semvec/pypi-packages huggingface dataset:
excitation-rk4-sparse 0.2.0
High-performance sparse matrix RK4 solver for quantum excitation dynamics
Requires Python: >=3.8
Keywords: quantum dynamics rk4 sparse physics simulation

# Excitation RK4 Sparse

量子力学的な励起ダイナミクスを計算するための疎行列ベースのRK4ソルバー。

## 機能
- CSR形式の疎行列サポート
- OpenMPによる並列化（動的スケジューリング最適化）
- Python/C++のハイブリッド実装
- 包括的なベンチマーク機能
  - 2準位系と調和振動子のテストケース
  - 詳細なパフォーマンス分析
  - 解析解との比較
- メモリ最適化
  - キャッシュライン境界を考慮したアライメント
  - 疎行列パターンの再利用

## バージョン情報
- 現在のバージョン: v0.2.0
- ステータス: 安定版
- 最終更新: 2024-01-08
- **新機能**: プロジェクト構造の大幅改善、性能問題の解決

## 必要条件
- Python 3.10以上
- C++17対応コンパイラ
- CMake 3.16以上
- pybind11
- Eigen3
- OpenMP（推奨）

## インストール

### 開発用インストール（推奨）
```bash
git clone https://github.com/1160-hrk/excitation-rk4-sparse.git
cd excitation-rk4-sparse

# C++ライブラリのビルド
./tools/build.sh --clean

# Pythonパッケージのインストール
pip install -e .
```

### クイックテスト
```bash
# 自動テストの実行
python tools/test_examples.py
```

## 使用例

### 基本的な使用法
```python
from excitation_rk4_sparse import ExcitationRK4Sparse
from excitation_rk4_sparse.bindings import ExcitationRK4SparseCpp

# Python実装
solver_py = ExcitationRK4Sparse()
result_py = solver_py.solve(H0, mux, muy, Ex, Ey, psi0, dt, True, stride, False)

# C++実装（高速）
solver_cpp = ExcitationRK4SparseCpp()
result_cpp = solver_cpp.solve(H0, mux, muy, Ex, Ey, psi0, dt, True, stride, False)
```

### 例題
すべての例は新しい構造で整理されています：

1. **基本例**
```bash
python examples/basic/example_rk4.py  # 簡単な2準位系
```

2. **量子システム例**
```bash
python examples/quantum/two_level_excitation.py   # 2準位励起
python examples/quantum/harmonic_oscillator.py   # 調和振動子
```

3. **ベンチマーク**
```bash
python examples/benchmarks/benchmark_comparison.py
```

## ベンチマーク
以下のスクリプトで様々なベンチマークを実行できます：

1. 実装間の比較
```bash
python examples/benchmark_comparison.py  # 基本的な比較
python examples/benchmark_ho.py         # 調和振動子系での比較
```

2. 詳細なプロファイリング
```bash
python examples/profile_comparison.py   # CPU使用率、メモリ使用量など
```

## 性能

最新の最適化により期待される高性能を実現：

| システムサイズ | Python [ms] | C++ [ms] | 速度比 |
|-------------:|------------:|----------:|-------:|
| 50レベル      | 12.8        | 0.5       | **23.65x** |
| 100レベル     | 14.5        | 0.9       | **15.69x** |
| 200レベル     | 17.3        | 1.8       | **9.81x** |
| 500レベル     | 12.2        | 2.9       | **4.29x** |

## 最適化の特徴

### v0.2.0での主要改善
1. **条件付きデバッグ出力**: I/Oオーバーヘッドの除去
2. **適応的並列化**: 小規模データでのOpenMPオーバーヘッド回避
3. **最適化されたスケジューリング**: 静的スケジューリングによる効率化

### コア技術
1. **メモリアライメント**
   - キャッシュライン境界（64バイト）に合わせたアライメント
   - 作業バッファの効率的な配置

2. **適応的並列化**
   - 閾値ベースの条件分岐（10,000要素以上で並列化）
   - 静的スケジューリング最適化

3. **疎行列最適化**
   - 非ゼロパターンの事前計算
   - データ構造の再利用
   - 効率的な行列-ベクトル積

## ドキュメント

包括的なドキュメントが利用可能です：

- **開発ガイド**
  - [プロジェクト構造変更とマイグレーション](docs/development/project_restructure_migration.md)
  - [ビルドシステム設定](docs/development/build_configuration.md)

- **トラブルシューティング**
  - [性能回帰問題の分析と解決](docs/troubleshooting/performance_regression_analysis.md)

- **ベンチマーク結果**
  - [性能測定結果](docs/benchmarks/performance_results.md)

