semvec/pypi-packages huggingface dataset:
hssp 0.4.18
一个简单快速的异步爬虫框架
Requires Python: >=3.10
Keywords: 异步, 爬虫, 爬虫框架

# HSSP 爬虫框架

[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/x-haose/hssp)

一个基于python asyncio开发的爬虫框架 (开发中)

## 作者

- [@昊色居士](https://github.com/x-haose)

## 特性

- 使用scrapy框架的选择器 [parsel](https://github.com/scrapy/parsel) 作为内置网页选择器
- 基于tenacity的自动异常重试
- 基于fake-useragent的可选随机UA
- 可选的多种下载器: [httpx](https://github.com/encode/httpx)、[aiohttp](https://github.com/aio-libs/aiohttp)、[requests](https://github.com/psf/requests)、[curl-cffi](https://github.com/lexiforest/curl_cffi)、[requests-go](https://github.com/wangluozhe/requests-go)
- 请求前、响应后、重试后监听

## 路线

- 增加其他解析器
- 在情求过程中临时更换下载器：比如net初始化时使用的是httpx下载器，其中一个情求要临时切换至 `DrissionPage`, 其他的依旧是httpx
- 支持 `DrissionPage`、`playwright` 浏览器渲染的下载器
- 下载器支持更多配置项及自定义项
- 编写详细使用文档

## 安装

###

使用 pip 安装 hssp

```bash
pip install hssp
```

###

使用 uv 安装 hssp

```bash
uv add hssp
```

## 支持

如需支持，请发送电子邮件至 xhrtxh@gmail.com。

## 开发测试

项目使用`uv`管理依赖，需先安装uv

```bash
    rye sync
```
iprPy 0.11.8
Interatomic Potential Repository Python Property Calculations and Tools
Keywords: atom, atomic, atomistic, molecular dynamics, high-throughput, interatomic potential

=====
iprPy
=====

Introduction
------------

The iprPy framework provides

- The calculation methodology scripts used by the NIST Interatomic potentials
  Repository for evaluating crystalline and crystal defect materials properties,
- Tools allowing for users to interact with databases and the records contained
  within to easily explore the results of the calculations, and
- Workflow tools that allow for preparing and performing high throughput runs
  of the implemented calculation methods.

The design of the package aims for being user-friendly, open and transparent at
all levels.  To this end

- All code is open source,
- Calculation documentation and the Python code can be easily accessed and
  explored,
- Calculations can be performed individually or *en masse* using the workflow
  tools,
- Command line options allow for runs to be set up and performed with limited
  or no Python knowledge,
- Calculations are modular meaning that new methods can be easily added,
- Calculation methodology is separated from the workflow operations as much as
  possible,
- Implementation of new calculations can be supported by sharing input/output
  terms with existing calculations, and
- The results records are in a format that is both human and machine readable. 


Documentation
-------------

- Documentation can be found in the doc directory or by visiting 
  https://www.ctcms.nist.gov/potentials/iprPy/. 

- The notebook directory contains Jupyter Notebooks that outline how the
  implemented calculation methods work and provides an example run.

- For help using the package, feel free to contact potentials@nist.gov or
  submit an issue or pull request to the https://github.com/lmhale99/iprPy
  github repository.
audioop-lts 0.2.2
LTS Port of Python audioop
Requires Python: >=3.13

# audioop

An LTS port of the Python builtin module `audioop` which was deprecated since version 3.11 and removed in 3.13.

This project exists to maintain this module for future versions.

## Using this project

> [!WARNING]
**This module only functions at Python versions of 3.13 or greater** due to being removed in this version.

