semvec/pypi-packages huggingface dataset:
inuktitut 0.1.0
Fast converter between Inuktitut Unicode syllabics and Latin script
Requires Python: >=3.6

# `inuktitut`

`inuktitut` is a fast and accurate converter between Inuktitut romanized transcription and Unified Canadian Aboriginal Syllabics (UCAS).  
It is implemented in Cython for performance and provides easy-to-use functions for both directions of conversion.

---

## 🧩 Features

- Convert romanized Inuktitut to syllabics (UCAS Unicode)
- Convert syllabics to romanized Inuktitut
- Cython-accelerated for efficient bulk conversion
- Based on ICI standard Qaniujaaqpait mapping
- Handles edge cases such as `qqi`, `nngii`, and `lh`/`&`

---

## 🔧 Installation

```bash
pip install inuktitut-converter
```
If installing from source:

```bash
git clone https://github.com/ColemanHaley/inuktitut-converter.git
cd inuktitut-converter
pip install .
```

Or with Cython compilation:

```bash
pip install cython
python setup.py build_ext --inplace
```
## 🧪 Usage

```python
from inuktitut_converter import roman_to_syllabic, syllabic_to_roman

text = "Qaniujaaqpait"
syllabics = roman_to_syllabic(text)
print("Syllabics:", syllabics)  # ᖃᓂᐅᔮᖅᐸᐃᑦ 

back = syllabic_to_roman(syllabics)
print("Back to roman:", back)  # qaniujaaqpait
```

## 📦 API
```python
roman_to_syllabic(text: str) -> str
```
Converts romanized Inuktitut text into Unicode syllabics.

```python
syllabic_to_roman(text: str) -> str
```
Converts syllabic Unicode text into romanized Inuktitut.

## 🗂️ Structure
`inuktitut_converter.pyx`: Core Cython module with conversion logic.

`setup.py`: Cython build script.

`__init__.py`: Python interface for the compiled module.

tests/: Unit tests.

## 📝 License
MIT License

## 🧑‍💻 Author
Maintained by Coleman Haley.
Contributions welcome via GitHub pull requests!
usingai311 0.2.0
A collection of AI-powered tools for games and apps.
Requires Python: >=3.8

# usingai: AI 기반 게임/앱 모듈 컬렉션

이 패키지는 **Google Gemini API**와 **OpenAI DALL·E 3 API**를 활용하여,  
게임 및 애플리케이션 개발에 사용할 수 있는 다양한 AI 기능들을 제공합니다.  
현재 **이미지 스타일 변환** 및 **AI 챗봇** 기능을 포함하고 있습니다.

---

## 🚀 주요 기능

- **🖼️ 이미지 스타일 변환**  
  텍스트 프롬프트 기반으로 예술 스타일(유화, 픽셀아트, 수묵화 등)을 적용하여 이미지를 생성합니다.

- **🤖 AI 챗봇**  
  텍스트 또는 이미지 기반 질문에 답하는 멀티모달 챗봇 기능 (Gemini API 사용)

---

## 📦 설치 방법

```bash
pip install usingai
```

---

## ⚙️ 사용법

### 1. API 키 자동 설정

최초 실행 시 `.env` 파일이 없으면 자동으로 생성됩니다.  
명령어 실행 후 안내에 따라 **OpenAI** 및 **Google Gemini** API 키를 입력하세요.


🔑 아래 링크에서 각 API 키를 발급받을 수 있습니다:

- [OpenAI API 키 발급하기](https://platform.openai.com/account/api-keys)
- [Google Gemini API 키 발급하기](https://ai.google.dev/gemini-api/docs/get-started)

```bash
python -m ai_tools.main
```

또는 `main.py`를 바로 실행해도 자동으로 설정 스크립트가 호출됩니다.

