semvec/pypi-packages huggingface dataset:
gmol-base 0.1.0b2
Basic components of GalaxyMol
Requires Python: <3.14,>=3.10

# gmol-base

Basic components of GalaxyMol.

## Notes to developers

Before starting development, make sure to create the virtual environment and
install the pre-commit hooks by running the following command:

```bash
uv sync
uv run pre-commit install --install-hooks --overwrite
```

Before you push to the development branch, make sure to run mypy checks for
any type-related errors.

```bash
mypy [--pretty]
```

Although it's not a strict requirement, consider following the
[conventional commits](https://www.conventionalcommits.org/en/v1.0.0/)
guidelines for commit messages. This will help reviewers to understand the
changes in a more structured way.

## License

This project is licensed under the Apache License, Version 2.0. See the
[LICENSE](LICENSE) file for details.
django-interval 0.5.4
A Django field for storing fuzzy date information
Requires Python: >=3.11

# Django Interval

django-interval is a [Django](https://www.djangoproject.com/) app. It provides
model fields to store date information with some extra data stored in
additional fields. The additional fields are composed of a `_date_sort`, a `_date_from`
and a `_date_to` field that store data that is generated from the string stored in
the main model field.

# Installation

Install `django-interval` and add `django_interval` to your
[INSTALLED_APPS](https://docs.djangoproject.com/en/stable/ref/settings/#std-setting-INSTALLED_APPS)
add the `django_interval.urls` to your `urlpatterns`:
```
urlpatterns += [path("", include("django_interval.urls"))]
```

Use either the `django_interval.fields.FuzzyDateParserField` or the
`django_interval.fields.FuzzyDateRegexField` in your models. Both
come with sensible defaults (a parser method in the `FuzzyDateParserField`
and a list of regexes in the `FuzzyDateRegexField`), but it is possible
to pass custom parser or regexes as arguments.
b64image 0.1.2
Base64 image encoding/decoding with LLM API support
Requires Python: >=3.12

# b64image

Base64 image encoding/decoding with LLM API support.

## Installation

```bash
uv add b64image
```

## Usage

### Python API

```python
from b64image import Base64Image, to_openai, to_anthropic, create_vision_message

# Load from various sources
img = Base64Image.from_path("photo.png")
img = Base64Image.from_url("https://example.com/image.jpg")
img = Base64Image.from_bytes(raw_bytes)
img = Base64Image.from_base64("iVBORw0KGgo...")
img = Base64Image.from_auto("photo.png")  # auto-detect source type

# Convert to outputs
img.to_bytes()        # raw bytes
img.save("output.png") # save to file
img.data_uri          # data:image/png;base64,...

# LLM API formats
to_openai(img)     # OpenAI vision format
to_anthropic(img)  # Anthropic Claude format
to_google(img)     # Google Gemini format

# Create complete vision message
msg = create_vision_message(
    "What's in this image?",
    [img],
    provider="openai"  # or "anthropic", "google"
)
```

### Batch Processing

```python
from b64image import load_images, load_images_from_directory

# Load multiple images
for img in load_images(["a.png", "b.jpg", "https://example.com/c.webp"]):
    print(img.ext, img.size_bytes)

# Load from directory
for img in load_images_from_directory("./images", recursive=True):
    print(img.data_uri)
```

### CLI

```bash
# Encode image to base64
b64image encode photo.png
b64image encode photo.png --format raw
b64image encode photo.png --llm openai

# Encode directory
b64image encode ./images/ -r

# Decode base64 to file
b64image decode "data:image/png;base64,..." -o output.png

# Show image info
b64image info photo.png
```

## Supported Formats

JPEG, PNG, GIF, WebP, BMP
django-base-ai 0.0.2
Django/DRF/RBAC 权限与常用 AI 接口封装
Requires Python: >=3.9

