semvec/pypi-packages huggingface dataset:
meadowrun 0.2.16
The easiest way to run python code on one or more remote machines
Requires Python: >=3.7,<4.0

# ![Meadowrun](meadowrun-logo-full.svg)

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/meadowrun) ![PyPI](https://img.shields.io/pypi/v/meadowrun)  ![PyPI - Downloads](https://img.shields.io/pypi/dm/meadowrun) ![Conda](https://img.shields.io/conda/v/meadowdata/meadowrun) ![Conda](https://img.shields.io/conda/dn/meadowdata/meadowrun?label=conda%20downloads)

[![Join the chat at https://gitter.im/meadowdata/meadowrun](https://badges.gitter.im/meadowdata/meadowrun.svg)](https://gitter.im/meadowdata/meadowrun?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)



Meadowrun is a library for data scientists and data engineers who run python code on
AWS, Azure, or Kubernetes. Meadowrun:

- scales from a single function to thousands of distributed tasks.
- syncs your local code and libraries for a faster, easier iteration loop. Edit your
  code and rerun your analysis without worrying about building packages or Docker
  images.
- optimizes for cost, choosing the cheapest instance types and turning them off when
  they're no longer needed.
  
For more context, see our [case
studies](https://docs.meadowrun.io/en/stable/case_studies/) of how Meadowrun is used in
real life, or see the [project homepage](https://meadowrun.io)

To get started, go to our [documentation](https://docs.meadowrun.io), or [join the chat
on Gitter](https://gitter.im/meadowdata/meadowrun)

## Quickstart

First, install Meadowrun using pip:

```
pip install meadowrun
```

Next, assuming you've [configured the AWS
CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-quickstart.html)
and are a root/administrator user, you can run:

```python
import meadowrun
import asyncio

print(
    asyncio.run(
        meadowrun.run_function(
            lambda: sum(range(1000)) / 1000,
            meadowrun.AllocEC2Instance(),
            meadowrun.Resources(logical_cpu=1, memory_gb=8, max_eviction_rate=80),
            meadowrun.Deployment.mirror_local()
        )
    )
)
```

[The documentation](https://docs.meadowrun.io) has examples of how to use other package
managers (conda, poetry), and other platforms (Azure, GKE, Kubernetes).
algo-beast 0.1.7
Fyers Api Builder

## Fyers Token Generator

```
from fyers_api_builder import FyersApiBuilder
```

```
config = {
  "username": "<USERNAME>",
  "password": "<PASSWORD>",
  "pin": "<PIN>",
  "client_id": "<CLIENT_ID>",
  "secret_key": "<SECRET_KEY>",
  "redirect_uri": <REDIRECT_URL>
}
```

#### Initialization

```
from fyers_api import accessToken, fyersModel
from fyers_api.Websocket import ws

fyersApiBuilder = FyersApiBuilder(config=config, accessToken=accessToken, fyersModel=fyersModel, ws=ws)
```

#### HTTP Client

- fyersApiBuilder.client.get_profile()
- fyersApiBuilder.client.history(payload)

#### WebSocket Client

- fyersApiBuilder.ws_client.subscribe(payload)
odoo-rest-framework 1.2.0
Store user access token for one-time-login

### Odoo REST Framework

    Version : 1.2.0

### Package :

    pip3 install odoo-rest-framework

###### package link :

    https://pypi.org/project/odoo-rest-framework/

###### github link :

    https://github.com/HanZawNyein/odoo-rest-framework.git

###### Author :

    Han Zaw Nyein

###### Author_email:

    hanzawnyineonline@gmail.com

### Description :

    Store user access token for one-time-login

# Controller Code Example

    from odoo import http
    from .odoo_rest_framework import (
        login_required,
        jwt_http,
        fields_extractor,
        read_serializer
    )
    from odoo.http import request


    class Testing(http.Controller):
        @login_required(route='/api/testing/', csrf=False, methods=['GET'])
        def testing(self, limit=5, offset=1, *args, **kwargs):
            model_res_partner = request.env['res.partner']
            # all_fields = fields_extractor(model_res_partner)
            all_partner = model_res_partner.search_read([], fields=['id','name'], limit=limit, offset=offset)
            all_partner = read_serializer(all_partner)
            return jwt_http.response(data=all_partner, message='Holidays')


# Response Example
    {
        "success": true,
        "message": "Holidays",
        "data": [
            {
                "id": 37,
                "name": "Brandon Freeman"
            },
            {
                "id": 44,
                "name": "Colleen Diaz"
            }
        ]
    }
gerdison-imagem-processo 0.0.1
Pacote de Processamento de imagens
Requires Python: >=3.8

 RepositÃ³rio com desenvolvimento dos cÃ³digos, e arquivos, para entrega do Projeto do Curso de Python para Cientistas de dados: CriaÃ§Ã£o de Pacotes de Processamento de Imagens; da plataforma [DIO - Digital Innovation One](https://web.dio.me/).  
#
# Processamento de Imagens

DescriÃ§Ã£o: 

O pacote '**processamento_de_imagem**' pode ser usado para:

	Processamento:
		- CorrespondÃªncia de Histograma;
		- Similaridade Estrutural;
		- Redimensionamento de Tamanhos
	
	UtilitÃ¡rios:
		- Leitor de Imagem;
		- Salvador de Imagem;
		- Exibidor de Imagens;
		- Exibidor de Resultados;
		- Exibidor de Histograma

## InstalaÃ§Ã£o:

Use o gerenciador de pacotes [pip](https://pip.pypa.io/en/stable/) para instalaÃ§Ã£o do pacote '**processamento_de_imagem**':

```bash
pip install processamento_de_imagens
```

## Uso:

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```
 RepositÃ³rio com desenvolvimento dos cÃ³digos, e arquivos, para entrega do Projeto do Curso de Python para Cientistas de dados: CriaÃ§Ã£o de Pacotes de Processamento de Imagens; da plataforma [DIO - Digital Innovation One](https://web.dio.me/).  
#
# Processamento de Imagens

DescriÃ§Ã£o: 

O pacote '**processamento_de_imagem**' pode ser usado para:

	Processamento:
		- CorrespondÃªncia de Histograma;
		- Similaridade Estrutural;
		- Redimensionamento de Tamanhos
	
	UtilitÃ¡rios:
		- Leitor de Imagem;
		- Salvador de Imagem;
		- Exibidor de Imagens;
		- Exibidor de Resultados;
		- Exibidor de Histograma

## InstalaÃ§Ã£o:

Use o gerenciador de pacotes [pip](https://pip.pypa.io/en/stable/) para instalaÃ§Ã£o do pacote '**processamento_de_imagem**':

```bash
pip install processamento_de_imagens
```

## Uso:

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```

## Autor:
Esse Projeto: Gerdison

## License
[MIT](https://choosealicense.com/licenses/mit/)