semvec/pypi-packages huggingface dataset:
cutmv 0.0.4
small video cut tools
Requires Python: >=3.7

<h1 align="center">cutmv</h1>

<p align="center"><img src="https://img.shields.io/badge/Licence-MIT-green.svg?style=for-the-badge" /></p>

### 项目介绍

调用ffmpeg库实现视频剪辑

该库需要依赖ffmpeg软件。所以在调用该库写代码时，请首先确保您的计算机中已安装ffmpeg并将ffmpeg软件路径加入环境变量。

### 计划目标

* [x] 全格式音频文件分段切割
* [x] 全格式视频文件分段切割
* [x] 全格式音频格式转换
* [x] 全格式视频格式转换

### 项目文件参数说明

* s_time ：音视频文件分割起始时间
* e_time ：音视频文件分割截至时间
* files ：需要切割的文件名
* i_format ：输出格式

### 版本修正

* 2020-12-10 [v0.0.3]：修正了代码中的小错误
* 2020-12-10 [v0.0.4]：追加批量压制视频功能

### 代码演示：
* 调用 cutmv 批量剪辑视频
```python
import os
import cutmv

if __name__ == "__main__":
    rootdir = './'
    list = os.listdir(rootdir)  # 列出文件夹下所有的目录与文件
    for i in range(0, len(list)):
        cutmv.video_cut(0, 480, list[i], '.mp4')
```

* 调用 cutmv 批量压制视频
```python
import cutmv

if __name__ == "__main__":
    cutmv.video_compress('./', './压制/', '600k')
```

### 合作及交流

* 联系邮箱：<gomehome@qq.com>
* QQ ：287000822
envutils 1.0.0
A python library to read and parse environment variables

[![PyPI version](https://badge.fury.io/py/envutils.svg)](https://badge.fury.io/py/envutils) ![Build/Lint/Test](https://github.com/mfilippo/envutils/workflows/Python%20package/badge.svg)

envutils
--------

This python library contains some utils functions to read and parse environment variables.

It can be installed with:
```bash
pip3 install envutils
```

Usage examples:

```python
# Set some example values for demonstration purposes
>>> import os
>>> os.environ['MY_ENV'] = 'my_value'
>>> os.environ['MY_INT_ENV'] = '42'
>>> os.environ['MY_BOOL_ENV'] = 'True'

>>> from envutils import envutils

# Read env variable as string
>>> envutils.get_from_environment('MY_ENV', 'my_default_value')
'my_value'

# Read env variable as int
>>> envutils.get_int_from_environment('MY_INT_ENV', 666)
42

# Read env variable as boolean
>>> envutils.get_bool_from_environment('MY_BOOL_ENV', False)
True

# Read a non-existing env variable
>>> envutils.get_from_environment('UNSET_ENV', 'my_default_value')
'my_default_value'
```
py-scraper 0.0.0
Proxy scraper using requests and bs4. Scrap HTTP/HTTPS/SOCKS proxy. Each proxy is stored with his country.

Proxy scraper
=============================

This package can scrap HTTP/HTTPS/SOCKS proxy from specific country

You can install with pip : 

pip3 install py-scraper


Example : 

    python3 py-scraper.py --https --socks --country US

    OR If you want use it at module 

    import py_scraper

Argument : 

    --socks : Scrap only socks proxy
    --https : Scrap only HTTPS proxy
    --country <??> : Scrap only proxy from specific country (see --countrycode)
    --countrycode : See all code country
    By default py_scraper will scrap HTTP/HTTPS proxy

py-scraper scrap his proxy on : 
    https://free-proxy-list.net/
    https://www.socks-proxy.net/
    https://www.sslproxies.org/
md410-2021-conv-common 1.12.0
Common libraries for applications related to the 2020 and 2021 Lions MD410 Conventions
Requires Python: >=3.7,<4.0

# Introduction

Common libraries for applications related to the 2020 and [2021 Lions Multiple District 410 Conventions](https://www.lionsconvention2021.co.za/).