# django-base-ai

Django / DRF / RBAC 权限与常用基础能力封装，可作为业务项目的基础依赖包（如 DocsAI、Survey 等）复用。

- **Python**：3.9+
- **包名**：`django-base-ai`（导入名：`django_base_ai`）

---

## 功能概览

| 模块 | 说明 |
|------|------|
| **RBAC** | 用户、角色、部门、菜单、按钮权限、接口白名单、数据权限过滤 |
| **认证** | JWT（SimpleJWT）、单点登录、第三方登录、验证码 |
| **系统配置** | 字典、系统配置键值、调度与配置中心（`dispatch`） |
| **基础能力** | 文件上传/列表、导入导出（Excel）、操作日志、消息中心、WebSocket |
| **工具** | 统一 JSON 响应、分页、异常处理、权限校验（`validation_api`）、加解密、任务执行等 |

---

## 安装

```bash
# 克隆或进入项目目录
cd DjangoBaseAi

# 依赖安装
pip install -r requirements.txt

# 或可编辑安装（开发时推荐）
pip install -e ".[dev]"
```

`[dev]` 会安装 pytest、pytest-django、ruff。

---

## 在项目中使用

1. **安装**：将本仓库可编辑安装或打包后安装到目标 Django 项目环境。

2. **配置**：在目标项目的 `settings.py` 中：
   - 将 `django_base_ai` 加入 `INSTALLED_APPS`
   - 按需配置数据库、缓存、JWT、CORS 等（可参考本仓库 `DjangoBaseAi/settings.py` 或 `django_base_ai/settings.py`）
   - 自定义异常处理：`EXCEPTION_HANDLER = "django_base_ai.utils.exception.custom_exception_handler"`

3. **路由**：在目标项目 `urls.py` 中挂载：
   ```python
   from django_base_ai.system.urls import urlpatterns as system_urls
   urlpatterns = [..., path("api/", include(system_urls))]
   ```

4. **初始化**：执行迁移、并可通过 `django_base_ai.dispatch` 及 system fixtures 初始化菜单、角色、字典等。

---

## 项目结构（简要）

```
DjangoBaseAi/
├── django_base_ai/          # 主包（供其他项目安装引用）
│   ├── dispatch.py          # 配置/调度入口
│   ├── settings.py          # 默认配置片段
│   ├── system/              # 系统模块：模型、视图、权限、fixtures、management 命令
│   ├── utils/               # 工具：认证、权限、响应、分页、导入导出、加解密等
│   ├── websocket/           # WebSocket 路由与配置
│   └── templates/
├── DjangoBaseAi/            # 本仓库作为“可运行项目”时的应用入口（settings、asgi、urls）
├── conf/                    # 环境配置（env/test/pro）
├── tests/                   # pytest 测试
├── pyproject.toml           # 项目与 ruff/pytest 配置
├── requirements.txt
└── README.md
```

---

## 开发约定（与 Rules/python.mdc 一致）

- **PEP 8**：全包使用小写+下划线命名，`ruff format .` 与 `ruff check .` 通过后再提交。
- **类型与文档**：新代码与核心模块使用类型注解与 docstring。
- **测试**：使用 pytest，`DJANGO_SETTINGS_MODULE` 已写在 `pyproject.toml`，在项目根执行 `pytest` 即可。

### 常用命令

```bash
# 代码格式与检查
ruff format .
ruff check . --fix

# 测试
pytest
pytest tests/ -v

# 打包
pip install build && python -m build
```

---

## 许可证

见仓库根目录 `LICENSE`。
pyfemtet 1.8.3
Design parameter optimization using Femtet.
Requires Python: <3.15,>=3.10

# Welcome! PyFemtet is the extension package for Femtet.

## Main Features

<img src="img/pyfemtet_conceptual_diagram.png" width="450">

> [!NOTE]
> For details, see the documentation of "Related Pages" section.

### Design Parameter Optimization
- Single or multiple objective optimization
- Progress monitor GUI
- Parallel computation with controling multiple Femtet processes
- Result output in easy-to-analyze csv format for Excel and other tools

### Dispatch Extensions
- Create `CFemtet` object with process ID specification

## Related Pages

- Femtet Website: https://www.muratasoftware.com/
- Documentation (English): https://pyfemtet.readthedocs.io/en/stable/
- ドキュメント（日本語）: https://pyfemtet.readthedocs.io/ja/stable/
- Source code: https://github.com/pyfemtet/pyfemtet
- Bug reports: https://github.com/pyfemtet/pyfemtet/issues


## Getting Started

> [!NOTE]
> Currently Femtet and `pyfemtet` supports **Windows only**.

