semvec/pypi-packages huggingface dataset:
py-rpautom 0.0.55b0
Conjunto de utilitários para automação de processos.
Requires Python: >=3.9

# Py-RPAutom

Py-RPAutom é uma biblioteca que nasceu para facilitar o desenvolvimento de robôs automatizados (RPA), uma vez que precisamos fazer vários códigos repetitivos para determinadas ações. Tem como objetivo facilitar a criação de processos automatizados via código. Criamos uma breve documentação explicando como utilizar recursos para automação web, desktop e recursos do sistema como cópia de arquivos ou criação de pastas. A documentação pode ser conferida [aqui](https://aranseiki.github.io/py-rpautom/).

Ainda, a biblioteca é feita originalmente em português para aumentar a inclusão da população brasileira. Mesmo que o inglês seja o idioma predominantes na área de tecnologia, essa não é a realidade da nossa população. Se o objetivo dessa biblioteca é facilitar o desenvolvimento de aplicações, não faz sentido criar uma barreira idiomática.

Tendo origem em 2019, a biblioteca foi desenvolvida de modo separado cada módulo para facilitação no desenvolvimento de projetos pessoais, mas incorporados rapidamenta ao desenvolvimento profissional. Em 2022, foi a consolidação dos três módulos básicos até então (web, desktop e utilitários do sistema), unificando-os e criando assim a sua primeira versão pública.

Essa biblioteca usa ferramentas populares para automação e testes como Selenium, pywinauto, openpyxl e pywin32.
gradupe 3.0.5
Sobel Gradient Image Deduplication
Requires Python: >=3.11
Keywords: image-dedupe, sobel-gradient

<h3 align="center">
    <img src="https://raw.githubusercontent.com/wavim/gradupe/master/.media/icon.png" width="140" alt="GraDupe Icon" /><br />
    GraDupe
</h3>
<p align="center">Sobel Gradient Image Deduplication</p>

### Usage

Get the CLI tool with `pip install -U gradupe`, or retrieve from PyPI manually.
It is recommended to run the CLI with admin privilege.

`gradupe init` initializes cache in the current directory for long-term
management.  
`gradupe scan` scans the current directory for duplicates, utilizing cache if
available.

For further information and options, refer to `gradupe` and
`gradupe [command] --help`. Cache is stored in the `.gradupe` SQLite database in
the current directory if enabled.

### Motive

Classical algorithms based on image hashes can be inaccurate. Innovative ones
based on RNNs can be inefficient. As the demand for image storage increases
rapidly over the decade, we need a prompt solution that combines the benefits of
both.

At one point, Sobel gradients occurred to me as a decent fingerprint for an
image. Similar to finite differences and derivatives, two distinct images bear
the same gradient only if they differ by a constant. By reading an image in
grayscale, we obtain a 2D matrix suitable for Sobel operators.

Images of different dimensions are downscaled into a square grid. Although
convolutions are blazingly fast on modern hardware, this is done to unify
dimensions and speed up diffing. After downscaling, there remains a sufficient
amount of informative bits for diffing in the next step.

Sobel operators are traditionally used for edge detection, but their nature lies
in differentiating an image. Computing the Sobel gradient of an image in both
the x and y directions yields two matrices, which we flatten and concatenate
into a contiguous array.

The gradients are thresholded into bitmasks since Hamming distance can be
optimized using SIMD XOR instructions, making it magnitudes faster than
Euclidean norm. By mapping sub-indices of pairs into combinatorial indices, a
densely packed array can be used as a distance matrix, saving memory and
enabling parallel computation.

The single flat distance array can be thresholded into a boolean mask with SIMD
instructions. All that remains is to compress the image combinations with the
mask (combinatorial indexing ensures correct correspondence), resulting in a
list of duplicate pairs which is then merged into groups via union find.

### Credits

Library  
[OpenCV](https://opencv.org), [NumPy](https://numpy.org),
[Numba](https://numba.pydata.org)

Cache  
[SQLite](https://sqlite.org)

CLI  
[Typer](https://github.com/fastapi/typer), [Rich](https://github.com/Textualize/rich)
datazone 0.0.18
Datazone Client Package
Requires Python: <4.0,>=3.9

Datazone Client Library 👋

## Installation

You can install package via pip

```shell
pip install datazone
```

## Configuration & Login

Run the following command in your terminal to create a new profile.

```shell
datazone profile create
```

When prompted, enter the following information:

```shell
Profile Name: <PROFILE_NAME>
Host [app.datazone.co]: <DATAZONE_ADDRESS>
API Key: <API Key>
```

Then you can run following command to test it. (At the first profile creation, it will test automatically)

```shell
datazone auth test
```

## Create first repository

```shell
datazone repository create hello-world
cd hello-world
```

It creates a new folder in your current directory. Thıs folder has two file initially. `config.yaml` and `first_pipeline.py`.
After you modify your script you can deploy project via following command.

```shell
datazone repository deploy
```

After the deployment is complete, you can check your repository status via following command.

```shell
datazone repository summary
```

If repository has deployed, you can run your first execution with following command.

```shell
datazone execution run <pipeline_id>
```

## Commands

Also you can check all command with help sub command. `datazone auth --help` or `datazone repository summary --help`

```shell
# Auth
datazone auth test

# Profile
datazone profile list
datazone profile create
datazone profile delete <profile_name>
datazone profile setdefault <profile_name>

# Repository
datazone repository list
datazone repository create
datazone repository deploy
datazone repository summary <file_name>
datazone repository clone <repository_id>
datazone repository pull

# Dataset
datazone dataset list
datazone dataset show <dataset_id> [--size <n>] [--transaction-id <id>] [--query "<SQL>"]
datazone dataset transactions <dataset_id>

# Source
datazone source create
datazone source list
datazone source update <source_id>
datazone source delete <source_id>

# Extract
datazone extract create
datazone extract list
datazone extract update <extract_id>
datazone extract delete <extract_id>
datazone extract execute <extract_id>

# Schedule
datazone schedule create
datazone schedule list
datazone schedule delete <source_id>

# Execution
datazone execution run [--extract-id] [--pipeline-id] [<execution_type>] [<transform_selection>]
datazone execution list [--extract-id] [--pipeline-id]
datazone execution log <execution_id>

# Pipeline
datazone pipeline create
datazone pipeline list
datazone pipeline delete <pipeline_id>

# View
datazone view create
datazone view list [--dataset-id <dataset_id>]
datazone view delete <view_id>

# SQL
datazone sql <dataset_id> "<QUERY>" [--transaction-id <id>] [--size <n>]

# Project
datazone project activities list <project_id>

# Common
datazone version
datazone info
```
atmql 0.1.0
Advanced Topic Modeling Query Language with rich terminal visualization
Requires Python: >=3.7

# ATMQL - Advanced Topic Modeling Query Language

ATMQL is a powerful Python library for topic modeling with an advanced query language interface. It provides comprehensive analysis capabilities, rich terminal visualizations, and secure authentication.

## Features

- **Advanced Query Language**: Multiple commands to analyze topic models
- **Rich Metrics**: Coherence, diversity, quality scores, and more
- **Terminal Visualization**: Custom charts and plots in the terminal
- **Secure Authentication**: Email-based verification system
- **Multiple Model Support**: LDA (scikit-learn) and Gensim implementations

## Installation

```bash
pip install atmql