---

### 2. 실행 예시

실행 후 아래와 같은 메뉴가 제공됩니다:

```
💾 출력 이미지를 저장할 폴더를 입력하세요 (Enter 시 입력 이미지와 같은 위치):

```

이후 경로는 수정할 수 없으니 주의하세요!

```
1. 이미지 스타일 변환
2. AI 챗봇
3. 종료
```

---

## 🧪 테스트

테스트용 이미지 생성 및 모듈 테스트를 진행할 수 있습니다.

```bash
python tests/test_ai_tools.py
```

---

## 🛠️ 개발 체크리스트 보기

👉 [✅ checklist.md 보기](docs/checklist.md)

---

## 🤝 기여 (Contributing)

이 프로젝트는 오픈 소스로 개발되고 있으며 누구나 환영합니다.  
자세한 기여 방법은 [`CONTRIBUTING.md`](CONTRIBUTING.md)를 확인하세요.

---

## 📄 라이선스

이 프로젝트는 [MIT License](LICENSE)에 따라 배포됩니다.

---

## ⚠️ 사용 시 주의사항

- 본 프로젝트는 OpenAI 및 Google Gemini API를 사용하며, 각 API 제공자의 **사용 정책을 반드시 준수**해야 합니다.
- 생성된 콘텐츠에 대해 개발자는 책임지지 않으며, 사용자의 책임 하에 사용해야 합니다.

📄 자세한 정책은 [OpenAI 정책](https://openai.com/policies/usage-policies), [Gemini 정책](https://ai.google.dev/terms)을 참고하세요.
argcast 0.3.0
Automatically cast function parameters at run time based on their types.
Requires Python: <4.0,>=3.10

# argcast - Run time function argument caster

Automatically cast function parameters at run time based on their types.


## Installation

```bash
pip install argcast
```

## Usage

Below is an example usage:

```python
from decimal import Decimal
from enum import Enum

import numpy as np
import pandas as pd

from argcast import coerce_params

class MatrixOp(Enum):
    INVERSE = 1
    TRANSPOSE = 2

    @classmethod
    def get(cls, name):
        return cls[name] if isinstance(name, str) else cls(name)

coerce = coerce_params({np.ndarray: np.array, MatrixOp: MatrixOp.get})

@coerce
def f(
    a: np.ndarray, b: np.ndarray, k: np.float64, b_trans: MatrixOp
) -> pd.DataFrame:

    if b_trans == MatrixOp.INVERSE:
        b = np.linalg.inv(b)
    elif b_trans == MatrixOp.TRANSPOSE:
        b = b.T
    return k * a @ b

f([[1, 2], [3, 4]], [[5, 6], [7, 8]], Decimal("2.0"), "TRANSPOSE")
# returns pd.DataFrame([[34.0, 46.0], [78.0, 106.0]])
```

## Limitations

1) Using this decorator will confuse type checkers like mypy and you
might wish to place a top level comment in your module like:

```python
# mypy: disable-error-code="return-value"
```

2) Although basic sequence and mapping types are supported, ie list|tuple|set[str|int|...]
or dict[..., ...], many other types aren't (eg nested types list[list[...]] etc).

## Development

Before commit run following format commands in project folder:

```bash
poetry run black .
poetry run isort . --profile black
poetry run docformatter . --recursive --in-place --black
```
SylphietteIA 0.1.1
Assistente IA estilo Sylphiette com fala, VTube Studio e Gemini.
Requires Python: >=3.7

# Sylphiette Assistant (sylph_ai)

Assistente virtual personalizavel. Inclui:

- IA com Gemini
- Voz com pyttsx3
- Integração com VTube Studio (Bulding)
- Interface terminal personalizada

## Instalação

```bash
pip install SylphietteIA

```

Uso


---


```python
from IA import base


personalidade = (
    "Coloque a personalidade da sua IA"
)
sylph = base.AI("Nome da sua Ia", personalidade, "GOOGLE API KEY")
sylph.run()

```
usingai39 0.1.0
A collection of AI-powered tools for games and apps.
Requires Python: ==3.9.*

# usingai: AI 기반 게임/앱 모듈 컬렉션

이 패키지는 **Google Gemini API**와 **OpenAI DALL·E 3 API**를 활용하여,  
게임 및 애플리케이션 개발에 사용할 수 있는 다양한 AI 기능들을 제공합니다.  
현재 **이미지 스타일 변환** 및 **AI 챗봇** 기능을 포함하고 있습니다.