### 1. Install Femtet

- Access [Murata Software Website](https://www.muratasoftware.com/) and get **Femtet trial version** or purchase a lisence.
- Get installer and launch it.
- **Run 'EnableMacros'** from the start menu.
> [!NOTE]
> This procedure requires administrator privileges.


### 2. Install PyFemtet

> [!NOTE]
> The commands on this section are for `CMD` or `PowerShell` on with `py launcher`.
> For a virtual environment, replace `py` to `python`.
> If you do not have Python, please install it first.

- Get `pyfemtet` via following command:

    ```py -m pip install pyfemtet```


### 3. Setting `win32com` Constants

- Run following command to enable COM constants:

    ```py -m win32com.client.makepy FemtetMacro```
    
That's all! Now you can use Femtet's extention features via `pyfemtet`.
For more information including sample code and FEM projects, see the documentation of "Related Pages" section.
our-compliance 0.1.0
GDPR/PII compliance module for the ourochronos ecosystem
Requires Python: >=3.11

# our-compliance

GDPR/PII compliance module for the ourochronos ecosystem.

## Features

- **Data Deletion**: GDPR Article 17 (Right to Erasure) compliant deletion with cryptographic erasure
- **PII Scanning**: Detect and block PII (emails, phone numbers, SSNs, credit cards, etc.)
- **Tombstone Records**: Federation-aware deletion propagation with audit trails

## Installation

```bash
pip install our-compliance
```

## Usage

```python
from our_compliance import delete_user_data, scan_for_pii, DeletionReason

# Scan text for PII
matches = scan_for_pii("Contact me at user@example.com")

# Delete user data with compliance audit trail
await delete_user_data(user_id, reason=DeletionReason.USER_REQUEST)
```

## Development

```bash
pip install -e ".[dev]"
ruff check src/ tests/
mypy src/
pytest tests/ -m "not integration"
```
our-consensus 0.1.0
VRF-based consensus, validator selection, and anti-gaming for the ourochronos ecosystem
Requires Python: >=3.11

# our-consensus

VRF-based consensus, validator selection, and anti-gaming for the ourochronos ecosystem

## Overview

_Brief description of what this brick does and why it exists._

## Install

```bash
pip install our-consensus
```

## Usage

```python
from our_consensus import ...
```

## API

_Document the public interface here._

## Development

```bash
# Install with dev dependencies
make dev

# Run linters
make lint

# Run tests
make test

# Run tests with coverage
make test-cov

# Auto-format
make format
```

## State Ownership

_Document what state this brick owns (tables, caches, files, etc.)._

## License

MIT
our-confidence 0.1.0
Dimensional confidence system for belief scoring
Requires Python: >=3.11

# our-confidence

Dimensional confidence system for belief scoring. Part of the [ourochronos](https://github.com/ourochronos) ecosystem.

## Installation

```bash
pip install our-confidence
```

## Usage

```python
from our_confidence import DimensionalConfidence, ConfidenceDimension

# Simple confidence
conf = DimensionalConfidence.simple(0.8)

# Full dimensional confidence
conf = DimensionalConfidence.full(
    source_reliability=0.9,
    method_quality=0.7,
    internal_consistency=0.8,
    temporal_freshness=0.95,
    corroboration=0.6,
    domain_applicability=0.85,
)

# Dimension registry for validation
from our_confidence import get_registry
registry = get_registry()
result = registry.validate("v1.confidence.core", {"source_reliability": 0.8})
```

## Development

```bash
make dev    # Install with dev dependencies
make test   # Run tests
make lint   # Run linters
```
our-db 0.1.0
Database connectivity and configuration brick
Requires Python: >=3.11

# our-db

Database connectivity, configuration, and migration brick. Part of the [ourochronos](https://github.com/ourochronos) ecosystem.

