semvec/pypi-packages huggingface dataset:
petstore-sdk 6.6.6
this is pet description.
Requires Python: >=3.7
Keywords: this,is,testing


# Getting Started with Swagger Petstore

## Introduction

This is a sample server Petstore server.  You can find out more about Swagger at [http://swagger.io](http://swagger.io) or on [irc.freenode.net, #swagger](http://swagger.io/irc/).  For this sample, you can use the api key `special-key` to test the authorization filters.

Find out more about Swagger: [http://swagger.io](http://swagger.io)

## Install the Package

The package is compatible with Python versions `3 >=3.7, <= 3.11`.
Install the package from PyPi using the following pip command:

```python
pip install petstore-sdk==6.6.6
```

You can also view the package at:
https://pypi.python.org/pypi/petstore-sdk/6.6.6

## Initialize the API Client

**_Note:_** Documentation for the client can be found [here.](https://www.github.com/SYed-Subrta/test/tree/6.6.6/doc/client.md)

The following parameters are configurable for the API Client:

| Parameter | Type | Description |
|  --- | --- | --- |
| `environment` | Environment | The API environment. <br> **Default: `Environment.PRODUCTION`** |
| `http_client_instance` | `HttpClient` | The Http Client passed from the sdk user for making requests |
| `override_http_client_configuration` | `bool` | The value which determines to override properties of the passed Http Client from the sdk user |
| `http_call_back` | `HttpCallBack` | The callback value that is invoked before and after an HTTP call is made to an endpoint |
| `timeout` | `float` | The value to use for connection timeout. <br> **Default: 60** |
| `max_retries` | `int` | The number of times to retry an endpoint call if it fails. <br> **Default: 0** |
| `backoff_factor` | `float` | A backoff factor to apply between attempts after the second try. <br> **Default: 2** |
| `retry_statuses` | `Array of int` | The http statuses on which retry is to be done. <br> **Default: [408, 413, 429, 500, 502, 503, 504, 521, 522, 524]** |
| `retry_methods` | `Array of string` | The http methods on which retry is to be done. <br> **Default: ['GET', 'PUT']** |
| `password` | `string` |  |

The API client can be initialized as follows:

```python
from swaggerpetstore.swaggerpetstore_client import SwaggerpetstoreClient
from swaggerpetstore.configuration import Environment

client = SwaggerpetstoreClient(
    password='Password'
)
```

## Authorization

This API uses `Custom Authentication`.

## List of APIs

* [Pet](https://www.github.com/SYed-Subrta/test/tree/6.6.6/doc/controllers/pet.md)
* [Store](https://www.github.com/SYed-Subrta/test/tree/6.6.6/doc/controllers/store.md)
* [User](https://www.github.com/SYed-Subrta/test/tree/6.6.6/doc/controllers/user.md)

## Classes Documentation

* [Utility Classes](https://www.github.com/SYed-Subrta/test/tree/6.6.6/doc/utility-classes.md)
* [HttpResponse](https://www.github.com/SYed-Subrta/test/tree/6.6.6/doc/http-response.md)
* [HttpRequest](https://www.github.com/SYed-Subrta/test/tree/6.6.6/doc/http-request.md)
glemos 0.0.1
A small example package
Requires Python: >=3.7

# Example Package

[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Coverage](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=coverage)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python) 
[![Code Smells](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=code_smells)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=sqale_rating)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Bugs](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=bugs)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Vulnerabilities](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=vulnerabilities)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Duplicated Lines (%)](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=duplicated_lines_density)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)
[![Technical Debt](https://sonarcloud.io/api/project_badges/measure?project=guilhermedelemos_glemos-python&metric=sqale_index)](https://sonarcloud.io/summary/new_code?id=guilhermedelemos_glemos-python)

This is a simple example package.
co2ncientes 0.0.2
Una herramienta de detección de elementos peculiares
Keywords: python,CODEFEST


# CO2nscientes CODEFEST 2023

## Problema de detección de objetos

### Librerías necesarias

Para el desarrollo de este reto, la libreria core con la que se realizó la detección de objetos fue tensorflow

(Instalar las librerias requeridas en el requirements.txt)

### Proceso de trabajo

Primero hubo una etapa de creación de un modelo de detección de objetos:
- Basados en los videos que nos entregaron, con la herramienta v2 segmentamos el video en frames
- Los frames del video fueron examinados por una persona que se encargo de anotar cuando encontrara algo de interés (carros, barcos, zonas deforestadas, etc)
- Se creó un diccionario de imágenes, anotando elementos de interés con la herramienta labelImg.py
- tensorflow generó un modelo que podía identificar objetos en un frame

Luego hubo una segunda etapa que es la de creación de la herramienta
- Se registra cada 60 frames una imágen
- La imágen es pasada por una función que primero trata de identificar la hora y las coordenadas usando easyocr
- Luego la misma imágen es pasada por un proceso de detección de objetos usando el modelo anteriormente creado
- Finalmente si algún objeto es detectado en la imágen, esta pasa a guardarse y a ser registrado el valor en el csv

Imágen de una máquinaria identificada

![deteccion](assets/detección.png)
python-strategies-breezeconnect 4.0
breeze connect strategies in python
Requires Python: >=3.6

# UAT  python_strategies

## Steps to install Strategy Library for UAT


```python

pip install python_strategies_breezeconnect==4.0

```


## code usage

```python

from python_strategies import Strategies

async def main():
    obj = Strategies(app_key = "your app key",secret_key = "your secret key",api_session = "your api session",max_profit = "your max profit",max_loss = "your max loss")
    await obj.long_straddle(stock_code = "NIFTY",strike_price = "18700",qty = "50",expiry_date = "2023-06-15T06:00:00.000Z",exchange_code = "NFO")

await main()

```
yatrash 0.1.3
Safely move files into a trash can instead of `rm`.
Requires Python: >=3.6.0

# yatrash
Safely move files into a trash can instead of `rm`.

OS Independent, as long as your OS supports Python's `pathlib` and `shutil`.

