semvec/pypi-packages huggingface dataset:
constitutions 0.1.0
Add your description here
Requires Python: >=3.11

# constitutions

Initial PyPI package scaffold for `constitutions`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
currents 0.1.0
Add your description here
Requires Python: >=3.11

# currents

Initial PyPI package scaffold for `currents`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
tenures 0.1.0
Add your description here
Requires Python: >=3.11

# tenures

Initial PyPI package scaffold for `tenures`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
constituencies 0.1.0
Add your description here
Requires Python: >=3.11

# constituencies

Initial PyPI package scaffold for `constituencies`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
reputations 0.1.0
Add your description here
Requires Python: >=3.11

# reputations

Initial PyPI package scaffold for `reputations`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
ledgers 0.1.0
Add your description here
Requires Python: >=3.11

# ledgers

Initial PyPI package scaffold for `ledgers`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
unknowns 0.1.0
Add your description here
Requires Python: >=3.11

# unknowns

Initial PyPI package scaffold for `unknowns`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
assemblies 0.1.0
Add your description here
Requires Python: >=3.11

# assemblies

Initial PyPI package scaffold for `assemblies`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
modellock 0.2.1
AI 模型加密保护与授权系统
Requires Python: <3.14,>=3.10

# ModelLock - AI 模型加密保护与授权系统

ModelLock 是一个用于加密和授权机器学习模型的工具。它提供了基于密码的文件加密功能，以及基于设备指纹的许可证管理系统，确保只有授权设备才能解密和使用加密的模型。

## 功能特性

- 基于密码的文件加密（使用 AES-GCM 算法）
- 设备指纹识别（跨平台支持）
- 许可证管理（带有效期控制）
- 命令行界面（CLI）便于集成和使用
- 安全的密钥派生（使用 PBKDF2）
- Python API 接口便于程序集成
- 支持直接提供主密钥进行解密（适用于特殊场景）
- 支持创建无设备绑定的许可证
- 增加`from model.lock import lock `支持，用于程序快速加密

## 快速开始

### 安装

```bash
pip install modellock
```

或从源码安装：

```bash
git clone https://github.com/yaqiangsun/modellock.git
cd modellock
pip install -e .
```

### 使用流程示例

1. **准备阶段**
   ```bash
   # 生成服务器密钥（**必须妥善保管生成文件且不能分享给他人**）
   bash ./scripts/run0_generate_server_secret.sh
   
   # 生成密钥文件与master_key文件（**必须妥善保管生成文件且不能分享给他人**）
   bash ./scripts/run1_generate_keyfile.sh
   
   # 加密模型文件（**加密后的文件可以分享给授权使用**）
   bash ./scripts/run2_encrypt_file.sh
   
   # 收集目标设备信息 (**不限制使用**)
   bash ./scripts/run3_collect_device.sh
   
   # 为目标设备创建许可证（**生成文件可分享给授权使用**）
   bash ./scripts/run4_create_license.sh
   ```
   上述生成的文件 **只将** 加密后的模型文件、许可证文件分发给授权使用。

2. **部署和使用**
   ```bash
   # 在目标设备上解密模型 (**为保证模型安全，只能在内存中解密，且解密代码需编译以保证源码不可见**)
   ./scripts/run5_decrypt_file.sh
   ```
   解密形式包含两种：1.提供密钥文件与密码，自动生成master_key内容解密；2.直接提供master_key解密

所有中间文件和输出文件都会保存在 `tmp/` 目录下。

## 重要安全提示

在使用 ModelLock 过程中会生成多个敏感文件，这些文件或内容**必须妥善保管且不能分享给他人**：
- `password` - 密码，用户自定义的密码
- `server_secret.key` - 服务器密钥文件，用于许可证加密（[步骤0生成服务密钥](scripts/run0_generate_server_secret.sh)中生成的内容）
- `keyfile.json` - 密钥文件，包含加密相关信息([步骤1生成密钥种子](scripts/run1_generate_keyfile.sh)中生成的内容)

这些文件或内容能够授权使用**可分享给授权的使用者**：
- `license.json` - 许可证文件，用于解密已加密的模型([步骤4生成用户授权license](scripts/run4_create_license.sh)中生成的内容)

