semvec/pypi-packages huggingface dataset:
xcpcio 0.84.0
xcpcio python lib
Requires Python: >=3.11
Keywords: xcpcio

# xcpcio-python

Python library and CLI tools for XCPCIO.

## Features

- **Type Definitions**: Pydantic models for contest data structures (teams, submissions, problems, etc.)
- **Constants**: Shared constants for submission statuses, time units, and penalty calculations
- **CCS Archiver**: CLI tool to archive CCS API data to contest package format
- **Contest API Server**: CLI tool to serve contest packages via CCS API
- **Cross-Language Compatibility**: Mirrors TypeScript types for data consistency

## Installation

```bash
pip install xcpcio
```

Or install with [uv](https://github.com/astral-sh/uv):

```bash
uv add xcpcio
```

## Development

### Setup

```bash
# Clone repository
git clone https://github.com/xcpcio/xcpcio.git
cd xcpcio/python

# Install dependencies with uv
uv sync
```

### Testing

```bash
# Run tests
uv run pytest

# Run specific test file
uv run pytest tests/test_types.py
```

## Documentation

For detailed documentation, visit:

- [Clics Utility Guide](https://xcpcio.com/guide/clics-utility)

## License

[MIT](../LICENSE) License &copy; 2020 - PRESENT [XCPCIO][xcpcio]

[xcpcio]: https://xcpcio.com
sqlalchemy-query-helpers 1.0.107
Some helpers for SQLAlchemy
Requires Python: >=3.10

Пакет содержит хелперы для пакета SQLAlchmy - методов `insert`, `upsert`, `update` и другое.

Инициализация: `db = DB(db_name, Base, use_os_env=False, echo=False)`. Где:
* `use_os_env`. Значение `True` означает брать определения для подключения к базе данных из переменных окружения хоста: `os.environ['DB_USER'], os.environ['DB_PASSWORD'], os.environ['DB_HOST']`. При значении `False` переменные user, password, host берутся из файла `cfg.py`, который должен быть создан в каталоге скрипта.

## Экземпляр класса содержит
Методы:
* `insert_many`, `upsert` и другие

Свойства:
* `engine` - подключение к базе данных
* `name` - имя базы данных
* `base`: DeclarativeMeta
* `Session: sessionmaker` - фабрика сессий
* `session: Session` - инициализированная сессия

## Пример использования

Определяем модель таблицы в файле `db_model.py`:
```python
from sqlalchemy import Column, Integer, String, Date, ForeignKey
from sqlalchemy.dialects.mysql import TINYINT, SMALLINT, INTEGER, ENUM, FLOAT
from sqlalchemy.schema import Index
from sqlalchemy.orm import declarative_base

Base = declarative_base()

db_name = 'some_database'


class ATable(Base):
    __tablename__ = 'A_table'
    id = Column(INTEGER(unsigned=True), primary_key=True, autoincrement=True)
    name = Column(String(100), nullable=False)
```

Основной файл:
```pythonа
from sqlalchemy_query_helpers import DB
from db_model import db_name, Base, ATable

db = DB(db_name, Base)

values_from_database = db.session.query(ATable).all()
```

#### Пример `cfg.py`
```python
# Settings of database
host = '100.100.100.100'
user = 'root'
password = 'qwerty'
```
spelling-bee-tts 0.4.2
GTK4 spelling quiz app with text-to-speech and AI generated example sentences.
Requires Python: >=3.9

![Spelling Bee icon](spelling-bee-tts.png)

Spelling Bee TTS
================

Spelling Bee TTS is a spelling testing app I wrote for my kids.  It uses natural text-to-speech for the prompt and a local large language model
to generate definitions and example sentences.  It has a built in corpus of ~31,000 words.

The app tracks the user's progress and automatically adjusts the word difficulty level based on how well they're doing.

![Screenshot](screenshot.png)

Install
-------

System dependencies (Ubuntu):

```
sudo apt install python3-gi gir1.2-gtk-4.0 mpv
```

System dependencies (macOS with Homebrew):

```
brew install python gtk4 pygobject3 gobject-introspection mpv
```

Install with `pip`:

```
pip install spelling-bee-tts
```

Run
---
On Linux `pip` creates a application shortcut.  Hit your launcher and search for 'spell'.  Or to run from the command line:

```
python3 -m spellingbee
```

Notes
-----
- `edge-tts` requires network access.
- You can override the voice with `EDGE_TTS_VOICE` (default: `en-US-AriaNeural`).
- Offline sentence generation uses `llama-cpp-python` with a local GGUF model.
  - By default, the app prompts to download Qwen3-4B-Instruct-2507 Q8_0 into the Hugging Face cache on first use.
  - Override with `LLM_REPO_ID` or `LLM_MODEL_PATH`.
  - Optional tuning: `LLM_N_CTX`, `LLM_THREADS`, `LLM_N_BATCH`, `LLM_TEMPERATURE`, `LLM_TOP_P`.
xcals 0.1.6
A股交易日历以及日内交易时间
Requires Python: >=3.8

# xcals

### 安装

```bash
pip install -U xcals
```

### 使用示例

```python
import xcals

print(xcals.__version__)

# 更新最新交易日数据(不定时更新)
xcals.update()

# 获取交易日列表
xcals.get_tradingdays()

# 获取最新交易日
xcals.get_recent_tradeday()

# 获取前5个交易日
xcals.shift_tradeday("2025-05-06", -5)

# 获取最新财报日期
xcals.get_recent_reportdate("2025-05-06") # 2025-03-31

# 获取前5个财报日期
xcals.shift_reportdate("2025-03-31", -5)

# 获取日内交易时间
xcals.get_tradingtime(freq="1s") # 分钟频: freq="1min"

# 移动交易时间
xcals.shift_tradetime("09:45:00", "10min5s") # 前推5秒: -5s
```