Now living on [Github][5]

## Detailed usage

```
$ trash --help
usage: __main__.py [-h] [--trash-dir TRASH_DIR]
                   [trash_files [trash_files ...]]

...
see the real --help for more
```

## Why do we need another trash utility?

This is mostly for self-educational purposes.

If you actually want a trash utility, see [trash-cli][1] which
implements the [xdg trash specification][2], so it will play nicely
with your Desktop's trash and other tools. It also features more
utilities, such as recover.

## Origin

- [CodeReview][3]
- [Github Gist][4]
- [Github Repository][4] (current home)

[1]: https://pypi.org/project/trash-cli/
[2]: http://www.ramendik.ru/docs/trashspec.html
[3]: https://gist.github.com/charmoniumQ/0c5fe34dbb5b3905440a3b06c4f60634
[4]: https://codereview.stackexchange.com/questions/188842/safely-trash-instead-of-rm
[5]: https://github.com/charmoniumQ/yatrash/
cellulose-sdk 0.0.3
Cellulose Python SDK
Requires Python: >=3.10, <4
Keywords: artificial intelligence,benchmarking,debugging,development,onnx,profiler,profiling,pytorch,tensorflow

# Cellulose Python SDK

Welcome to Cellulose!
We build tools designed to help ML engineers understand, fine tune and improve
inference performance of their models.

By using this Python SDK, you can upload and visualize your PyTorch models in
our dashboard.

Please refer to our official documentation [here](https://docs.cellulose.ai)
for installation and more usage details.
dvadmin-celery 1.0.5
适用于 django-vue-admin 的celery异步插件
Requires Python: >=3.7, <4

# dvadmin_celery

#### 介绍
dvadmin-celery 插件是集成 django-celery-beat、tenant-schemas-celery、django-redis、django-celery-results 的一个后端插件，
安装可快速使用异步任务，包含在线添加任务、启停任务、查看任务记录等功能。<br>
与之相对应的是 dvadmin-celery-web 前端插件

## 安装包

使用pip安装软件包：

```python
pip install dvadmin-celery
```

目录结构：<br>
```
dvadmin-celery
|   dvdadmin_celery
|   |   fixtures
|   |   |   __init__.py
|   |   |   init_menu.json
|   |   |   initialize.py
|   |   views
|   |   |   __init__.py
|   |   |   crontab_schedule.py
|   |   |   interval_schedule.py
|   |   |   periodic_task.py
|   |   |   task.py
|   |   |   task_detail.py
|   |   __init__.py
|   |   admin.py
|   |   apps.py
|   |   settings.py
|   |   tasks.py
|   |   urls.py
|   setup.py
```

### 方式一: 一键导入注册配置
在 application / settings.py 插件配置中下导入默认配置
```python
...
from dvadmin_celery.settings import *
```
### 方式二: 手动配置
在INSTALLED_APPS 中注册app（注意先后顺序）

```python
INSTALLED_APPS = [
    ...
    'django_celery_beat',
    'django_celery_results',
    'dvadmin_celery',
]
```

在 application / urls.py 中注册url地址

```python
urlpatterns = [
    ...
    path(r'api/dvadmin_celery/', include('dvadmin_celery.urls')),
]
```

如果没有系统redis，请启动redis并添加配置 (./conf/env.example.py 及 ./conf/env.py中添加如下配置)

```python
# redis 配置
REDIS_PASSWORD = '' # 如果没密码就为空
REDIS_HOST = '127.0.0.1'
REDIS_URL = f'redis://:{REDIS_PASSWORD or ""}@{REDIS_HOST}:6379'

```

在 application / settings.py 下添加配置

```python
...
CACHES = { # 配置缓存
    "default": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": f'{REDIS_URL}/1', # 库名可自选1~16
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
        }
    },
}
BROKER_URL = f'{REDIS_URL}/2' # 库名可自选1~16
CELERY_RESULT_BACKEND = 'django-db' # celery结果存储到数据库中
CELERYBEAT_SCHEDULER = 'django_celery_beat.schedulers.DatabaseScheduler'  # Backend数据库
```

进行迁移及初始化
```python
python3 manage.py makemigrations 
python3 manage.py migrate 
# 注意备份初始化信息
python3 manage.py init -y 
```

其他配置请参考 django_celery_beat 和 celery 文档

#### 使用说明

``` python
mac/linux:
celery -A application.celery worker -B --loglevel=info

win:
需要安装: pip install eventlet,需要启动两个程序（worker + beat 顺序不分先后）
celery -A application.celery worker -P eventlet --loglevel=info
celery -A application.celery beat --loglevel=info
```

#### 注意
``` python
如果 celery worker 报错 KeyError,可尝试在django_vue_admin/application/celery.py文件里将
app = Celery(f"application")
改为
app = Celery(f"application", include=['dvadmin_celery.tasks'])
再重启尝试
```
redis下载地址：<br>
Mac/Linux下载 http://download.redis.io/releases/ <br>
Windows下载 https://github.com/tporadowski/redis/releases/ <br>