semvec/pypi-packages huggingface dataset:
apsarea 0.0.3
module to calculate area
Requires Python: >=3.6

from apsarea import*
#area of square
print(square(2))
#area of rectangle
print(rectangle(2,4))
#area of circle
print(circle(2))
#area of trapezoid
print( trapezoid(1,5,8))
#area of ellipse
print(ellipse(2,4))
dlcv 0.1.0
A package for cross-validation of dual leaf models
Requires Python: >=3.6

# dual_leaf_cross_score (DLCV)

![dual_leaf_cross_score Logo](https://via.placeholder.com/600x200.png?text=dual_leaf_cross_score) <!-- Replace with an actual image URL -->

`dual_leaf_cross_score` (DLCV) is a Python package designed to evaluate machine learning models through cross-validation, with a focus on dual leaf models. The package provides easy-to-use functionality to test models on various datasets, offering flexibility in the scoring metrics and cross-validation strategies.

## Features

- **Flexible Model Support**: Works with any estimator that implements the `fit` method.
- **Cross-Validation**: Easily perform cross-validation using custom or default strategies like KFold or StratifiedKFold.
- **Custom Metrics**: Choose from built-in metrics like `mean` or define your own custom scoring function.
- **Integration with scikit-learn**: Designed to work seamlessly with scikit-learn models and pipelines.

![Cross-Validation Process](https://via.placeholder.com/800x400.png?text=Cross+Validation+Workflow) <!-- Replace with a relevant image -->

## Installation

To install the package from PyPI:

```bash
pip install dual_leaf_cross_score
relint 3.3.1
Write your own linting rules using regular expressions.
Requires Python: >=3.8
Keywords: regex, linter

![/(re)lint/](https://repository-images.githubusercontent.com/127479533/061e8bfe-f0e0-4f0b-857b-eae82f9df6af)

# reLint

**Regular Expression Linter**

*Write your own linting rules using regular expressions.*

[![PyPi Version](https://img.shields.io/pypi/v/relint.svg)](https://pypi.python.org/pypi/relint/)
[![Test Coverage](https://codecov.io/gh/codingjoe/relint/branch/main/graph/badge.svg)](https://codecov.io/gh/codingjoe/relint)
[![GitHub License](https://img.shields.io/github/license/codingjoe/relint)](https://raw.githubusercontent.com/codingjoe/relint/main/LICENSE)

## Installation

```shell-session
python3 -m pip install relint
# or, if you have super advanced linting expressions
python3 -m pip install "relint[regex]"
```

## [Examples & Recipes – The reLint Cookbook](https://github.com/codingjoe/relint/blob/main/COOKBOOK.md)

## Usage

You can write your own regular rules in a YAML file, like so:

```yaml
- name: No ToDo
  pattern: '(?i)todo' # case insensitive flag
  hint: Get it done right away!
  filePattern: .*\.(py|js)
  error: false
```

The `name` attribute is the name of your linter, the `pattern` can be
any regular expression. The linter does lint entire files, therefore
your expressions can match multiple lines and include newlines.

You can narrow down the file types your linter should be working with,
by providing the optional `filePattern` attribute. The default is `.*`.

The optional `error` attribute allows you to only show a warning but not
exit with a bad (non-zero) exit code. The default is `true`.

The following command will lint all files in the current directory:

```shell
relint -c .relint.yml FILE FILE2 ...
```

The default configuration file name is `.relint.yml` within your working
directory, but you can provide any YAML or JSON file.

If you prefer linting changed files (cached on git) you can use the
option `--diff [-d]` or `--git-diff [-g]`:

```shell
git diff --unified=0 | relint my_file.py --diff
```

### pre-commit

You can automate the linting process by adding a
[pre-commit](https://pre-commit.com/) hook to your project. Add the
following entry to your `.pre-commit-config.yaml`:

```yaml
- repo: https://github.com/codingjoe/relint
  rev: 1.4.0
  hooks:
    - id: relint
      args: [-W]  # optional, if you want to fail on warnings during commit
```
hardware-metrics 0.0.3
Decorador que proporciona estadisticas del hardware
Requires Python: >=3.10

# Hardware metrics

Librería desarrollada para la evaluación de consumo de recursos hardware por un método.

Para ello se ha desarrollado un decorador que se coloca sobre la función que ejecuta el código del que queremos obtener las métricas.

## Instalación

- Clonar repositorio
- Ejecutar el siguiente comando
        
        python3 -m pip install --upgrade build
        python3 -m build
        
- Tras ello se habrá creado una carpeta /dist con un fichero con formato .whl, se deberá hacer pip install y la ruta al archivo, esto instalará la librería y todas las dependencias en el environment actual.

## Uso

Para importar la función principal de la librería:

``` python
from hardware_metrics import hardware_metrics
```

Para usar el método, se deberá poner encima de la función el decorador importado:

``` python
@hardware_metrics('path/de/prueba')
def hello_world: #función de ejemplo
    print('hola')
```

También se puede escribir sin path para que se utilice el path por defecto 'hardware_metrics_results':

``` python
@hardware_metrics()
def hello_world: #función de ejemplo
    print('hola')
```
smrt-sql 0.1.1
A package that generates and optimizes SQL queries.
Requires Python: >=3.6

# smrt_sql: Query Generator and Optimizer

smrt_sql is a Python package that generates optimized SQL queries based on user input data.

## Features
- Automatically generate SQL queries.

## Installation
pip install auto_sql_gen

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
infin8t 1.0.1
A Python package for integrating AI-powered chatbots into web applications
Requires Python: >=3.6

# Infin8t

Infin8t is a Python package for integrating AI-powered chatbots into web applications.

## Installation

You can install Infin8t using pip:

```
pip install infin8t
```

## Usage

Here's a basic example of how to use Infin8t:

```python
from infin8t import Infin8t

# Initialize the chatbot with your API key
chatbot = Infin8t(api_key='YOUR_API_KEY')

# Send a message to the chatbot
response = chatbot.send_message('Hello, chatbot!')
print(response)

# Get the script tag for web integration
script_tag = chatbot.get_script_tag()
print(script_tag)
```

## Features

- Easy integration with web applications
- Customizable chatbot behavior
- Support for multiple AI models

## Documentation

For more detailed information, please refer to our [official documentation](https://infin8t.tech/docs).

## Contributing

We welcome contributions! Please see our [contributing guidelines](CONTRIBUTING.md) for more details.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Support

If you encounter any issues or have questions, please file an issue on our [GitHub issue tracker](https://github.com/yourusername/infin8t/issues).

## About

Infin8t is developed and maintained by the Infin8t Team. For more information, visit our [website](https://infin8t.tech).