into usable `xarray.Dataset` objects.

## Features

- Access GFS 0.25Â° forecast data from NOAA
- Support for both pressure-level and surface variables
- Handles multiple or single forecast hours
- Outputs structured `xarray.Dataset` objects for easy analysis

## Installation

You can install the package from PyPI (after publishing):

```bash
pip install gfs-req

from ncep_data_req import get_data_preprocess, get_data_preprocess_s

# Example: Download temperature on pressure levels for 6 forecast hours
ds = get_data_preprocess(
    yy=2025, mm=5, dd=23, utc=0, ft=6, var='tmpprs', pvar='yes'
)

# Example: Download surface variable at one forecast hour
ds_surface = get_data_preprocess_s(
    yy=2025, mm=5, dd=23, utc=0, ft=3, var='tmp2m', pvar='no'
)


Parameters
yy, mm, dd: Year, month, day

utc: Initialization hour (0, 6, 12, or 18 UTC)

ft: Forecast hour (0 to 384 depending on GFS run)

var: GFS variable name (e.g., tmpprs, rhprs, ugrdprs, etc.)

pvar: 'yes' if pressure-level variable, 'no' for surface

Output
Returns an xarray.Dataset containing:

Dimensions: time/levels, lat, lon

Coordinates: pressure levels, lat/lon grid

Data variables: selected GFS variable 
check this link ( https://nomads.ncep.noaa.gov/dods/gfs_0p25_1hr/)


contact:subhrjitrath17@gmail.com
ossSign 1.8
oss文件上传签名计算
Requires Python: >=3.10

# 计算阿里云OSS文件上传签名

## 用法
> 说明：获取回调
>
> 方法：ossSign.GetCallback
> 
> 入参：
>> callback_url：回调地址
>> 
>> oss_callback_body：回调内容
> 
> 说明：计算签名
> 
> 方法：ossSign.GetSignV1
>
> 入参：
>> security_token：security_token
>> 
>> secret_access_key：secret_access_key
>> 
>> access_key_id：access_key_id
>> 
>> bucket_name：桶名称 
>>
>> object_path：文件对象路径
>>
>> object_name：文件对象名
>>
>> date：GMT时间，默认使用当前时间
>> 
>> callback：回调信息，默认不回调

## 打包
```shell
python setup.py sdist bdist_wheel
```

## 上传
### 安装依赖工具
```shell
pip install twine
```
### 上传到pypi（需要提前准备好账号）
```shell
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
```
nextrngbook 0.1.0.post1
A Python Random Number Generation Package for RNG Book
Requires Python: >=3.9
Keywords: pseudo-random number generation, pseudo-random number generator, PRNG, RNG, randomness, DX generator, Monte Carlo simulation, statistical simulation, statistical sampling

# NextRNGBook: A Python Random Number Generation Package for RNG Book

## Introduction

The goal of **NextRNGBook** package is to incorporate a variety of high-quality random number 
generators (RNGs) from 
*Random Number Generators for Computer Simulation and Cyber Security* [[1]](#references). 
Designed for seamless compatibility with **NumPy**, 
this Python package can integrate easily into existing workflows, 
offering a wide range of selections from state-of-the-art random number generation techniques 
suitable for scientific computing, large-scale simulations, and cryptographic applications.

The goal of designing high-quality random number generators is to produce variates 
that behave like truly random numbers. 
This means the generated variates can cover the space evenly over high dimensions, 
and do not repeat for a very long time. 
They can be generated efficiently across different systems, 
and they can pass a wide range of statistical tests that detect hidden patterns. 
A good RNG should perform reliably for large-scale simulations with 
a strong support for parallel computing, 
and an easy integration across various computing platforms. 
For security applications,  generated variates need to be unpredictable, 
so that future values cannot be inferred from past outputs.

There are several  high-quality RNGs to be implemented in this NextRNGBook Package which should provide a solid foundation 
for better statistical simulation and/or secure applications. 
Combining strong theoretical supports and great practical performance, 
NextRNGBook can help users to explore, evaluate, and 
apply high-quality RNGs in a modern Python environment.


## Documentation & Distribution

For full details, please refer to the links.
- **Documentation**: [NextRNGBook Documentation](https://nextrngbook.github.io/nextrngbook/)
- **PyPI**: [NextRNGBook on PyPI](https://pypi.org/project/nextrngbook/)


## References

[1] Deng, L.-Y., Kumar, N., Lu, H. H.-S., & Yang, C.-C. (2025). 
*Random Number Generators for Computer Simulation and Cyber Security:
 Design, Search, Theory, and Application* (1st ed.). Springer. 
 [https://doi.org/10.1007/978-3-031-76722-7](https://doi.org/10.1007/978-3-031-76722-7)
evilwormgpt 0.2
Unofficial wrapper for a sarcastic and evil GPT-style API chatbot
Requires Python: >=3.6


# EvilWormGPT

**EvilWormGPT** is a lightweight Python wrapper for a twisted, sarcastic AI chatbot API that behaves like a villain.

## Features

- Keeps a conversation history
- Sends requests to a publicly exposed evil AI endpoint
- Returns hostile, funny, or unexpected replies

## Installation

