semvec/pypi-packages huggingface dataset:
wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt4 2023.2.15.0
吾爱破解移动安全区 2019.12~2020.6 PT4
Requires Python: >=3.6

# 吾爱破解移动安全区 2019.12~2020.6 PT4

## 下载

### Docker

```
docker pull apachecn0/wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt4
docker run -tid -p <port>:80 apachecn0/wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt4
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt4
wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt4 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g wuai-pojie-yidong-anquanqu-erlingyijiu-yier-erlingerling-6-pt4
wuai-pojie-yidong-anquanqu-erlingyijiu-yier-erlingerling-6-pt4 <port>
# 访问 http://localhost:{port} 查看文档
```
wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt1 2023.2.15.0
吾爱破解移动安全区 2019.12~2020.6 PT1
Requires Python: >=3.6

# 吾爱破解移动安全区 2019.12~2020.6 PT1

## 下载

### Docker

```
docker pull apachecn0/wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt1
docker run -tid -p <port>:80 apachecn0/wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt1
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt1
wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt1 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g wuai-pojie-yidong-anquanqu-erlingyijiu-yier-erlingerling-6-pt1
wuai-pojie-yidong-anquanqu-erlingyijiu-yier-erlingerling-6-pt1 <port>
# 访问 http://localhost:{port} 查看文档
```
wuai-pojie-yidong-anquanqu-2011-6-2014-12 2023.2.15.0
吾爱破解移动安全区 2011.6~2014.12
Requires Python: >=3.6

# 吾爱破解移动安全区 2011.6~2014.12

## 下载

### Docker

```
docker pull apachecn0/wuai-pojie-yidong-anquanqu-2011-6-2014-12
docker run -tid -p <port>:80 apachecn0/wuai-pojie-yidong-anquanqu-2011-6-2014-12
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install wuai-pojie-yidong-anquanqu-2011-6-2014-12
wuai-pojie-yidong-anquanqu-2011-6-2014-12 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g wuai-pojie-yidong-anquanqu-erlingyiyi-6-erlingyisi-yier
wuai-pojie-yidong-anquanqu-erlingyiyi-6-erlingyisi-yier <port>
# 访问 http://localhost:{port} 查看文档
```
wuai-pojie-ruanjian-diaoshi-qu-2009-8-2023-1-pt2 2023.2.15.0
吾爱破解软件调试区 2009.8~2023.1 PT2
Requires Python: >=3.6

# 吾爱破解软件调试区 2009.8~2023.1 PT2

## 下载

### Docker

```
docker pull apachecn0/wuai-pojie-ruanjian-diaoshi-qu-2009-8-2023-1-pt2
docker run -tid -p <port>:80 apachecn0/wuai-pojie-ruanjian-diaoshi-qu-2009-8-2023-1-pt2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install wuai-pojie-ruanjian-diaoshi-qu-2009-8-2023-1-pt2
wuai-pojie-ruanjian-diaoshi-qu-2009-8-2023-1-pt2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g wuai-pojie-ruanjian-diaoshi-qu-erlinglingjiu-8-erlingersan-1-pt2
wuai-pojie-ruanjian-diaoshi-qu-erlinglingjiu-8-erlingersan-1-pt2 <port>
# 访问 http://localhost:{port} 查看文档
```
wuai-pojie-bingdu-fenxi-qu-2008-10-2023-1 2023.2.15.0
吾爱破解病毒分析区 2008.10~2023.1
Requires Python: >=3.6

# 吾爱破解病毒分析区 2008.10~2023.1

## 下载

### Docker

```
docker pull apachecn0/wuai-pojie-bingdu-fenxi-qu-2008-10-2023-1
docker run -tid -p <port>:80 apachecn0/wuai-pojie-bingdu-fenxi-qu-2008-10-2023-1
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install wuai-pojie-bingdu-fenxi-qu-2008-10-2023-1
wuai-pojie-bingdu-fenxi-qu-2008-10-2023-1 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g wuai-pojie-bingdu-fenxi-qu-erlinglingba-yiling-erlingersan-1
wuai-pojie-bingdu-fenxi-qu-erlinglingba-yiling-erlingersan-1 <port>
# 访问 http://localhost:{port} 查看文档
```
mmkg-mm-hoi-detection 0.1.3
MMKG Models
Requires Python: >=3.7

