semvec/pypi-packages huggingface dataset:
grepy-tool 0.1.0
An enhanced grep-like tool
Requires Python: >=3.6

# Grepy

Grepy is an enhanced grep-like tool built in Python that extends the functionality of traditional grep with additional features for more efficient and powerful text searching.

## Features

- **All standard grep functionality**: Case-insensitive search, recursive search, invert matching, line numbers, word boundaries, etc.
- **Enhanced display**: Highlighted matches, colored output for easy reading
- **Performance options**: Parallel processing for faster searches on multiple files
- **Advanced search**: File filtering with include/exclude patterns, directory exclusion
- **Customizable output**: Sort results, limit results count, show column numbers
- **Detailed information**: Summary statistics, verbose mode for debugging
- **Portable**: Works on any platform that supports Python 3.6+

## Installation

```bash
pip install grepy-tool
```

## Usage

Basic usage:

```bash
grepy "pattern" file.txt
```

Search in all files in current directory:

```bash
grepy "pattern" .
```

Recursive search with highlighting:

```bash
grepy "pattern" --recursive --highlight .
```

Case-insensitive search with line numbers:

```bash
grepy -i -n "pattern" file.txt
```

Advanced example:

```bash
grepy "function" --recursive --highlight --summary --include="*.py" --exclude="*test*" .
```

## Options

### Standard grep options:
- `-i, --ignore-case`: Case insensitive search
- `-r, --recursive`: Search directories recursively
- `-v, --invert-match`: Show lines that do not match
- `-n, --line-numbers`: Show line numbers
- `-w, --word`: Match whole words only
- `-m, --max-count NUM`: Stop after NUM matches

### Enhanced features:
- `-p, --parallel`: Use parallel processing for faster searches
- `--workers NUM`: Number of worker threads for parallel processing
- `--highlight`: Highlight matching text
- `--no-color`: Disable colored output
- `--sort`: Sort results by filename
- `--column`: Show column number of match
- `--first-only`: Only show first match per file
- `--summary`: Show summary of matches
- `--multiline`: Enable multiline regex matching
- `--include GLOB`: Only search files matching GLOB pattern
- `--exclude GLOB`: Skip files matching GLOB pattern
- `--exclude-dir GLOB`: Skip directories matching GLOB pattern
- `--filename`: Always show filename
- `--verbose`: Enable verbose output

## Python API

Grepy can also be used as a Python library:

```python
from grepy.grepy import Grepy

# Create a grepy instance
grepy = Grepy()

# Search with options
matches = grepy.search(
    "pattern",
    ["file.txt", "directory"],
    {
        "recursive": True,
        "ignore_case": True,
        "highlight": True
    }
)

# Process results
for match in matches:
    print(f"{match.filename}:{match.line_number}: {match.line}")
```

## License

MIT License
longer-limbs 1.0a1
Wrapper for SciKit-learn tree-based estimators providing linear regression fallback for inputs outside of training data range.
Requires Python: >=3.10

# longer-limbs
Wrapper for SciKit-learn tree-based estimators providing linear regression fallback for inputs outside of training data range.

## Instructions

Install longer-limbs with:

```bash
pip install longer-limbs
```

Longer-limbs wraps SciKit-learn's `GradientBoostingRegressor()`. It offers identical `.fit()` and `.predict()` methods. To adapt code which currently uses pure SciKit-learn, change the import of `GradientBoostingRegressor()` from:

```python
from sklearn.ensemble import GradientBoostingRegressor
```

to:

```python
from longer_limbs.regressors import GradientBoostingRegressor
```

## Usage

See the [example regression notebook](https://github.com/gperdrizet/longer-limbs/blob/main/examples/regression.ipynb) for usage demonstration and comparison to SciKit-learn.
flake8-vedro-allure-id 0.1.0
Flake8 plugin to enforce @allure.id() decorator for Vedro Scenario classes
Requires Python: >=3.8

# flake8-vedro-allure-id

[![Tests](https://github.com/your-org/flake8-vedro-allure-id/actions/workflows/tests.yml/badge.svg)](https://github.com/your-org/flake8-vedro-allure-id/actions/workflows/tests.yml)
[![PyPI version](https://badge.fury.io/py/flake8-vedro-allure-id.svg)](https://badge.fury.io/py/flake8-vedro-allure-id)
[![Python versions](https://img.shields.io/pypi/pyversions/flake8-vedro-allure-id.svg)](https://pypi.org/project/flake8-vedro-allure-id/)

Flake8 плагин для проверки наличия декоратора `@allure.id()` для классов Scenario в тестах на базе Vedro.