```bash
pip install evilwormgpt
```

## Usage

```python
from evilworm import EvilWorm

bot = EvilWorm()
reply = bot.ask("Who created you?")
print("AI:", reply)
```

## Reset Chat

```python
bot.reset()
```

## Warning

This library interacts with an experimental and intentionally rude chatbot. Use responsibly and for fun purposes only.
geomdl 5.4.0
Object-oriented B-Spline and NURBS evaluation library
Requires Python: >=3.10
Keywords: NURBS, B-Spline, curve, surface, volume, CAD, modeling, geometry

NURBS-Python (geomdl)
^^^^^^^^^^^^^^^^^^^^^

|GHACTIONS|_ |RTD|_ |PYPI|_ |PYPIDL|_

Introduction
============

NURBS-Python (geomdl) is a pure Python, self-contained, object-oriented B-Spline and NURBS spline library.

Features
========

NURBS-Python (geomdl) provides convenient data structures and highly customizable API for rational and non-rational
splines along with the efficient and extensible implementations of the following algorithms:

* Spline evaluation
* Derivative evaluation
* Knot insertion
* Knot removal
* Knot vector refinement
* Degree elevation
* Degree reduction
* Curve and surface fitting via interpolation and least squares approximation

NURBS-Python (geomdl) also provides customizable visualization and animation options via Matplotlib, Plotly and VTK
libraries. Please refer to the `documentation <http://nurbs-python.readthedocs.io/>`_ for more details.

Further reading
===============

* Documentation: https://nurbs-python.readthedocs.io/
* Examples: https://github.com/orbingol/NURBS-Python_Examples
* Development: https://github.com/orbingol/NURBS-Python

Additional file format support
==============================

* `rw3dm <https://github.com/orbingol/rw3dm>`_
* `rwsat <https://github.com/orbingol/rwsat>`_

Citing
======

The `following article <https://doi.org/10.1016/j.softx.2018.12.005>`_ outlines the design and features of NURBS-Python
(geomdl)::

    @article{bingol2019geomdl,
      title={{NURBS-Python}: An open-source object-oriented {NURBS} modeling framework in {Python}},
      author={Bingol, Onur Rauf and Krishnamurthy, Adarsh},
      journal={{SoftwareX}},
      volume={9},
      pages={85--94},
      year={2019},
      publisher={Elsevier},
      doi={https://doi.org/10.1016/j.softx.2018.12.005}
    }

License
=======

NURBS-Python (geomdl) is licensed under the terms of the `MIT License <LICENSE>`_.
It also contains the following modules:

* ``six`` is licensed under the terms of the MIT License.
* ``backports.functools_lru_cache`` is licensed under the terms of the MIT License.

.. |RTD| image:: https://readthedocs.org/projects/nurbs-python/badge/?version=5.x
.. _RTD: https://nurbs-python.readthedocs.io/en/5.x/?badge=5.x

.. |PYPI| image:: https://img.shields.io/pypi/v/geomdl.svg
.. _PYPI: https://pypi.org/project/geomdl/

.. |PYPIDL| image:: https://img.shields.io/pypi/dm/geomdl.svg
.. _PYPIDL: https://pypi.org/project/geomdl/

.. |GHACTIONS| image:: https://img.shields.io/github/actions/workflow/status/orbingol/NURBS-Python/build.yml
.. _GHACTIONS: https://github.com/orbingol/NURBS-Python/actions/workflows/build.yml
querymind 0.1.1
Ask natural language questions about your CSV data using AI
Requires Python: >=3.8
Keywords: pandas, ai, llm, openai, anthropic, data-analysis, natural-language

# QueryMind

Ask natural language questions about your CSV data using AI. This package intelligently translates your questions into pandas code using Large Language Models (LLMs) and provides clear, concise answers.

## Features

- 🤖 Natural language to pandas code translation
- 🔒 Safe code execution in a controlled environment
- 📊 Optional result summarization in natural language
- 🔌 Support for multiple LLM providers (OpenAI and Anthropic)
- 🛠️ Clean, maintainable, and extensible design

## Installation

```bash
pip install querymind
```

## Quick Start

```python
from querymind import AIQuery

# Initialize with your CSV file and preferred LLM provider
ai = AIQuery(
    csv_path="your_data.csv",
    llm_provider="openai",  # or "anthropic"
    api_key="your-api-key"
)

# Ask questions in natural language
result = ai.ask("What is the average age of customers who made purchases over $100?")
print(result)

# Get raw results without summarization
raw_result = ai.ask("Show me the top 5 products by sales", summarize=False)
print(raw_result)
```

## Supported LLM Providers

### OpenAI

- Uses GPT-4 for code generation and summarization
- Requires an OpenAI API key

### Anthropic

- Uses Claude 3 Opus for code generation and summarization
- Requires an Anthropic API key

## Example Questions

Here are some example questions you can ask:

- "What is the average salary by department?"
- "Show me the top 10 customers by total purchase amount"
- "How many products are out of stock?"
- "What is the correlation between age and purchase amount?"
- "List all transactions from last month"

## Security

The package executes generated code in a controlled environment with limited access to:

- The pandas DataFrame (`df`)
- The pandas library (`pd`)
- No access to other Python built-ins or system resources

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.