# Associated Applications

See [this Gitlab group](https://gitlab.com/md410_2021_conv) for associated applications.
SiriusCXRseg 0.0.1
Проект по сегментации легких.
Requires Python: >=3.6.9

# CXRsegmentation
Проект по сегментации легких сделанный в рамках проектной смены Сириуса 'Большие вызовы'. 
Программа модуля разработана Центром ИИ Университета Иннополис. 
### Основная идея
Распознать легкие и отсегментировать их.
### Стек технологий:
Для этого предполагается использование следующий стек технологий:
* > Linux (Ubuntu)
* > Python, pip, numpy, PyTorch, Pathlib и т.д.
* > OpenCV 
* > Machine learning or Deep learning 
* > Jupyter lab / Jupyter Notebook
* > To be added
SiriusCXR 0.0.1
Проект по сегментации легких.
Requires Python: >=3.6.9

# CXRsegmentation
Проект по сегментации легких сделанный в рамках проектной смены Сириуса 'Большие вызовы'. 
Программа модуля разработана Центром ИИ Университета Иннополис. 
### Основная идея
Распознать легкие и отсегментировать их.
### Стек технологий:
Для этого предполагается использование следующий стек технологий:
* > Linux (Ubuntu)
* > Python, pip, numpy, PyTorch, Pathlib и т.д.
* > OpenCV 
* > Machine learning or Deep learning 
* > Jupyter lab / Jupyter Notebook
* > To be added
Sirius-CXRsegmentation 0.0.1
Проект по сегментации легких.
Requires Python: >=3.6.9

# CXRsegmentation
Проект по сегментации легких сделанный в рамках проектной смены Сириуса 'Большие вызовы'. 
Программа модуля разработана Центром ИИ Университета Иннополис. 
### Основная идея
Распознать легкие и отсегментировать их.
### Стек технологий:
Для этого предполагается использование следующий стек технологий:
* > Linux (Ubuntu)
* > Python, pip, numpy, PyTorch, Pathlib и т.д.
* > OpenCV 
* > Machine learning or Deep learning 
* > Jupyter lab / Jupyter Notebook
* > To be added
common-harness-lib 0.0.18
Helper package for H2H Task images.
Requires Python: >=3.6

# common-harness-lib
[H2H Toolkit] Common python library for harness functions within task docker images.


## Usage:
Create object with `CommonHarnessLib()`. To disable s3 features, use the `disable_s3=False` flag.

Methods:
* S3:
  * **handle_s3_exception(err: Exception)**: Handle's common s3 errors.
  * **get_object_content(bucket_name: str, object_key: str)**: Get's a s3 object content as a string.
  * **download_wordlists(wordlist_keys: List[str])**: Downloads a list of wordlists from s3 and combines them as one file.
* System:
  * **run_command(command: List[str])**: Run a command (i.e. ["echo", "hello world"])
* Validation:
  * **validate_dict({"firstname": str, "age": int})**: Validates that a dict matches required type entries
rev-seqs 0.0.1
Revert sequences to consensus
Requires Python: >=3.6

# rev-seqs: Revert Sequences to Consensus Preserving Variants

rev-seqs is a command line utility to revert sequences to consensus preserving variants detected in a VCF file. It reverts all other positions the sample consensus

# Usage
Once installed rev-seqs is just a simple command line command.

```shell
python -m revseqs --input <input file> --vcf <vcf file> > <output_file>
```

run `python revseqs -h` for more options

The new bam file is written to standard output.
Input file must be an indexed bam file

## Inserts
Any inserts that are not flagged as variants are removed, those inserts along with sample header are printed to standard error. The sample name is followed by a comma separated list of insert positions that were removed.

To save this file for future inspect redirect standard error to a file using the command below 

```shell
python -m revseqs --input <input.bam> --vcf <variants.vcf> > <output.bam> 2> <inserts>
```

# Installation

```shell
pip install rev-seqs
```

# Collaboration
If you have any issues, bugs, or feature requests please contact the author, <nhannguyen72089@gmail.com>