semvec/pypi-packages huggingface dataset:
boto3-pydantic-inspector 1.0.2
Pydantic models for AWS Inspector
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, inspector

# boto3-pydantic-inspector

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Inspector.
boto3-pydantic-importexport 1.0.2
Pydantic models for AWS Importexport
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, importexport

# boto3-pydantic-importexport

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Importexport.
rakopy 0.0.5
RakoPy is a Python library that allows you to control Rako Controls system programmatically.
Requires Python: >=3.12

# RakoPy
RakoPy is a Python library that allows you to control [Rako Controls](https://rakocontrols.com) system programmatically.

The library has been tested with Rako [WK-HUB](https://rakocontrols.com/wkhub/) but it should also work with [RK-HUB](https://rakocontrols.com/rkhub/).


## Installation
```
pip install rakopy
```

## Usage
```python
import asyncio

from rakopy.hub import Hub

async def main():
    client_name = "client_name"
    host = "192.168.1.42"
    hub = Hub(client_name, host)
    response = await hub.get_rooms()

asyncio.run(main())
```

## License
RakoPy is released under the [MIT license](https://github.com/princekama/rakopy/blob/main/LICENSE).
boto3-pydantic-imagebuilder 1.0.2
Pydantic models for AWS Imagebuilder
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, imagebuilder

# boto3-pydantic-imagebuilder

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Imagebuilder.
llm-pdf-to-images 0.1
LLM fragment plugin to load a PDF as a sequence of images
Requires Python: >=3.9

# llm-pdf-to-images

[![PyPI](https://img.shields.io/pypi/v/llm-pdf-to-images.svg)](https://pypi.org/project/llm-pdf-to-images/)
[![Changelog](https://img.shields.io/github/v/release/simonw/llm-pdf-to-images?include_prereleases&label=changelog)](https://github.com/simonw/llm-pdf-to-images/releases)
[![Tests](https://github.com/simonw/llm-pdf-to-images/actions/workflows/test.yml/badge.svg)](https://github.com/simonw/llm-pdf-to-images/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/llm-pdf-to-images/blob/main/LICENSE)

LLM fragment plugin to load a PDF as a sequence of images

## Installation

Install this plugin in the same environment as [LLM](https://llm.datasette.io/).
```bash
llm install llm-pdf-to-images
```
The `llm-pdf-to-images` plugin provides a [fragment loader](https://llm.datasette.io/en/stable/fragments.html#using-fragments-from-plugins) that converts each page of a PDF document into an image attachment.

You can use the `pdf-to-images:` fragment prefix to convert a PDF file into a series of image attachments which can be sent to a model.

Example usage:

```bash
llm -f pdf-to-images:path/to/document.pdf 'Summarize this document'
```

### Fragment syntax

```
pdf-to-images:<path>?dpi=N&format=jpg|png&quality=Q
```

- `<path>`: Path to the PDF file accessible to the environment where LLM runs.
- `dpi=N`: (optional) Dots per inch to use when rendering the PDF pages, which affects the resolution of the output images. Defaults to `300` if omitted.
- `format=jpg|png`: (optional) Image format to use for the output. Can be either `jpg` (default) or `png`.
- `quality=Q`: (optional) JPEG quality factor between 1 and 100. Only applies when using JPG format. Defaults to `30` if omitted. Higher values produce better quality but larger file sizes.

### More examples

Convert a PDF file to images with default settings (300 DPI, JPG format, quality 30):

```bash
llm -f pdf-to-images:document.pdf 'summarize this document'
```

Convert a PDF with higher resolution (600 DPI):

```bash
llm -f 'pdf-to-images:document.pdf?dpi=600' 'summarize'
```

Convert a PDF to PNG format:

```bash
llm -f 'pdf-to-images:document.pdf?format=png' 'describe all figures'
```

Convert a PDF with high-quality JPG images:

```bash
llm -f 'pdf-to-images:document.pdf?quality=90' 'extract all visible text'
```

Combine multiple parameters:

```bash
llm -f 'pdf-to-images:document.pdf?dpi=450&format=jpg&quality=75' 'OCR'
```