# 人-物交互关系检测使用说明

## 1. 算法描述
该算法用于人-物交互关系检测，检测图像内部细粒度人-物三元组列表。该算法基于PyTorch框架开发，输入文本与图像，输出人-物三元组关系列表及概率。

## 2. 依赖及安装

CUDA版本: 11.3
其他依赖库的安装命令如下：

```bash
conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.3
```

可使用如下命令下载安装算法包：
```bash
pip install -U mmkg-mm-hoi-detection
```

## 3. 使用示例及运行参数说明

```python
from mmkg_mm_hoi_detection import MMHOIDetection
from PIL import Image
ckpt_path =  '***.pth'
model = MMHOIDetection(ckpt_path)
img_path = '***.jpg'
img = Image.open(img_path)
res,_ = model.inference(img,output_path='')
```
## 4. 论文
```
@article{liao2022gen,
  title={GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection},
  author={Liao, Yue and Zhang, Aixi and Lu, Miao and Wang, Yongliang and Li, Xiaobo and Liu, Si},
  journal={arXiv preprint arXiv:2203.13954},
  year={2022}
}
```
ganna01 0.0.1
Generative Algorithm for N-Doped Novel Aromatics
Requires Python: >=3.7

# Generative Algorithm for N-Doped Novel Aromatics

This algorithm can be used to generate nitrogen-doped polycyclic aromatic compounds whose geometry is optimised using the MMFF94s force field. The application of this algorithm is very straightforward to understand. The users are required to create a folder with the aromatic structures that they want to be doped with nitrogen atoms, specify the number of nitrogen atoms that they want in the structure, and provide the folder on which the MMFF94s optimised structures should be saved.  Please contact the author via email with any questions or concerns about the code (stiv.llenga@h-its.org).

## Setting the input variables:

Since the purpose of the algorithm is for everybody to use it, the amount of input needed to make it function properly is kept to a minimum. Users are required to provide the following information: 

INPUT:
   
* --inp_dir -> (Str) The directory that houses all of the PAH xyz files;
   
* --out_dir -> (Str) The directory where the N-doped PAH must be saved;
   
* --hetero_nr -> (Int) The total amount of dopants (Nitrogen atoms);

* --iters -> (Int) The maximum iteration value of the force field.
GANNA01 0.0.1
Generative Algorithm for N-Doped Novel Aromatics
Requires Python: >=3.7

# Generative Algorithm for N-Doped Novel Aromatics

This algorithm can be used to generate nitrogen-doped polycyclic aromatic compounds whose geometry is optimised using the MMFF94s force field. The application of this algorithm is very straightforward to understand. The users are required to create a folder with the aromatic structures that they want to be doped with nitrogen atoms, specify the number of nitrogen atoms that they want in the structure, and provide the folder on which the MMFF94s optimised structures should be saved.  Please contact the author via email with any questions or concerns about the code (stiv.llenga@h-its.org).