---

## 🚀 주요 기능

- **🖼️ 이미지 스타일 변환**  
  텍스트 프롬프트 기반으로 예술 스타일(유화, 픽셀아트, 수묵화 등)을 적용하여 이미지를 생성합니다.

- **🤖 AI 챗봇**  
  텍스트 또는 이미지 기반 질문에 답하는 멀티모달 챗봇 기능 (Gemini API 사용)

---

## 📦 설치 방법

```bash
pip install usingai
```

---

## ⚙️ 사용법

### 1. API 키 자동 설정

최초 실행 시 `.env` 파일이 없으면 자동으로 생성됩니다.  
명령어 실행 후 안내에 따라 **OpenAI** 및 **Google Gemini** API 키를 입력하세요.


🔑 아래 링크에서 각 API 키를 발급받을 수 있습니다:

- [OpenAI API 키 발급하기](https://platform.openai.com/account/api-keys)
- [Google Gemini API 키 발급하기](https://ai.google.dev/gemini-api/docs/get-started)

```bash
python -m ai_tools.main
```

또는 `main.py`를 바로 실행해도 자동으로 설정 스크립트가 호출됩니다.

---

### 2. 실행 예시

실행 후 아래와 같은 메뉴가 제공됩니다:

```
💾 출력 이미지를 저장할 폴더를 입력하세요 (Enter 시 입력 이미지와 같은 위치):

```

이후 경로는 수정할 수 없으니 주의하세요!

```
1. 이미지 스타일 변환
2. AI 챗봇
3. 종료
```

---

## 🧪 테스트

테스트용 이미지 생성 및 모듈 테스트를 진행할 수 있습니다.

```bash
python tests/test_ai_tools.py
```

---

## 🛠️ 개발 체크리스트 보기

👉 [✅ checklist.md 보기](docs/checklist.md)

---

## 🤝 기여 (Contributing)

이 프로젝트는 오픈 소스로 개발되고 있으며 누구나 환영합니다.  
자세한 기여 방법은 [`CONTRIBUTING.md`](CONTRIBUTING.md)를 확인하세요.

---

## 📄 라이선스

이 프로젝트는 [MIT License](LICENSE)에 따라 배포됩니다.

---

## ⚠️ 사용 시 주의사항

- 본 프로젝트는 OpenAI 및 Google Gemini API를 사용하며, 각 API 제공자의 **사용 정책을 반드시 준수**해야 합니다.
- 생성된 콘텐츠에 대해 개발자는 책임지지 않으며, 사용자의 책임 하에 사용해야 합니다.

📄 자세한 정책은 [OpenAI 정책](https://openai.com/policies/usage-policies), [Gemini 정책](https://ai.google.dev/terms)을 참고하세요.
hatch-command-version 1.0.1
A version source plugin to the hatchling.build python build backend that obtains a version by running a command.
Keywords: hatch, hatchling, version

[![PyPI - Version](https://img.shields.io/pypi/v/hatch-command-version)](https://pypi.org/project/hatch-command-version)

# hatch-command-version

A version source plugin to the `hatchling.build` python build backend that obtains a version by running a command.

```.toml
[build-system]
requires = [
    "hatchling",
    "hatch-command-version",        # pull in this plugin
]
build-backend = "hatchling.build"

[tool.hatch.version]
source = "command"                   # pick this version source plugin
command = "path/to/custom-generator" # the command to execute
```
supaclaude 0.1.0
Enhanced CLI wrapper for Claude with beautiful JSON formatting
Requires Python: >=3.8
Keywords: ai, anthropic, claude, cli, formatting

# supaclaude

Enhanced CLI wrapper for Claude with beautiful JSON formatting using Rich.