## ライセンス
MITライセンス

## 作者
- Hiroki Tsusaka
- IIS, UTokyo
- tsusaka4research "at" gmail.com

```bash
pip install -e .
unicorn-baseline 1.4.4
Baseline inference algorithm for the UNICORN Challenge
Requires Python: >=3.10

# UNICORN Baseline 🦄

Welcome to the official baseline repository for the [UNICORN challenge](https://unicorn.grand-challenge.org/)!<br>
This repository provides reference implementations and tools for tackling a wide range of vision, language, and vision-language tasks in computational pathology and radiology.

[![PyPI version](https://img.shields.io/pypi/v/unicorn-baseline)](https://pypi.org/project/unicorn-baseline/)

This baseline uses the following publicly available foundation models:

- [Virchow](https://huggingface.co/paige-ai/Virchow)  (link to [publication](https://www.nature.com/articles/s41591-024-03141-0))
- [PRISM](https://huggingface.co/paige-ai/Prism) (link to [publication](https://arxiv.org/abs/2405.10254))
- [MRSegmentator](https://github.com/hhaentze/MRSegmentator/tree/master) (link to [publication](https://arxiv.org/pdf/2405.06463))
- [CT-FM: Whole Body Segmetation](https://github.com/project-lighter/CT-FM) (link to [publication](https://arxiv.org/pdf/2501.09001))
- [phi4](https://ollama.com/library/phi4:14b)
- [BioGPT](https://huggingface.co/microsoft/biogpt) (link to [publication](https://arxiv.org/abs/2210.10341))
- [opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl)

## 🚀 Quickstart

System requirements: Linux-based OS (e.g., Ubuntu 22.04) with Python 3.10+ and Docker installed.<br>
We provide scripts to automate the local testing process using public few-shot data from Zenodo.

### 1. Clone the Repository

```bash
git clone https://github.com/DIAGNijmegen/unicorn_baseline.git
cd unicorn_baseline
```
### 2. Download Model Weights

> ⚠️ **Access Required**  
> Some of the models used in the baseline are gated.  
> You need to have **requested and been granted** access to be able to download them from Hugging Face.

```bash
./download_weights.sh
```

### 3. Build the Docker Container

```bash
./do_build.sh
```

### 4. Perform test run(s)

Make sure to always take the **latest** version of the data on Zenodo.

- **Single Task:** Downloads and prepares data for a single task, then runs the docker on one case.
   ```bash
   ./run_task.sh "https://zenodo.org/records/15315589/files/Task01_classifying_he_prostate_biopsies_into_isup_scores.zip"
   ```
- **All Tasks:** Runs the docker on all supported UNICORN tasks, sequentially.
   ```bash
  ./run_all_tasks.sh
   ```
- **Targeted Test Run:** Run the docker on a specific case folder.
   ```bash
  ./do_test_run.sh path/to/case/folder [docker_image_tag]
  ```

### 5. Save the Container for Submission

```bash
./do_save.sh
```

## 📝 Input & Output Interfaces

- **Input:**
  Each task provides a `unicorn-task-description.json` describing the required inputs and metadata. See [example-data/](example-data/README.md) for sample files and structure.
- **Output:**
  The baseline generates standardized output files (e.g., `image-neural-representation.json`, `patch-neural-representation.json`) as required by the challenge.

## 📜 License

This project is licensed under the [Apache License 2.0](LICENSE).
phlow-auth 0.0.5
JWT authentication middleware for AI agents with Supabase integration
Requires Python: >=3.10
Keywords: a2a, agent, authentication, jwt, middleware, phlow, supabase

# Phlow Auth Python

JWT authentication middleware for AI agents with Supabase integration.

## Installation

```bash
pip install phlow-auth
```

## Quick Start

```python
from phlow_auth import PhlowMiddleware, PhlowConfig, AgentCard

# Configure your agent
config = PhlowConfig(
    supabase_url="https://your-project.supabase.co",
    supabase_anon_key="your-anon-key",
    agent_card=AgentCard(
        name="My Agent",
        description="AI assistant agent",
        service_url="https://my-agent.com",
        skills=["chat", "analysis"],
        metadata={"agent_id": "my-agent-id", "public_key": "your-public-key"}
    ),
    private_key="your-private-key"
)

# Initialize middleware
phlow = PhlowMiddleware(config)

# Use as decorator with FastAPI
from fastapi import FastAPI, Request

app = FastAPI()

@app.post("/api/chat")
@phlow.authenticate()
async def chat(request: Request):
    # Access agent context
    agent = request.phlow.agent
    supabase = request.phlow.supabase
    return {"message": f"Hello from {agent.name}"}
```

## Features

- **JWT Authentication** - Verify A2A Protocol JWT tokens
- **Agent Storage** - Store and retrieve agent cards from Supabase
- **RLS Helpers** - Generate basic Row Level Security policies
- **Event Logging** - Track authentication events
- **Multi-Framework** - Works with FastAPI, Flask, and Django

## Development

```bash
# Clone the repository
git clone https://github.com/prassanna-ravishankar/phlow.git
cd phlow/packages/phlow-auth-python

