semvec/pypi-packages huggingface dataset:
nest-ages 0.5.2
A python package that provides neural networks to estimate ages of stars/star populations.

A python package that provides Neural Networks to estimate ages of stars/star populations.

Based on an upcoming paper by Boin et al. 2025.

# Dependencies:
- numpy
- scikit (optional but recommended for speed)
- tqdm (optional)

The [documentation website](https://star-age.github.io/NEST-docs/) guides you through the package usage.

A [Web interface](https://star-age.github.io/) of the project is available to quickly test the Neural Networks:

![image](https://star-age.github.io/NEST-docs/_images/website.png)
CellFreeGMF 0.0.1
cfRNA
Keywords: python, windows, mac, linux, cfRNA, CellFreeGMF


# CellFreeGMF v0.0.1

## Overview
we developed CellFreeGMF, an open-source Python package to identify cfRNA biomarkers and analyze the functional alterations in their originating cells. CellFreeGMF not only enables diagnostic classification of disease samples and identification of cfRNA biomarkers but also elucidate the functional alterations of cfRNA source cells based on cell-free transcriptome and single-cell sequencing data.

## Getting started
See [Documentation and Tutorials](https://cellfreegmf.readthedocs.io).

## Installation
cd CellFreeGMF-main

python setup.py build

python setup.py install

## Citation
Wenxiang Zhang, et al. "Integrative Biomarker Discovery and Cell-of-Origin Tracing from Cell-Free Transcriptomes via Graph Matrix Factorization with Comparative Single-Cell Landscape Modeling in Health and Disease." Submitted.
gd-sm 1.0.6
python实现的国密SM2/SM3/SM4算法库

# gd_sm

## 软件说明

这是一个python实现的国密算法库，按照国密规范实现了sm2/sm3/sm4算法，其中sm2的功能主要是参考[gmssl](https://github.com/py-gmssl/py-gmssl) 库，在其基础上进行了一定的完善。
sm3和sm4是利用了[cryptography](https://github.com/pyca/cryptography)库，采用这个库进行了sm3和sm4运算，这里只是对其进行了简单封装。  

每个模块实现了以下功能，在使用这些函数或方法时，传入的数据需要是16进制字符串或者bytes格式(传入bytes格式也会被转成16进制字符串处理)，
返回的数据均为16进制字符串，建议调用的时候统一使用16进制字符串，为了方便，最好统一成16进制字符串大写格式。

- sm2:
  - sign: 使用sm2私钥进行签名
  - verify: 使用sm2公钥进行验签
  - encrypt: 使用sm2公钥进行加密
  - decrypt: 使用sm2私钥进行加密
  - private_key_export_pubkey: 通过私钥导出公钥
  - nor_compressed_pubkey: 输出公钥的非压缩形式
  - compressed_pubkey: 输出公钥的压缩形式
- sm3:
  - sm3_hash: 计算sm3 hash值
  - check_sm3: 校验sm3 hash值是否正确
  - sm3_hmac: 使用sm3算法计算hmac值，返回类型可以为hex或base64urlsafe编码的字符串
- sm4:
  - encrypt: sm4加密，支持ecb/cbc模式，不填充或者采用pkcs7 padding填充
  - decrypt: sm4解密，支持ecb/cbc模式，不填充或者采用pkcs7 padding填充

## 使用说明

使用pip安装本项目`pip install gd_sm`,在代码中使用`from gd_sm import *`语句引用

也可以clone本项目到本地，在项目根目录下执行`pip install .`安装

## 示例

参考本项目的test下的示例代码example.py

## 打包上传pypi

- 首先安装flit、twine

```pip
pip install flit twine
```

- 修改pyproject.toml文件中的版本号、依赖等相关信息,打包前要先使用pytest运行test目录下的test_*.py文件确认代码没有问题
- 并将代码提交，保存目录下没有未提交的文件

- 在项目根目录下执行`flit build`命令打包，生成dist文件夹，里面包含打包后的文件，执行`twine upload dist/*`命令上传到pypi

## 注意事项

- 目前只支持sm2/sm3/sm4的部分功能，后续会逐步支持更多的功能
clarence 0.2.1
A utility for working with pass
Requires Python: >=3.10

# Clarence

A package for working with [`pass`](https://www.passwordstore.org/). It allows you to fetch and list secrets stored in the password store. It's named for an [excellent passer](https://en.wikipedia.org/wiki/Clarence_Seedorf).