![PyPI version](https://badge.fury.io/py/supaclaude.svg)
![Python Support](https://img.shields.io/pypi/pyversions/supaclaude.svg)
![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)

## Features

- Beautiful JSON formatting with Rich panels
- Drop-in replacement for the `claude` CLI
- Zero configuration required
- Automatic detection of `--output-format stream-json`
- Preserves all original Claude CLI functionality

## Installation

```bash
pip install supaclaude
```

## Usage

Simply use `supaclaude` as a drop-in replacement for the `claude` command:

```bash
# Regular usage - passes through as normal
supaclaude "What is the weather today?"

# JSON stream formatting - automatically formats with Rich
supaclaude -p "Create a Python hello world" --output-format stream-json --verbose

# All claude flags and options work as expected
supaclaude --help
supaclaude --version
```

### JSON Formatting

When using `--output-format stream-json`, supaclaude automatically:
- Parses each JSON object in the stream
- Formats it with syntax highlighting
- Wraps it in a beautiful panel with rounded borders
- Uses cyan borders for valid JSON and yellow for non-JSON output

## Requirements

- Python 3.8+
- `claude` CLI installed and available in PATH
- Rich library (automatically installed)

## Development

```bash
# Clone the repository
git clone https://github.com/yourusername/supaclaude.git
cd supaclaude

# Install with uv
uv pip install -e .

# Run tests
uv run supaclaude --help
```

## Building and Publishing

```bash
# Build the package
uv build

# Upload to PyPI
uv publish
```

## License

MIT License - see [LICENSE](LICENSE) file for details.

## Contributing

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

## Acknowledgments

- Built on top of Anthropic's Claude CLI
- Beautiful formatting powered by [Rich](https://github.com/Textualize/rich)
bibquest 0.1.2
A tool to automatically fetch BibTeX entries from ADS, arXiv, and INSPIRE-HEP based on citation keys found in LaTeX log files or documents.
Requires Python: >=3.9



<p align="center">
    <picture>
      <source width="600px" height="300px" srcset="https://github.com/sahiljhawar/BibQuest/blob/main/assets/bibquest.svg">
      <img alt="BibQuest">
    </picture>
</p>


[![PyPi](https://badge.fury.io/py/bibquest.svg)](https://badge.fury.io/py/bibquest)

Often while writing scientific documents, it's tedious to constantly switch between your text editor and various tabs to copy BibTeX entries. Instead of interrupting your writing flow, simply use citation keys in your document and let $\mathrm{\large B{\scriptsize{IB}}Q{\scriptsize{UEST}}}$ fetch all the bibliography entries automatically once you're done writing.

## Installation
### Method 1: 
```bash
pip install bibquest
```
### Method 2: 

```bash
git clone https://github.com/sahiljhawar/BibQuest.git
cd BibQuest
```
With `pip`
```bash
pip install .
```
or, if using `uv`
```bash
uv venv
. .venv/bin/activate
uv pip install .
```

The above methods will make sure that the `bibquest` command is available in your terminal.

## Usage


### Method 1: From `.bcf` file  (recommended)
Compile your LaTeX document once, then use the generated `.bcf` file:
```bash
bibquest example/document.bcf example/bibliography.bib
```

### Method 2: From LaTeX log file
Compile your LaTeX document once, then use the generated log file:
```bash
bibquest example/document.log example/bibliography.bib
```

### Method 3: From TeX source file
Scan your TeX source file directly:
```bash
bibquest --scan-tex example/document.tex example/bibliography.bib
```

## Terminal Output
<p align="center">
    <picture>
      <source width="600px" height="300px" srcset="https://github.com/sahiljhawar/BibQuest/blob/main/assets/example.jpeg">
      <img alt="BibQuest">
    </picture>
</p>


### Options
- `--delay 2.0` - Set delay between requests (default: 1.0 seconds)
- `--append` - Append to existing bibliography file (default behavior)
- `--verbose` - Show verbose output

## Supported Sources
- **arXiv**: `arXiv:2301.12345` or `2301.12345`
- **INSPIRE-HEP**: `Author:2023abc`
- **NASA ADS**: ADS identifiers (fallback method)


## Note
- In arXiv, it uses the arXiv ID instead of the bib key for the entry. This is because arXiv does not let you search with the bib key.
- Try not to use ADS as they rate limit requests. In general as well it is better not to use ADS and instead use arXiv or INSPIRE-HEP. See the [ads2inspire](https://github.com/duetosymmetry/ads2inspire).
lora-phy 0.3.0
Full implementation of the physical layer of LoRa. (Python translation of jkadbear/LoRaPHY)
Requires Python: >=3.8

# pyLoRaPHY
Full implementation of the physical layer of LoRa. (Python translation of jkadbear/LoRaPHY)

It also contains comments from my understanding of
the code and unique implementation choices optimized for clarity, Pythonic
style, and performance.