## Установка

```bash
pip install flake8-vedro-allure-id
```

Или из исходников:

```bash
pip install -e .
```

## Использование

После установки плагин автоматически интегрируется с flake8. Для запуска проверки выполните:

```bash
flake8 path/to/your/tests
```

Для проверки только ошибок, генерируемых этим плагином:

```bash
flake8 path/to/your/tests --select=UGC
```

## Проверки

Плагин выполняет следующие проверки:

- **UGC100**: Класс Scenario должен иметь декоратор `@allure.id()`. Этот код ошибки возникает, когда импорт `allure` присутствует, но декоратор `@allure.id()` отсутствует.
- **UGC101**: Импортируйте `allure` и добавьте декоратор `@allure.id()` для класса Scenario. Этот код ошибки возникает, когда нет ни импорта `allure`, ни декоратора.

## Примеры

### Правильный код

```python
import allure
import vedro

@allure.id(12345)
class TestSomething(vedro.Scenario):
    pass
```

### Неправильный код

```python
import allure
import vedro

# Не указан декоратор @allure.id()
class TestSomething(vedro.Scenario):
    pass
```

```python
import vedro

# Нет ни импорта allure, ни декоратора @allure.id()
class TestSomething(vedro.Scenario):
    pass
```

## Разработка

### Установка для разработки

```bash
git clone https://github.com/your-org/flake8-vedro-allure-id.git
cd flake8-vedro-allure-id
pip install -e .
```

### Запуск тестов

```bash
pytest
```

## Публикация новой версии

1. Обновите версию в файле `flake8_vedro_allure_id_plugin/__init__.py`
2. Создайте новый тег в формате `vX.Y.Z`
3. Опубликуйте тег на GitHub
4. GitHub Actions автоматически опубликует пакет в PyPI, используя Trusted Publishing

### Настройка Trusted Publishing для PyPI

Для публикации пакета в PyPI через GitHub Actions мы используем метод Trusted Publishing:

1. Перейдите в настройки вашего проекта на PyPI: https://pypi.org/manage/account/publishing/
2. Создайте новую конфигурацию Trusted Publishing с использованием GitHub Actions
3. Укажите имя репозитория, рабочий процесс (workflow) и триггер (ref pattern: `refs/tags/v*`)
4. Workflow уже настроен с разрешением `id-token: write` для работы Trusted Publishing

## Лицензия

MIT
materialized-intelligence 0.1.11
Materialized Intelligence SDK
Requires Python: >=3.10

# materialized-client

The official Python client for Materialized Intelligence. See [docs.materialized.dev](https://docs.materialized.dev/) for more information.

## Installing Locally (to test changes during development)

Run `make install` from the root directory. This should remove the old builds and reinstall the package in your environment with the latest. You can run `uv pip list` to ensure the package is pointing at the local files instead of the PyPI package.

## Creating releases

Make sure you increment the version appropriately in `pyproject.toml`. Generally speaking we'll do patch versions for small tweaks, minor versions for large additions or changes to behavior, and probably do major releases once it makes sense. Since we're still in beta and `0.x.x` releases, its probably okay to add backwards-incompatible changes to minor releases, but we want to avoid this if possible. 

To create a release, run: 

`make release <version>` with `<version>` formatted like `0.1.1`

It'll prompt you for an API key to PyPI, which you must have for it to work. 

We also have a test PyPI account which you can use to test creating releases before pushing to the actual PyPI hub. I believe you can only create **one** release per version number, so it may be worth testing if you're paranoid about getting it right.

Also make sure to update the docs and increment the docs version number to match the new release. Keeping these consistent will provide a better user experience.
plct-cli 0.0.5
Command line interface for working with Sphinx projects.

# PLCT CLI App

This Command-Line Interface (CLI) app, named PLCT (Petlja Learning Content Tools), provides a set of commands to streamline the management and generation of learning content using Sphinx.

## Installation

1. Clone the repository:

    ```bash
    pip install plct-cli
    ```

## Commands

### `build`

Generate learning content using `sphinx-build`. You have the option to pass specific options to Sphinx using the `-so` flag. If no options are passed, they will be deduced or read from the `plct_config` file.

```bash
plct build [-so <sphinx-options>] [-sf <sphinx-files>]
```

- `-so`, `--sphinx-options`: Specify additional options for Sphinx-build.
- `-sf`, `--sphinx-files`: Specify filenames for Sphinx-build.

### `preview`

Starts a local server using `sphinx-autobuild`. The root of this server corresponds to the root of the generated content. This allows you to edit files in the source directory and see the changes reflected in real-time on the server, as it will automatically refresh the content.

```bash
plct preview [-so <sphinx-options>]
```

- `-so`, `--sphinx-options`: Specify additional options for Sphinx-autobuild.

### `publish`

Publish learning content. Provides an easy way to create doc folder that can be used as the root for git hub pages.

```bash
plct publish
```

### `clean`

Clean the generated output directory.

```bash
plct clean
```

### `get_markdown`

Command zips all markdown files from the source directory.

```bash
plct get_markdown
```

## Configuration

The app tries to determine the command arguments (source and output directories) of the sphinx command based on the project file structure. You can also specify these configurations `plct_config.yaml`.

## License

This CLI app is licensed under the [MIT License](LICENSE). Feel free to customize and extend it according to your needs.