## Installation

Before using Clarence please ensure you have the `pass` command-line tool installed and properly configured.

Then using the package is simply:

```sh
# If using uv
uv add clarence

# Or with pip
pip install clarence
```

## Usage

### Fetching a secret

To fetch a secret from your password store:

```python
from clarence import get_secret
secret = get_secret("path/to/secret")
```

The result of `get_secret` is an object of class `Secret`: this is basically just a regular `str` but with a print method that obscures the secret if you happen to print it to the console. While this helps make it harder to reveal a secret, you still must take care.

When you want to use your secret you need to use the `reveal` method. This makes it obvious when your code is using the unobscured secret.

```python
connection_string = f"mssql+pymssql://1234567:{secret.reveal()}@example.com/testingdb"
```

### Listing secrets

You can view all available secrets:

```python
from clarence import list_secrets
list_secrets()
```

The result is a `SecretsList` object, which will print in the same way as the `pass` command-line utility.

## Documentation

### Functions

#### `get_secret(path: str) -> Secret`

Fetches a secret from the password store.

- Args:
  - `path (str)`: The path to the secret in the password store.
- Returns:
  - `Secret`: The retrieved secret.

#### `list_secrets() -> SecretsList`

Lists all the secrets in the password store.

- Returns:
  - `SecretsList`: An object containing the list of secrets.

---

### Classes

#### `Secret`

Represents a secret as a string, obscured when printing.

- Methods:
  - `__init__(value: str)`: Initializes the secret with its actual value.
  - `__str__() -> str`: Returns an obscured string representation of the secret.
  - `__repr__() -> str`: Returns an obscured string representation for debugging.
  - `__format__(format_spec: str) -> str`: Returns the actual secret value when used in formatted strings.
  - `reveal() -> str`: Returns the actual secret value as a string.

#### `SecretsList`

Represents a list of secrets.

- Methods:
  - `__init__(tree: str)`: Initializes the SecretsList with a tree structure.
  - `__str__() -> str`: Returns the string representation of the tree.
  - `__repr__() -> str`: Returns the string representation of the tree for debugging.
adev-tools 0.1.1
Advanced Development Tools for GitLab, Jira, and CI/CD automation
Requires Python: >=3.8
Keywords: gitlab, jira, ci-cd, development-tools, automation

# ADEV Tools

Advanced Development Tools for GitLab, Jira, and CI/CD automation.

## 소개

ADEV Tools는 GitLab, Jira, 그리고 CI/CD 자동화를 위한 종합적인 개발 도구 모음입니다. 개발자들이 일상적인 작업을 자동화하고 효율성을 향상시킬 수 있도록 설계되었습니다.

## 주요 기능

### GitLab 및 Git 관련 도구
- **adev-fork**: GitLab 저장소 포크 관리
- **adev-branch**: Jira 이슈와 연동된 브랜치 생성
- **adev-commit**: AI 기반 커밋 메시지 생성
- **adev-diff2commit**: 코드 변경사항 기반 커밋 메시지 자동 생성
- **adev-cloneupstream**: 업스트림 저장소 클론

### CI/CD 도구
- **adev-ci-copy**: GitLab Runner를 통한 파일 복사
- **adev-ci-stlink**: ST-Link 관련 CI/CD 작업
- **adev-gitlab-runner-status**: GitLab Runner 상태 확인

