semvec/pypi-packages huggingface dataset:
statute-trees 0.1.5
Tree-based Philippine Codifications, Statutes, and Documents, using a uniform node structure (i.e., leaves of a tree) identified by a given material path.
Requires Python: >=3.11,<4.0

# statute-trees

![Github CI](https://github.com/justmars/statute-trees/actions/workflows/main.yml/badge.svg)

 Tree-based Philippine Codifications, Statutes, and Documents, using a uniform node structure; utilized in the [LawSQL dataset](https://lawsql.com).

## Documentation

See [documentation](https://justmars.github.io/statute-trees).

## Development

Checkout code, create a new virtual environment:

```sh
poetry add statute-trees # python -m pip install statute-trees
poetry update # install dependencies
poetry shell
```

Run tests:

```sh
pytest
```
statute-patterns 0.2.5
Philippine statutory law pattern matching and unit retrieval.
Requires Python: >=3.11,<4.0

# statute-patterns

![Github CI](https://github.com/justmars/statute-patterns/actions/workflows/main.yml/badge.svg)

Philippine statutory law pattern matching and unit retrieval; utilized in [LawSQL dataset](https://lawsql.com).

## Documentation

See [documentation](https://justmars.github.io/statute-patterns).

## Development

Checkout code, create a new virtual environment:

```sh
poetry add statute-patterns # python -m pip install statute-patterns
poetry update # install dependencies
poetry shell
```

Run tests:

```sh
pytest
```
TACTUS-model 1.0.0
Threatening activities classification toward users' security
Requires Python: >=3.8

# TACTUS - model

> Threatening activities classification toward users' security

## Useful ressources

- [Write a better commit message](https://gist.github.com/MarcBresson/dd57a17f2ae60b6cb8688ee64cd7671d)
- [PEP 8 – Style Guide for Python Code](https://peps.python.org/pep-0008/)
GPTerminal 0.1.1
A command line tool that utilizes GPT models to assist with various tasks
Requires Python: >=3.6

# GPTerminal

GPTerminal 是一个基于 GPT 模型的命令行工具，旨在帮助用户解决各种编程问题，提供智能问答和代码执行功能。

## 功能

1. 聊天：与 GPT-3 或 GPT-4 进行智能聊天。
2. Git 命令行助手：根据您的问题提供 Git 命令帮助。
3.代码执行：执行命令并捕获错误输出及其上下文，提供智能解决方案。

## 安装

确保安装 Python 3.6 或更高版本。然后运行：

```bash
pip install GPTerminal
```

## 使用

在命令行中输入以下命令启动 GPTerminal：

```bash
gpterminal
```

然后选择您想要使用的功能。

### 示例

1. 聊天

   ```
   gpterminal chat
   ```

2. Git 命令行助手

   ```
   gpterminal git
   ```

3. 代码执行

   ```
   gpterminal run your-command
   ```


当您在终端中输入 `gpterminal`令时，如果提示 `Command not found`，这可能是因为您的系统未将 `gpterminal` 命令添加到 PATH 环境变量中。在这种情况下，您可以按照以下步骤操作：

### 在 macOS 或 Linux 上

1. 打开终端并输入以下命令：

   ```
   echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
   ```

   这将向您的 `.bashrc` 文件添加一行，该行将 `~/.local/bin` 目录添加到 PATH 环境变量中。

2. 然后，重新加载您的 `.bashrc` 文件：

   ```bash
   source ~/.bashrc
   ```

   现在，您应该能够在终端中使用 `gpterminal` 命令了。

### 在 Windows 上

1. 打开命令提示符并输入以下命令：

   ```batch
   setx PATH "%USERPROFILE%\AppData\Roaming\\PythonXX\Scripts;%PATH%"
   ```

   请注意，将 `PythonXX` 替换为您的 Python 版本号，例如 `Python36` 或 `Python39`。

2. 然后，关闭并重新打开命令提示符，或者注销并重新登录您的 Windows 帐户。

   现在，您应该能够在命令提示符中使用 `gpterminal` 命令了。

## 贡献

欢迎对 GPTerminal 进行贡献！请在提交 Pull Request 之前确保您的代码符合项目的质量标准。

## 许可

GPTerminal 采用 MIT 许可证。有关详细信息，请参阅 [LICENSE](LICENSE) 文件。
pipHack 0.0.1
To Show the vulnerability of the pip
Keywords: python,vulnerability,remoteaccess,sockets

# pipHack
POC of RCE through unsafe pip package

## Usage
You can modify the remote `IP` and `PORT` by yourself.

use `python3 setup.py sdist bdist_wheel` to build the package.

use `twine upload dist/*` to upload the package to pypi.
dreye 1.1.0
Dreye: Color models and stimuli for all model organisms

# DrEye: Exploiting Receptor Space Geometry for Stimulus Design across Animals

[![DOI](https://zenodo.org/badge/243093421.svg)](https://zenodo.org/badge/latestdoi/243093421)

*drEye* is a package that implements various approaches to design stimuli for sensory receptors. The main focus of the package is geared towards designing color stimuli for any animal under investigation, where the photoreceptor spectral sensitivities are known. The hardware-agnostic approach incorporates photoreceptor models within the framework of the principle of univariance. This enables experimenters to identify the most effective way to combine multiple light sources to create desired distributions of light, and thus easily construct relevant stimuli for mapping the color space of an organism. The methods support broad applications in color vision science and provide a framework for uniform stimulus designs across experimental systems. Many of the methods described can be used more generally to design stimuli for other sensory organs or used more broadly where a set of linear filters define the input to a system. 

## Documentationa and tutorials

Documentation and tutorials can be found here <https://dreye.readthedocs.io/en/latest/>.

## Web application

To test stimulus creation, check out the corresponding web applitcation:
<https://share.streamlit.io/gucky92/dreyeapp/main/app.py>.

## Paper

Our paper that explains the purpose of the package *drEye* and goes through key concepts:

*"Exploiting colour space geometry for visual stimulus design across animals"*;
Matthias P. Christenson, S. Navid Mousavi, Elie Oriol, Sarah L. Heath and Rudy Behnia;
Philosophical Transactions of the Royal Society B: Biological Sciences;
<https://royalsocietypublishing.org/doi/10.1098/rstb.2021.0280>.

Please reference this paper when using *drEye*.

## Installation

```bash
pip install dreye
```

In order to use the non-linear fitting procedures, JAX should be installed separately:

```bash
pip install jax[cpu]
```

## Common Issues

* Running jax on the new Macbook Pro chips can run into problems. Make sure to install the versions that work with the M1 chip. For my purposes jaxlib==0.1.60 and jax==0.2.10 currently work (14/01/21).


## Old code

The nonlinear fitting procedures for the variance minimization, underdetermined, and decomposition algorithms and the silent substitution algorithm described in the paper have yet to be refactored into the new API. 
The old API can be found at <https://github.com/gucky92/dreye_ext>.
The linear versions (faster and convex) are already available in the new API.


## Development

If you are interested in contributing to the project, please email at gucky@gucky.eu.
We would also love any type of general feedback or contributions to the code and methods implemented.