请务必遵循此安全提示，防止未授权访问。
preprints 0.1.0
Add your description here
Requires Python: >=3.11

# preprints

Initial PyPI package scaffold for `preprints`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
keynotes 0.1.0
Add your description here
Requires Python: >=3.11

# keynotes

Initial PyPI package scaffold for `keynotes`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
lineage-simulator 0.1.2
A Single-Cell Lineage Simulator with Fate-Aware Gene Expression
Requires Python: >=3.11

# LineageSim: A Single-Cell Lineage Simulator with Fate-Aware Gene Expression

A simulator for single-cell lineage tracing data that generates fate-aware gene expression — progenitor cells carry transcriptomic signatures of their descendants' fates, capturing a key property of real developmental data.

## QuickStart

Requires Python >= 3.11.

#### Install as a package and import directly in your code:

```bash
# Using pip
pip install lineage-simulator

# Using uv
uv add lineage-simulator
```

Then in your code

```python
from lineagesim.simulator import LineageSim

sim = LineageSim()
tree, expression = sim.generate_dataset(
    n_cells=8192,
    n_genes=1000,
    beta=0.2,
)
# tree: dict mapping cell IDs to metadata (fate, parent, depth, etc.)
# expression: (n_cells, n_genes) array of observed counts
```

#### For development:

```bash
uv sync

```

## CLI Usage

```bash
# See all available options
uv run lineagesim generate --help

# Generate with default parameters
uv run lineagesim generate -o dataset.h5ad

# An example showing full control of simulation parameters
uv run lineagesim generate \
    --n-cells 4096 --n-genes 500 --n-cif 32 \
    --beta 0.3 --sigma 0.5 --commit-depth 7 \
    --gene-effect-prob 0.3 --scale-s 1.0 \
    --protocol UMI --skip-technical-noise \
    --seed 42 -o dataset.h5ad
```

## Parameters

`generate_dataset()` accepts the following parameters:

| Parameter              | Type    | Default | Description                                           |
| ---------------------- | ------- | ------- | ----------------------------------------------------- |
| `n_cells`              | `int`   | 8192    | Number of cells to simulate                           |
| `n_genes`              | `int`   | 1000    | Number of genes to simulate                           |
| `n_CIF`                | `int`   | 32      | Number of Cell Identity Factors (latent dimensions)   |
| `sigma`                | `float` | 0.2     | Brownian motion noise std dev                         |
| `beta`                 | `float` | 0.2     | Fate signal strength                                  |
| `gene_effect_prob`     | `float` | 0.3     | Probability that a CIF affects a gene                 |
| `scale_s`              | `float` | 1.0     | Transcription rate scaling factor                     |
| `protocol`             | `str`   | `"UMI"` | Sequencing protocol (`"UMI"` or `"nonUMI"`)           |
| `skip_technical_noise` | `bool`  | `False` | If `True`, return true counts without technical noise |
| `commit_depth`         | `int`   | 7       | Depth at which fate commitment occurs                 |

## Contribution Guide

PRs are welcome!

## License

MIT
accomplishments 0.1.0
Utilities for working with accomplishments.
Requires Python: >=3.10

# accomplishments

Initial PyPI package scaffold for `accomplishments`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
findings 0.1.0
Utilities for working with findings.
Requires Python: >=3.10

# findings

Initial PyPI package scaffold for `findings`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
intuitions 0.1.0
Utilities for working with intuitions.
Requires Python: >=3.10

# intuitions

Initial PyPI package scaffold for `intuitions`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
vsrife 5.7.0
RIFE function for VapourSynth
Requires Python: >=3.10
Keywords: PyTorch, RIFE, TensorRT, VapourSynth

# RIFE
Real-Time Intermediate Flow Estimation for Video Frame Interpolation, based on https://github.com/hzwer/Practical-RIFE.