### Jira 통합
- **adev-issues**: Jira 이슈 관리 및 처리

### Google Drive 연동
- **adev-gdrive-config**: Google Drive 설정 구성
- **adev-gdrive-upload**: Google Drive 파일 업로드
- **adev-gdrive-open-altium**: Altium 파일을 Google Drive에서 열기

### 빌드 도구
- **adev-pre-build**: 빌드 전 작업 실행
- **adev-post-build**: 빌드 후 작업 실행

### 테스트 도구
- **adev-test-branch**: 브랜치 기능 테스트
- **adev-test-gdrive**: Google Drive 연결 테스트
- **adev-test-upload-download**: 업로드/다운로드 기능 테스트

## 설치

### PyPI에서 설치
```bash
pip install adev-tools
```

### 개발 버전 설치
```bash
git clone https://github.com/jltech/adev-tools.git
cd adev-tools
pip install -e .
```

## 사용법

각 도구는 독립적인 CLI 명령어로 사용할 수 있습니다:

```bash
# GitLab 저장소 포크
adev-fork <search-term>

# Jira 이슈 기반 브랜치 생성
adev-branch [branch-name]

# AI 기반 커밋 메시지 생성
adev-commit

# 파일 복사 (CI/CD)
adev-ci-copy <runner-name> --target <target-path>

# Jira 이슈 관리
adev-issues
```

## 설정

대부분의 도구들은 설정 파일을 필요로 합니다. 설정 템플릿을 생성하려면:

```bash
adev-config-example
```

### 필수 설정 항목
- GitLab 토큰
- Jira 인증 정보
- Google Drive API 키 (선택사항)
- OpenAI API 키 (AI 기능 사용시)

## 종속성

- Python 3.8+
- GitPython
- python-gitlab
- jira
- requests
- 기타 (requirements.txt 참조)

## 개발

### 개발 환경 설정
```bash
git clone https://github.com/jltech/adev-tools.git
cd adev-tools
pip install -e ".[dev]"
```

### 테스트 실행
```bash
pytest
```

### 코드 포맷팅
```bash
black .
flake8 .
```

## 기여

1. Fork 프로젝트
2. 기능 브랜치 생성 (`git checkout -b feature/AmazingFeature`)
3. 변경사항 커밋 (`git commit -m 'Add some AmazingFeature'`)
4. 브랜치에 Push (`git push origin feature/AmazingFeature`)
5. Pull Request 생성

## 라이센스

이 프로젝트는 MIT 라이센스를 따릅니다. 자세한 내용은 `LICENSE` 파일을 참조하세요.

## 지원

- 이슈 리포트: [GitHub Issues](https://github.com/jltech/adev-tools/issues)
- 문서: [GitHub Wiki](https://github.com/jltech/adev-tools/wiki)

## 업데이트 로그

### v0.1.0
- 초기 릴리스
- GitLab, Jira, CI/CD 기본 기능 구현
- Google Drive 연동 기능
- AI 기반 커밋 메시지 생성
density-aware-smote 0.1.0
Density Aware SMOTE: A Python package for density-aware synthetic oversampling
Requires Python: >=3.7
Keywords: SMOTE, oversampling, machine-learning, imbalanced-data, synthetic-data

# Density Aware SMOTE

![PyPI version](https://img.shields.io/pypi/v/density-aware-smote?color=blue)
![Python versions](https://img.shields.io/pypi/pyversions/density-aware-smote)
![License](https://img.shields.io/github/license/nbeeeel/Improved-Oversampling-Density-Aware-Smote)

A Python package implementing **Density Aware SMOTE**, developed in the [**Improved-Oversampling-Density-Aware-Smote** repository](https://github.com/nbeeeel/Improved-Oversampling-Density-Aware-Smote).  
This method enhances SMOTE by generating synthetic samples based on **local data density**, reducing oversampling in sparse regions and preserving valuable patterns in dense areas.