Some MATLAB values distingush column and row vectors. They are all treated as
1D arrays here. All indices are C-style.

The original code was (c) 2020-2022 jkadbear licensed under MIT.

As a project developed during Maiyun's participation in the UCSD SRIP program,
The University of California may have claims to this work.

@author: Zhang Maiyun <maz005@ucsd.edu>
Created on Thu May 23 17:28:37 2024

I try to not use `self` in functions that just calculate values and return
them, to allow for easier testing.
Also, while the original implementation passes data with class properties,
this one uses parameters to pass ones that are packet-specific and only
store stable configuration as instance variables.


Implementation checklist:
- [x] Constructor tested
- [x] `init` tested
- [x] `dechirp` tested
- [x] `detect` tested
- [x] `demodulate` tested
- [x] `parse_header` tested
- [x] `dynamic_compensation` tested
- [x] `gray_coding` tested
- [x] `diag_deinterleave` tested
- [x] `hamming_decode` tested
- [x] `sync`
- [x] `decode` tested
- [x] `dewhiten` tested
- [x] `calc_crc` tested

Functions for modulating side stuff:
- [x] `modulate` tested
- [x] `encode` tested
- [x] `gray_decoding` tested
- [x] `calc_sym_num` tested
- [x] `whiten`: (as `dewhiten`)
- [x] `gen_header` tested
- [x] `hamming_encode` tested
- [x] `diag_interleave` tested

These three look like they are not referenced anywhere:
- [ ] `symbols_to_bytes`
- [ ] `calc_payload_len`
- [x] `time_on_air` tested

Other functions:
- [x] `print_bin` (debugging, use logging instead)
- [x] `print_hex` (debugging, use logging instead)
- [x] `log` (debugging, use logging instead)
- [ ] `plot_peak`
- [x] `bit_reduce` (as `xorbits`)
- [x] `word_reduce` merged into `hamming_encode`
- [x] `topn` merged into `dechirp`
- [x] `chirp` tested
- [x] `spec`: use `matplotlib.pyplot.specgram` instead
- [x] `read`: (as `demodulate_file`)
- [x] `write`: use `ndarray.tofile` instead
mcp-fmu 0.1.2
mcp server for fmu simulation
Requires Python: >=3.11



## mcp-fmu

MCP server for fmu.

### Prerequisites

* Install `uv` from https://docs.astral.sh/uv/getting-started/installation/

### Use with VS Code

Follow detailed instructions here: https://code.visualstudio.com/docs/copilot/chat/mcp-servers#_enable-mcp-support-in-vs-code for MCP server setup in VS Code.

Option1: Install from commandline:
```
code --add-mcp "{\"name\":\"mcp-fmu\",\"command\":\"uvx\",\"args\":[\"mcp-fmu\"]}"
```

Option 2: Manual installation:

Follow the instructions here for adding MCP server to VS Code: https://code.visualstudio.com/docs/copilot/chat/mcp-servers#_add-an-mcp-server

Use the following MCP server configuration for `mcp-fmu` server:

```json
{
  "mcp": {
    "servers": {
      "type": "stdio",
      "command": "uvx",
      "args": ["mcp-fmu"],
    }
  }
}
```

### Use with Claude Desktop

Follow detailed instructions here: https://modelcontextprotocol.io/quickstart/user for Claude Desktop MCP server setup.

MCP server configuration for `mcp-fmu`:

```json
{
  "mcpServers": {
    "mcp-fmu": {
      "command": "uvx",
      "args": ["mcp-fmu"],
    }
  }
}
```

*NOTE: Claude Desktop sometimes would fail due to not being able to finx `uvx`, use the absolute path of `uvx` instead*