## Dependencies
- [PyTorch](https://pytorch.org/get-started/) 2.10.0 or later
- [VapourSynth](http://www.vapoursynth.com/) R69 or later
- [vs-miscfilters-obsolete](https://github.com/vapoursynth/vs-miscfilters-obsolete) (only needed for scene change detection)

`trt` requires additional packages:
- [TensorRT](https://developer.nvidia.com/tensorrt) 10.14.1 or later
- [Torch-TensorRT](https://pytorch.org/TensorRT/) 2.10.0 or later

To install the latest stable version of PyTorch and Torch-TensorRT, run:
```
pip install -U packaging setuptools wheel
pip install -U torch torchvision torch_tensorrt --extra-index-url https://download.pytorch.org/whl/cu130 --extra-index-url https://pypi.nvidia.com
```


## Installation
```
pip install -U vsrife
```

If you want to download all models at once, run `python -m vsrife`. If you prefer to only download the model you
specified at first run, set `auto_download=True` in `rife()`.


## Usage
```python
from vsrife import rife

ret = rife(clip)
```

See `__init__.py` for the description of the parameters.
Tooldelta 1.3.3
Plugin Loader for NeteaseRentalServer on Panel
Requires Python: <3.13,>=3.10

<h1 align="center">ToolDelta - Bot Plugin Loader</h1>

<p align="center">
  <a href="https://github.com/ToolDelta/ToolDelta/releases"><img src="https://img.shields.io/github/v/release/ToolDelta/ToolDelta?display_name=tag&sort=semver" alt="Releases"></a>
  <img src="https://img.shields.io/github/stars/ToolDelta/ToolDelta.svg?style=falt" alt="Stars">
</p>

**ToolDelta** 是**为《我的世界：中国版》手机端租赁服**制作的、基于机器人的插件加载器。

ToolDelta 可以运行在**多种游戏交互启动器核心**上， 包括但不限于：
   - ~~FastBuilder~~
   - NeOmega接入点
   - NeOmega启动器
   - Eulogist赞颂者
   - FateArk接入点  （目前公开可用）
   - TanGame本地联机接入点 （目前公开可用）

ToolDelta 的插件可以极大幅提高您的租赁服的玩法上限和优化租赁服的流畅度。


## 目录
- [目录](#目录)
- [插件市场](#插件市场)
- [注意事项](#注意事项)
- [相关网站和交流群](#相关网站和交流群)
- [运行和配置](#运行和配置)
- [打包 Docker 镜像](#打包-docker-镜像)
- [使用已打包的 Docker 镜像](#使用已打包的-docker-镜像)



## 插件市场
- ToolDelta 的插件市场在 [这里](https://github.com/ToolDelta-Basic/PluginMarket)



## 注意事项
- 源码运行时，ToolDelta 仅能运行在 Python 3.10+ 版本上


## 相关网站和交流群
- [ToolDelta 官站](https://tooldelta.top)
- [ToolDelta 百科及用户指南](https://wiki.tooldelta.top)
- [ToolDelta 官方技术交流群](https://qm.qq.com/q/3JtUTHzZwY) 准入等级为 QQ 16 级（一个太阳）
- [ToolDelta 第三方社区交流群](https://qm.qq.com/q/6J79yelYNq) 无准入门槛




## 运行和配置
- 直接下载 ToolDelta 可执行文件并运行
   - [发行版 ToolDelta 下载点这里](https://github.com/ToolDelta/ToolDelta/releases)
- 在 `pip` 安装 ToolDelta 并运行
   ```sh
   pip install tooldelta
   echo import tooldelta;tooldelta.client_title() > main.py
   ```
- 运行:
   ```sh
   python3 main.py
   ```
   或
   ```
   python main.py
   ```

## 打包 Docker 镜像
在项目目录下运行命令：
```sh
docker build -t tooldelta .
```

## 使用已打包的 Docker 镜像
运行命令：
```sh
sudo docker pull crpi-6pmrt6su7uwffyo4.cn-shanghai.personal.cr.aliyuncs.com/tooldelta/tooldelta:latest
```
如果您需要将 ToolDelta 运行在 MCSM 中，在 **应用实例设置>容器化** 中选择镜像名 **tooldelta:latest** 即可。
zeitgeists 0.1.0
Utilities for working with zeitgeists.
Requires Python: >=3.10

# zeitgeists

Initial PyPI package scaffold for `zeitgeists`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```