---

##  Features
- Density-aware oversampling tuned for local distribution  
- Flexible sampling strategy: `auto`, ratio float, or custom class counts  
- Custom neighbor selection: `random`, `nearest`, `farthest`  
- Seamless integration with scikit-learn pipelines  
- Built-in visuals: class distribution, synthetic samples, decision boundaries  

---

##  Installation

```bash
pip install density-aware-smote
mcp-dingding 0.1.0
Add your description here
Requires Python: >=3.11.13

## DINGDING MCP
基于 Model Context Protocol (MCP) 的钉钉推送。


## 工具列表
| name | description |
| ----------- | ----------- |
| auth  | key验证 |
| push | 推送消息 |

## MCP 服务器配置
```
{
  "mcpServers": {
    "dingding": {
      "isActive": true,
      "name": "calculator",
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        "/Users/xiezi/explore/mcp-dingding/src",  # 替换为自己的路径
        "run",
        "-m",
        "mcp_dingding"
      ]
      }
  }
}

{
  "mcpServers": {
    "mcp_dingding": {
        "command": "uvx",
        "args": [
          "mcp_dingding@latest"
        ]
      }
  }
}
```
jpy-sync-db-lite 2025.5.0
Jim's Python - Synchronous Database Wrapper for SQLite
Requires Python: >=3.10
Keywords: sqlite, database, synchronous, threading, sqlalchemy, orm

# jpy-sync-db-lite

Jim's Python - Synchronous Database Wrapper for SQLite

A lightweight, thread-safe SQLite database wrapper built on SQLAlchemy with optimized performance for concurrent operations.

## Features

- **Thread-safe operations** via a single persistent connection protected by locks
- **SQLAlchemy 2.0+ compatibility** with modern async patterns
- **Performance optimized** with SQLite-specific pragmas
- **Simple API** for common database operations
- **Consolidated operations** for both single and bulk operations
- **Batch SQL execution** for multiple statements in a single operation
- **Transaction support** for complex operations
- **Statistics tracking** for monitoring performance
- **Robust SQL parsing** using sqlparse library for reliable statement parsing
- **SQLite-specific management** with VACUUM, ANALYZE, integrity checks, and PRAGMA configuration
- **Database optimization tools** for performance tuning and maintenance
- **Enhanced error handling** with SQLite-specific exception types

## Quick Start

```python
from jpy_sync_db_lite.db_engine import DbEngine

with DbEngine('sqlite:///my_database.db') as db:
    # Create table
    db.execute("""
        CREATE TABLE users (
            id INTEGER PRIMARY KEY,
            name TEXT NOT NULL,
            email TEXT UNIQUE
        )
    """)

    # Insert data
    db.execute(
        "INSERT INTO users (name, email) VALUES (:name, :email)",
        {"name": "John Doe", "email": "john@example.com"}
    )

    # Query data
    users = db.fetch("SELECT * FROM users")
    print(users)
```

## Documentation

For detailed documentation including API reference, installation instructions, and examples, see [docs/README-details.md](docs/README-details.md).
tezos-smartpy 0.22.0
A comprehensive solution for developing, testing, and deploying smart contracts on the Tezos blockchain.
Requires Python: <4.0,>=3.10

# SmartPy - Smart Contracts on Tezos

A comprehensive solution for developing, testing, and deploying smart contracts
on the Tezos blockchain. All in familiar Python syntax.

## Getting Started

### Prerequisites

- Python 3.10 or later.
- Pip (Python package installer)

### Installation

SmartPy is available on the Python Package Index (PyPi). To install it, just run
the following command in your terminal:

```sh
pip install tezos-smartpy
```

If you're using a system with both Python 2 and Python 3, you might need to use
pip3 instead:

```sh
pip3 install tezos-smartpy
```

## Documentation

You can find more detailed documentation and tutorials at the official SmartPy
documentation site: https://smartpy.io/docs

## License

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