## Setting the input variables:

Since the purpose of the algorithm is for everybody to use it, the amount of input needed to make it function properly is kept to a minimum. Users are required to provide the following information: 

INPUT:
   
* --inp_dir -> (Str) The directory that houses all of the PAH xyz files;
   
* --out_dir -> (Str) The directory where the N-doped PAH must be saved;
   
* --hetero_nr -> (Int) The total amount of dopants (Nitrogen atoms);

* --iters -> (Int) The maximum iteration value of the force field.
GANNA-N 0.0.1
Generative Algorithm for N-Doped Novel Aromatics
Requires Python: >=3.7

# Generative Algorithm for N-Doped Novel Aromatics

This algorithm can be used to generate nitrogen-doped polycyclic aromatic compounds whose geometry is optimised using the MMFF94s force field. The application of this algorithm is very straightforward to understand. The users are required to create a folder with the aromatic structures that they want to be doped with nitrogen atoms, specify the number of nitrogen atoms that they want in the structure, and provide the folder on which the MMFF94s optimised structures should be saved.  Please contact the author via email with any questions or concerns about the code (stiv.llenga@h-its.org).

## Setting the input variables:

Since the purpose of the algorithm is for everybody to use it, the amount of input needed to make it function properly is kept to a minimum. Users are required to provide the following information: 

INPUT:
   
* --inp_dir -> (Str) The directory that houses all of the PAH xyz files;
   
* --out_dir -> (Str) The directory where the N-doped PAH must be saved;
   
* --hetero_nr -> (Int) The total amount of dopants (Nitrogen atoms);

* --iters -> (Int) The maximum iteration value of the force field.
cfsp 0.1.17
The cloudFPGA Support Package
Requires Python: >=3.6

# cFSP

This is the **cloudFPGA Support Package (cFSP)** for the [cloudFPGA project](https://www.zurich.ibm.com/cci/cloudFPGA/).

## Requirements

Python 3.6+


## Installation

You can install `cfsp` system-wide through `pip install cfsp`, or user-wide through `pip install -u cfsp`.

It is advised though to do it in an isolated Python environment, e.g. through [virtualenv](https://virtualenv.pypa.io/):

```
virtualenv -p /usr/bin/python3.6 cfenv
source cfenv/bin/activate
pip install --upgrade pip
pip install cfsp
```

## Usage

Please check the [online documentation](https://cloudfpga.github.io/Doc/pages/CFSPHERE/cfsp.html).

## Development
```bash
git clone git@github.com:cloudFPGA/cFSP.git
cd cFSP
make env
source venv/bin/activate
<add your changes>  (do not forget to increase the `version="x.y.z"` in `setup.py`)
git commit -am "My changes for version x.y.z"
git push
make dist
make upload
```
meter-data-dynamodb 0.3.0
Script to read and modify meter-data-dynamodb entries
Requires Python: >=3.10,<4.0

# Get started

## Configure AWS credentials
Set up aws credentials if you haven't done it already.

Here's instructions: https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html

In short, you need to add a file called `credentials` to `~/.aws/` (C:\Users\USERNAME\.aws\credentials on Windows) to  with the following content:

```
[default]
aws_access_key_id = <your access key>
aws_secret_access_key = <your secret key>
```

and this file to `~/.aws/config` (C:\Users\USERNAME\.aws\config on Windows) to set the region:

```
[default]
region=eu-north-1
output = json

[profile taksi-helsinki-development]
region = eu-north-1

[profile taksi-helsinki-shared]
region = eu-north-1

[profile taksi-helsinki-production]
region = eu-north-1

```

## Option 1: Install using pipx

Install pipx if you haven't done it already:

```
pip install pipx
```

Get credetials from gitlab and install the package:

```
pipx install git+https://gitlab.com/taksi-helsinki/meter-data-dynamodb.git
```


## Option 2: Use poetry

Install poetry if you haven't done it already:

```
pip install poetry
```

Install required packages:

```
poetry install
```

And run the script:

```
poetry run python meter_data_dynamodb/meter_data.py --help

```
django-jsoneditor 0.2.4
Django JSON Editor

Django-JSONEditor
===================

Django-JSONEditor is an online structured JSON input widget for Django appropriate for various JSONField's provided for Django.

Code of the javascript JSONEditor online editor has been got from the http://jsoneditoronline.org/.