## Installation

```bash
pip install our-db

# With async support
pip install our-db[async]
```

## Usage

```python
from our_db import get_cursor, get_config

# Database access
with get_cursor() as cur:
    cur.execute("SELECT * FROM my_table")
    rows = cur.fetchall()

# Configuration
config = get_config()
print(config.db_host, config.db_port)
```

### Async

```python
from our_db import async_cursor

async with async_cursor() as conn:
    rows = await conn.fetch("SELECT * FROM my_table")
```

### Migrations

```python
from our_db import MigrationRunner

runner = MigrationRunner(migrations_dir="./migrations")
runner.up()        # Apply pending
runner.status()    # Check status
```

## Development

```bash
make dev    # Install with dev dependencies
make test   # Run tests
make lint   # Run linters
```
althenaxavier 1.0.0
Industrial-grade AI-powered big data processing engine
Requires Python: >=3.8

# AlthenaXavier 🚀

Industrial-grade AI-powered data processing engine for large CSV files.

## Features
- Big Data processing (1GB → 100GB+)
- Adaptive chunk processing
- CLI tool
- Validation & logging
- AI-ready architecture
- PyPI package support

## Install
everruns-sdk 0.1.2
Python SDK for Everruns API
Requires Python: >=3.10
Keywords: agents, ai, api, everruns, sdk

# Everruns SDK for Python

Python SDK for the Everruns API.

## Installation

```bash
pip install everruns-sdk
```

## Quick Start

```python
import asyncio
from everruns_sdk import Everruns

async def main():
    # Uses EVERRUNS_API_KEY environment variable
    client = Everruns()
    
    # Create an agent
    agent = await client.agents.create(
        name="Assistant",
        system_prompt="You are a helpful assistant.",
    )
    
    # Create a session
    session = await client.sessions.create(agent_id=agent.id)
    
    # Send a message
    await client.messages.create(session.id, "Hello!")
    
    # Stream events
    async for event in client.events.stream(session.id):
        if event.type == "output.message.completed":
            print(event.data)
            break
    
    await client.close()

asyncio.run(main())
```

## License

MIT
moxtools 0.8.2
MoxTools - Python tools

# MoxTools

MoxTools is a library for analyzing data and data plotting.

## 📌 Features
- **File Management** : Files Search Open/Read/Write
- **Radar IO**
- **Radar SP**
- **Data IO**
- **Data Proc**
- **Parse Tools** : parse log/string tools
- **serial worker** : serial port 
- **Chart Plot** 
- **app logging** : debug log for all system
- **Log utilities** : Save log data to file

## 🛠️ Supported Tools
- `asmt_tool`: A specialized tool for data analysis.

## 📦 Installation
```bash
pip install MoxTools
pip install --upgrade MoxTool
pip install MoxTools==0.5.2rc6
```
https://pypi.org/project/MoxTools/

## 🚀 Usage
```python
    from . import app_logging, asmt_tool, chart, dataIO, dataProc, fileMg, log_utils, lsf, parseTools, radarIO, radarSP, testImage
    from .radarSP import convert_xz2Angle
    from .app_logging import lazy_logger, setup_logging, get_logger
    from .log_utils import FileLogger
    from .dataIO import readTXT_cfgParam, to_cmd_param
    from .parseTools import parseLine_mcuDETOUT, paresLine_mcuLog
    from .fileMg import find_files_with_extensions, generate_output_path, extractFileNameInfo, is_matching_fileName

```

## 📜 version
🧪 0.8.2    workable without pandas
🧪 0.8.1    add parseFn_pattern() & file_file_with_extensions() wuth sort     
🧪 0.8.0    update for snrRxPowr     
🧪 0.7.2    parse FFT-HIST log      
🧪 0.7.1    parseEVK_fileNameTime can parse `_asmt` suffix file.  
🧪 0.7.0    add CCDD ack handling in SerialWorker  
🧪 0.6.0    add parse 3D MCU log  
🧪 0.3.2    add `add minor ytick for scatter chart` & `read detout data log from files`   
🧪 0.3.1    add `is_matching_fileName()`   
🧪 0.3.0    support `read_rs60g_csv_2APN()` & `parse MCU 2APN log`   
🧪 0.2.1    fix "read log file error issue(CP950): UTF-8"  
🧪 0.2.0    fix "read_rs60g_csv() read clst_r0 error" issue