semvec/pypi-packages huggingface dataset:
tokyo-sports-scraper 0.1.7
東京スポーツのオートレース情報をスクレイピングするライブラリ
Requires Python: >=3.10

# Tokyo Sports Scraper

[![tests](https://github.com/autorace/tokyo-sports-scraper/actions/workflows/tests.yml/badge.svg)](https://github.com/autorace/tokyo-sports-scraper/actions/workflows/tests.yml)
[![publish](https://github.com/autorace/tokyo-sports-scraper/actions/workflows/publish.yml/badge.svg)](https://github.com/autorace/tokyo-sports-scraper/actions/workflows/publish.yml)
![issues](https://img.shields.io/github/issues/autorace/tokyo-sports-scraper)
![pull requests](https://img.shields.io/github/issues-pr/autorace/tokyo-sports-scraper)
[![license](https://img.shields.io/github/license/autorace/tokyo-sports-scraper)](LICENSE)
[![last commit](https://img.shields.io/github/last-commit/autorace/tokyo-sports-scraper)](https://github.com/autorace/tokyo-sports-scraper/commits/main)

東京スポーツのオートレース情報をスクレイピングするライブラリです。

## インストール
```bash
pip install tokyo-sports-scraper
```

## 使い方
```python
from tokyo_sports_scraper import scrape, Race, Rider

if __name__ == '__main__':
    race_info: Race = scrape('2025/08/04', 5, 8)

    print(f'タイトル: {race_info.title}')
    print(f'サブタイトル: {race_info.subtitle}')
    print(f'天候: {race_info.weather}')
    print(f'気温: {race_info.temperature}')
    print(f'湿度: {race_info.humidity}')
    print(f'走路温度: {race_info.pavement_temperature}')
    print(f'走路状況: {race_info.track_condition}')
    print('---')

    for rider_info in race_info.riders:
        print(f'車番: {rider_info.number}')
        print(f'名前: {rider_info.name}')
        print(f'LG: {rider_info.locker_ground}')
        print(f'期別: {rider_info.registration_term}')
        print(f'年齢: {rider_info.age}')
        print(f'車級: {rider_info.bike_class}')
        print(f'ランク: {rider_info.rank}')
        print(f'審査ポイント: {rider_info.points}')
        print(f'ハンデ: {rider_info.handicap}')
        print(f'試走タイム: {rider_info.trial_time}')
        print(f'試走偏差: {rider_info.trial_deviation}')
        print(f'平均試走タイム: {rider_info.average_trial_time}')
        print(f'平均競走タイム: {rider_info.average_race_time}')
        print(f'最高競走タイム: {rider_info.fastest_race_time}')
        print('---')
```

## ライセンス
Tokyo Sports Scraperは [MITライセンス](LICENSE) の元で公開されています。
qiflib 1.0
A tiny, focused Python library for Quantitative Information Flow (QIF): model secrets, channels, and analyze leakage via the g-vulnerability framework.
Requires Python: >=3.9

**QIFLib** is a Python library for modeling, analyzing, and quantifying information leakage in probabilistic systems using the **Quantitative Information Flow (QIF)** framework.  
It provides a clean, composable set of primitives — *secrets*, *channels*, *hyper-distributions*, *vulnerability* and *uncertainty* functions — so you can build end-to-end analyses of how much information an adversary could learn.

For more details see the book [The Science of Quantitative Information Flow](https://link.springer.com/book/10.1007/978-3-319-96131-6).

## Features
- **Secrets Modeling** — define hidden states with flexible prior distributions.
- **Channels** — represent probabilistic mappings $p(y\mid x)$ from secrets to observations.
- **Hyper-distributions** — automatically compute joint $p(x,y)$, outer $p(y)$, and inners $p(x\mid y)$.
- **Vulnerability Analysis** — use $g$-vulnerability to quantify an adversary’s expected gain before and after observing outputs.
- **Uncertainty Analysis** — use $\ell$-uncertainty to measure the expected loss under optimal strategies.
- **Leakage Metrics** — compute additive and multiplicative leakage.
- **Flexible Functions** — provide gain/loss as matrices or Python callables.

Why QIF?
--------

Traditional security analysis asks whether a secret *can* be inferred. QIF quantifies **how much** an adversary is expected to learn, under probabilistic models of system behavior and strategies.  
Useful for:

- privacy-preserving data publishing,
- side-channel analysis,
- communication protocol evaluation,
- decision-making under uncertainty.

## Installation
You can install via PyPI:

```bash
pip install qiflib
```

or manuallly by copying this repository to your local machine and running:

```bash
pip install path/to/qiflib   
```

To verify if the package was installed correctly, you can run tests:

```bash
cd path/to/qiflib
python -m unittest discover tests
```

## Example

```python
from qiflib.core.secrets import Secrets
from qiflib.core.channel import Channel
from qiflib.core.hyper import Hyper
from qiflib.core.gvulnerability import GVulnerability

# Secrets & prior
secrets = Secrets(["x0", "x1"], [0.6, 0.4])

# Channel matrix: rows sum to 1
outputs = ["y0", "y1"]
C = [
      [0.8, 0.2],  # p(y|x0)
      [0.1, 0.9],  # p(y|x1)
]
channel = Channel(secrets, outputs, C)

# Hyper-distribution
hyper = Hyper(channel)

# Gain function (1 if correct guess, else 0)
actions = ["guess_x0", "guess_x1"]
def gfun(w, x): return 1.0 if w == x else 0.0

g = GVulnerability(secrets, actions, gfun)

print("Prior vulnerability:", g.prior_vulnerability())
print("Posterior vulnerability:", g.posterior_vulnerability(hyper))
```

## License

This project is licensed under the [MIT License](https://opensource.org/license/mit).
llm-tavily 0.0.2
LLM plugin to interact with Tavily API
Requires Python: >=3.9
Keywords: llm, tavily

# llm-tavily

[![PyPI](https://img.shields.io/pypi/v/llm-tavily.svg)](https://pypi.org/project/llm-tavily/)
[![Changelog](https://img.shields.io/github/v/release/ftnext/llm-tavily?include_prereleases&label=changelog)](https://github.com/ftnext/llm-tavily/releases)
[![Tests](https://github.com/ftnext/llm-tavily/actions/workflows/test.yml/badge.svg)](https://github.com/ftnext/llm-tavily/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/ftnext/llm-tavily/blob/main/LICENSE)



## Installation

Install this plugin in the same environment as [LLM](https://llm.datasette.io/).
```bash
llm install llm-tavily
```
## Usage

**prerequisite**: Tavily API key (Free, No credit card, 1,000 API credits / month)  
https://www.tavily.com/#pricing  
https://blog.tavily.com/getting-started-with-the-tavily-search-api/

```bash
export LLM_TAVILY_KEY=your_api_key_here

llm -m qa-tavily 'Who is Leo Messi?'
```

## Development

To set up this plugin locally, first checkout the code:
```bash
cd llm-tavily
```
Then create a new virtual environment and install the dependencies and test dependencies:
```bash
uv sync --extra test
```
To run the tests:
```bash
uv run pytest
```