See the latest versions of the javascript online JSON Editor here: https://github.com/josdejong/jsoneditor

Sample views:

.. image:: https://raw.github.com/josdejong/jsoneditor/master/misc/jsoneditor.png

Installation
------------
Latest version from the GIT repository::

    pip install "git+git://github.com/nnseva/django-jsoneditor.git"

Stable version from the PyPi repository::

    pip install django-jsoneditor

See all details on https://github.com/nnseva/django-jsoneditor
pyclvm 1.1.12b0
Command Line Utility to connect or redirect ports to a Cloud Virtual Machine
Requires Python: >=3.8

# <div align="center"><b>CLVM</b></div>

[![Downloads](https://static.pepy.tech/personalized-badge/pyclvm?period=total&units=none&left_color=orange&right_color=blue&left_text=Downloads)](https://pepy.tech/project/pyclvm)
[![](https://img.shields.io/pypi/v/pyclvm?label=PyPi)](https://pypi.org/project/pyclvm/)
[![](https://img.shields.io/github/license/BstLabs/py-clvm?color=blue&label=License)](https://github.com/BstLabs/py-clvm/blob/main/LICENSE.md)
[![Changelog](https://github.com/BstLabs/py-clvm/workflows/Changelog/badge.svg)](https://github.com/BstLabs/py-clvm/blob/main/CHANGELOG.md)
[![Lint](https://github.com/BstLabs/py-clvm/actions/workflows/lint.yml/badge.svg)](https://github.com/BstLabs/py-clvm/actions/workflows/lint.yml)
[![Type](https://github.com/BstLabs/py-clvm/actions/workflows/type.yml/badge.svg)](https://github.com/BstLabs/py-clvm/actions/workflows/type.yml)


A Cloud VM command line tool for powerful and efficient management of cloud instances

This tool addresses the inefficient hassle of having to use your mouse to click lots of buttons to start/stop instances and configure and use these cloud services via a web interface. This is an unproductive way to work. If you use VSCode Remote or port redirection to access your cloud instance resources, then the steps are doubled.

To improve the development experience, we have replicated various solutions for handling different steps of using cloud virtual machines and combined them with VSCode Remote to deliver maximum convenience for fellow developers and engineers.

CLVM currently works with cloud instances on AWS, Azure and GCP. It is built on top of [DynaCLI](https://github.com/BstLabs/py-dynacli), another excellent open-source tool from [BST Labs](https://github.com/BstLabs/). For more detail on using CLVM, see the blog post [Simplify Cloud Instance Management from your Command Line](https://medium.com/@orkhanshirin/simplify-cloud-instance-management-from-your-command-line-4b44a5b3949e).

## Capabilities of CLVM

1. Instance start/stop and listing operations
2. SSH key generation and tunneling
3. Session management
4. Port redirection (forwarding)
5. VSCode Remote utilities
6. Support for AWS, GCP, and, Azure

## Installation

`$ pip3 install pyclvm`

## Quick start

*Each cloud platform can require different authorization procedures. For detailed authorization information please check the respective provider's documentation.*

```console
$ clvm ssh new <vm_instance_name> platform=<aws, gcp or azure>
```
```console
$ clvm vscode start <vm_instance_name>
```

## How to install for local development

`$ flit install --symlink`

This will give you `clvm` command installed.

## Some Useful Tools

[ssh-over-ssm](https://github.com/elpy1/ssh-over-ssm)

[ssm-tools](https://github.com/mludvig/aws-ssm-tools)

[vscode remote over ssm](https://github.com/aws/aws-toolkit-vscode/issues/941)

[aws-mfa](https://github.com/broamski/aws-mfa)
vishal-makwana-Factorial-of-number-project 1.0.0
An sample project which implemnts the factorial functions.
Requires Python: >=3.7
Keywords: vishal,Factorial-of-number-project

This module is created for the demo purpose, which will return a factorial of a given number
For a full description, how to create package
[project page](https://packaging.python.org/en/latest/tutorials/packaging-projects/).