As such, you can conditionally add this project to your dependencies:

#### pip-requirements
```
audioop-lts; python_version>='3.13'
```

#### Poetry-pyproject
```toml
[tool.poetry.dependencies]
audioop-lts = { version = "...", python = "^3.13" }
```
Relevant documentation is [here](https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies), or alternatively use [`markers`](https://python-poetry.org/docs/dependency-specification/#using-environment-markers)

#### pdm-pyproject / uv-pyproject / hatch-pyproject
```toml
[project]
dependencies = [
    "audioop-lts; python_version >= '3.13'",
]
```

#### Pipenv-pipfile
```toml
[packages]
audioop-lts = { version = "...", markers = "python_version >= 3.13" }
```
dharsh-parrot 0.1.0
Animated Dharsh Parrot for your terminal 🦜
Requires Python: >=3.7

# 🦜 terminal-parrot

Bring the legendary **Party Parrot** to your terminal!  
This animated parrot will dance its way across your screen and brighten your CLI mood. 🎉🐦

---

## ✨ Features

- 🕺 Terminal animation of the Party Parrot
- ⚙️ Adjustable speed and loop count
- 📁 Support for custom animation files
- 🧪 Cross-platform (macOS, Linux, Windows)

---

## 📦 Installation

Install directly from [PyPI](https://pypi.org/project/terminal-parrot/):

```bash
pip install terminal-parrot
```

After installing, run
```bash
parrot
```

### 🔧 Command-line Options

Flag	Description	Default
--delay	Delay between frames (in seconds)	0.1
--loops	Number of times to loop animation	-1 (infinite loop)
--animation	Path to custom .animation file	animations/parrot.animation

### 🧪 Example

parrot --delay 0.05 --loops 3

---
golding-NEURON 0.1.13
Golding Lab NEURON project library
Requires Python: >=3.8

# Golding Lab's NEURON Tools

A package of tools and files from the Golding Lab at UT Austin for compartmental modeling using the simulation software, [NEURON](https://www.neuron.yale.edu/neuron/).

## Features

- Pre-configured scripts for compartmental modeling.
- Neurolucida cell morphology upload
- Tools for generating, simulating and analyzing neuronal activity.
- User interfaces for Neuroscience Gateway and simulation generation
- Utilizes with the NEURON simulation environment.
- Developed and maintained by the Golding Lab.

## Getting Started

### Prerequisites

- Install [NEURON](https://www.neuron.yale.edu/neuron/download) on your system.
- Ensure Python 3.8+ is installed.


### Installation

Install using pip:
```bash
pip install golding_NEURON
```

and compile the NEURON mechanisms:
```bash
compile-mechs
```

*or*
----
Clone the repository:
```bash
git clone https://github.com/jaredcasarez/golding-NEURON.git
cd golding-NEURON
```
        
and compile the NEURON mechanisms:
```bash
python -m golding_NEURON.compile_mechs
```

## Resources

- [Golding Lab Website](https://goldinglab.org)
- [NEURON Documentation](https://www.neuron.yale.edu/neuron/)
- [GitHub Repository](https://github.com/jaredcasarez/golding-NEURON)

## Acknowledgments

This project is developed and maintained by the [Golding Lab](https://goldinglab.org) at UT Austin. Special thanks to all contributors and the NEURON development team.

## License

This project is licensed under the MIT License. See the LICENSE file for details.
edc-visit-tracking-app 0.1.1
Test app for clinicedc/edc projects
Requires Python: <3.14,>=3.12
Keywords: clinical, clinicedc, collection, data, django, edc, trials, visit

|pypi| |downloads| |clinicedc|

edc-visit-tracking-app
======================

Test app for clinicedc projects

.. |pypi| image:: https://img.shields.io/pypi/v/edc-visit-tracking-app.svg
    :target: https://pypi.python.org/pypi/edc-visit-tracking-app

.. |downloads| image:: https://pepy.tech/badge/edc-visit-tracking-app
   :target: https://pepy.tech/project/edc-visit-tracking-app

.. |clinicedc| image:: https://img.shields.io/badge/framework-Clinic_EDC-green
   :alt:Made with clinicedc
   :target: https://github.com/clinicedc
vaulter 1.0.0
機密情報を自動暗号化するPythonライブラリ
Requires Python: >=3.7
Keywords: security, encryption, secrets, vault, cryptography