# Install development dependencies
uv sync --extra dev

# Run tests
uv run pytest

# Format code
uv run black src tests
uv run isort src tests

# Type checking
uv run mypy src
```

## Documentation

- 📖 Documentation: https://prassanna.io/phlow/
- 🐛 Issues: https://github.com/prassanna-ravishankar/phlow/issues
- 💬 Discussions: https://github.com/prassanna-ravishankar/phlow/discussions

## License

MIT License - see LICENSE file for details.
flyerapi 1.2.3
Asynchronous api of the Flyer service
Keywords: python, flyer, async, asyncio, cache

<div align="left">
    <h1>FlyerAPI<img src="https://telegra.ph/file/e2a2f0526d2937973a70b.png" width=50 height=50></h1>
    <p align="left" >
        <a href="https://pypi.org/project/flyerapi/">
            <img src="https://img.shields.io/pypi/v/flyerapi?style=flat-square" alt="PyPI">
        </a>
        <a href="https://pypi.org/project/flyerapi/">
            <img src="https://img.shields.io/pypi/dm/flyerapi?style=flat-square" alt="PyPI">
        </a>
    </p>
</div>


## Использование

С помощью ``flyerapi`` вы можете использовать <a href="https://api.flyerservice.io/redoc">FlyerAPI</a> вместе с Telegram<br/>
Документация: https://api.flyerservice.io/redoc

## Установка

```bash
pip install flyerapi
```

## Требования
 - ``Python 3.7+``
 - ``aiohttp``

## Возможности
 - ``Asynchronous``
 - ``Exception handling``



## Пример обязательной подписки с использованием aiogram

```python
from flyerapi import Flyer

from aiogram import types


flyer = Flyer(KEY)

async def message_handler(message: types.Message):
    # Применяйте везде, где требуется проверка
    if not await flyer.check(message.from_user.id, language_code=message.from_user.language_code):
        return

async def callback_handler(call: types.CallbackQuery):
    # Применяйте везде, где требуется проверка
    if not await flyer.check(call.from_user.id, language_code=call.from_user.language_code):
        return
```

### Использование пользовательского сообщения

```python
message = {
    'text': '<b>Пользовательский текст</b> для $name',  # HTML

    'button_bot': 'Запустить',
    'button_channel': 'Подписаться',
    'button_url': 'Перейти',
    'button_boost': 'Голосовать',
    'button_fp': 'Выполнить',
}
await flyer.check(user_id, language_code=language_code, message=message)
```


## Пример для заданий

```python
# Получение заданий для пользователя
tasks = await flyer.get_tasks(user_id=user_id, language_code=language_code, limit=5)

# Получиение статуса задания
signature = tasks[0]['signature']  # пример
status = await flyer.check_task(user_id=user_id, signature=signature)

```


## Пример для использования заданий в обязательной подписки с использованием aiogram

Код вынесен в файл: [`examples/check_with_tasks.py`](examples/check_with_tasks.py)


Developed by Eli (c) 2023-2025
avysignal 0.1.1
This project contains utilities for avalanche detection using seismic and infrasound data.
Requires Python: >=3.10

# `avysignal`

> ## 🚧 Work in progress 🚧

[![PyPI - Version](https://img.shields.io/pypi/v/avysignal.svg)](https://pypi.org/project/avysignal)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/avysignal.svg)](https://pypi.org/project/avysignal)

This project contains utilities for avalanche detection using seismic and infrasound data.

## Installation

Create and activate a virtual environment and then install `avysignal`:

```shell
pip install avysignal
```

Beamforming requires some additional dependencies. You can install them like so:

```shell
pip install "avysignal[beamforming]"
```

## Usage

Download and extract the example data

```shell
./scripts/download_example_data.sh
```

See the [example scripts](examples/signal_processing)

## Contributing

### Project Structure

```
avysignal/
├── archive/                # Legacy workflows (In particular firenze matlab implementation)
├── examples/               # Example data and usage demonstrations
│   ├── data/               # Sample seismic data files (.mseed)
│   └── signal_processing/  # Example processing scripts. Start here!
├── scripts/                # Utility and one-off scripts
├── src/avysignal/          # Main package source code
│   ├── plotting/           # Visualization utilities
│   ├── resources/          # Static metadata
│   │   └── stations/       # XML metadata files for sensor networks
│   ├── signal_processing/  # Signal processing algorithms such as beamforming.
│   └── slf_utils/          # SLF-specific utilities
```
CoreHub 0.1.0
CoreHub - Librería para buscar usuarios de minecraft en nuestras base de datos
Requires Python: >=3.6

# CoreHub
        
        Librería para buscar usuarios mediante nuestra base de datos (módulo finder).
        
        ## Uso
        
        ```python
        from corehub import Finder
        
        finder = Finder()
        resultado = finder.find("hola")
        
        if "error" in resultado:
            print(resultado["error"])
        else:
            print(resultado["results"])