# vaulter

機密情報（APIキー、パスワード、トークン等）を扱う変数を自動的に暗号化し、メモリ上でも平文を保持しないPythonライブラリです。

## 特徴

- **自動暗号化／自動復号**（アクセス時のみ一時復号）
- **メモリ上での平文保持を最小化**
- **AES-GCMによる強固な暗号化**
- **シリアライズ対応**（ファイル保存・DB保存時も暗号化状態維持）
- **Zeroization**（破棄時にメモリクリア）

## インストール

```bash
pip install vaulter
```

## 使用例

### 基本例

```python
from vaulter import Vault

# 暗号化されたVaultオブジェクトを作成
secret = Vault("API_KEY", value="sk_live_abc123")

# 文字列として取得（復号は内部で一時的に行われる）
print(secret.get())  # "sk_live_abc123"

# 値を更新
secret.set("new_api_key_456")

# JSONシリアライズ時も暗号化状態を維持
encrypted_data = secret.to_json()

# 復元
restored = Vault.from_json(encrypted_data)
print(restored.get())  # "new_api_key_456"
```

### グローバルキーの使用

```python
from vaulter import Vault, generate_key

# プロセス全体で共有するキーを設定
global_key = generate_key()
Vault.set_global_key(global_key)

# すべてのVaultインスタンスが同じキーを使用
vault1 = Vault("SECRET1", value="value1")
vault2 = Vault("SECRET2", value="value2")

# グローバルキーをクリア
Vault.clear_global_key()
```

### カスタムキーの使用

```python
from vaulter import Vault, generate_key

# 独自のキーを生成
custom_key = generate_key()

# カスタムキーでVaultを作成
vault = Vault("CUSTOM_SECRET", value="secret_value", key=custom_key)
```

## API仕様

### クラス: Vault

#### コンストラクタ

```python
Vault(name: str, value: Optional[str] = None, key: Optional[bytes] = None)
```

**引数**
- `name`: 識別名（例: "API_KEY"）
- `value`: 初期値（任意）
- `key`: AESキー（指定しない場合は内部で自動生成）

**動作**
- `value` が指定されていれば即時暗号化して保持
- 暗号鍵はメモリ上で安全に管理

#### メソッド

##### `set(value: str) -> None`
平文の値を暗号化して保存
- 保存後、平文を即座にメモリから破棄

##### `get() -> str`
一時的に復号して返す
- 呼び出し終了後、平文データを即座にゼロ化

##### `to_json() -> str`
暗号化データとメタ情報をJSON文字列で出力

例：
```json
{
  "name": "API_KEY",
  "ciphertext": "b64encodeddata",
  "nonce": "b64nonce",
  "tag": "b64tag"
}
```

##### `from_json(json_str: str) -> Vault`
JSONから復元してVaultインスタンスを生成

##### `wipe() -> None`
内部メモリを完全クリア（鍵・データ）

#### クラスメソッド

##### `Vault.generate_key() -> bytes`
安全なランダムAESキーを生成

##### `Vault.set_global_key(key: bytes)`
全Vaultインスタンスで共有するグローバルキーを設定

##### `Vault.clear_global_key()`
グローバルキーをクリア

## 内部仕様

### 暗号方式
- **AES-256-GCM**
- 鍵長：256bit
- IV（Nonce）：96bitランダム
- 認証タグ付与による改ざん検出

### メモリ保護
- 復号後は即座に `bytearray` をゼロ化
- Python GCに任せず明示的クリアを実行

### シリアライズ
- 暗号化データ・Nonce・タグをBase64でエンコード
- JSON・Pickleなどに対応

### エラーハンドリング
- 復号失敗時は `VaultDecryptionError` を送出
- 不正フォーマット時は `VaultFormatError` を送出
- キー関連エラー時は `VaultKeyError` を送出

## セキュリティ注意事項

1. **平文は `.get()` 呼び出し時のみメモリ上に存在**
2. **`.get()` の結果を別変数に保持すると安全性が損なわれるため、使用後即破棄すること**
3. **長期保管する場合は外部KMSまたは安全なストレージと併用すること**

## ライセンス

MIT