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd llm-pdf-to-images
python -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
python -m pip install -e '.[test]'
```
To run the tests:
```bash
python -m pytest
```
boto3-pydantic-identitystore 1.0.2
Pydantic models for AWS Identitystore
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, identitystore

# boto3-pydantic-identitystore

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Identitystore.
boto3-pydantic-iam 1.0.2
Pydantic models for AWS Iam
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, iam

# boto3-pydantic-iam

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Iam.
boto3-pydantic-healthlake 1.0.2
Pydantic models for AWS Healthlake
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, healthlake

# boto3-pydantic-healthlake

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Healthlake.
boto3-pydantic-health 1.0.2
Pydantic models for AWS Health
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, health

# boto3-pydantic-health

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Health.
boto3-pydantic-guardduty 1.0.2
Pydantic models for AWS Guardduty
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, guardduty

# boto3-pydantic-guardduty

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Guardduty.
boto3-pydantic-groundstation 1.0.2
Pydantic models for AWS Groundstation
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, groundstation

# boto3-pydantic-groundstation

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Groundstation.
mypv 0.2.0
my-PV device communication library
Requires Python: >=3.13
Keywords: acthor, mypv

# my-PV device communication library

## Installation

```bash
pip install mypv

# If you want to use the HTTP interface
pip install mypv[http]
```

## Supported devices

I have only tested the AC-THOR device and specifically the CH edition.
If you own any my-PV device, please contact me. It should be easy to add support for it if you have
the device and can provide me with the necessary information.
boto3-pydantic-greengrassv2 1.0.2
Pydantic models for AWS Greengrassv2
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, greengrassv2

# boto3-pydantic-greengrassv2

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Greengrassv2.
boto3-pydantic-greengrass 1.0.2
Pydantic models for AWS Greengrass
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, greengrass

# boto3-pydantic-greengrass

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Greengrass.
boto3-pydantic-grafana 1.0.2
Pydantic models for AWS Grafana
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, grafana

# boto3-pydantic-grafana

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Grafana.
boto3-pydantic-glue 1.0.2
Pydantic models for AWS Glue
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, glue

# boto3-pydantic-glue

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Glue.
boto3-pydantic-globalaccelerator 1.0.2
Pydantic models for AWS Globalaccelerator
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, globalaccelerator

# boto3-pydantic-globalaccelerator

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Globalaccelerator.
scikit-ptm-fs 0.12
A Python package for integrating scikit-learn feature selection with multi-label problem transformation methods.
Requires Python: >=3.7

# Scikit-PTM-FS

[![PyPI version](https://badge.fury.io/py/scikit-ptm-fs.svg)](https://pypi.org/project/scikit-ptm-fs/)
[![Python](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

**Scikit-PTM-FS** is a Python library that enables seamless integration of `scikit-learn` feature selection methods with multi-label classification via problem transformation methods (PTMs). The framework enables transformation-aware feature selection without requiring classification stages, allowing researchers to analyze the behavior of FS methods across various PTMs.

---

## 📦 Installation

```bash
pip install scikit-ptm-fs
```

---

## 🚀 Usage Example

Here is a basic example of how to apply a feature selection method within a problem transformation method using `Scikit-PTM-FS`:

```python
from scikit_ptm_fs.problem_transformation import LabelPowerset
from sklearn.feature_selection import SelectKBest, f_classif

# Example feature and label matrices (replace with your own)
# X = ...  # shape (n_samples, n_features)
# y = ...  # shape (n_samples, n_labels)

# Step 1: Define a scikit-learn selector
selector = SelectKBest(score_func=f_classif, k=5)

# Step 2: Wrap it using the LabelPowerset PTM
lp_selector = LabelPowerset(selector=selector, require_dense=[False, True])

# Step 3: Apply feature selection
X_selected = lp_selector.fit_transform(X, y)

# Step 4: Get selected feature indices
selected_indices = lp_selector.get_support()
print("Selected features:", selected_indices)

indices = np.where(lp_selector.get_support())[0]
print("The Number of Selected Features:", len(indices))
```

> 💡 You can use other PTMs like `BinaryRelevance`, `PPT`, or `PairwiseComparison` similarly.

---

## 🧠 Supported Problem Transformation Methods (PTMs)

- **Binary Relevance (BR)** — One-vs-All decomposition.
- **Label Powerset (LP)** — Unique labelset encoding.
- **Pairwise Comparison (PW)** — One-vs-One label pair modeling.
- **Pruned Problem Transformation (PPT)** — Labelset pruning before transformation.
- **Entropy-based Label Assignment (ELA)** — Copy-based transformation for NLP.

---

## 🔗 Project Links

- 📦 PyPI: [https://pypi.org/project/scikit-ptm-fs](https://pypi.org/project/scikit-ptm-fs)
- 🛠️ Source Code: [https://github.com/Omaimah-AlHosni/scikit-ptm-fs](https://github.com/Omaimah-AlHosni/scikit-ptm-fs)

---

## 📄 License

This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
boto3-pydantic-glacier 1.0.2
Pydantic models for AWS Glacier
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, glacier

# boto3-pydantic-glacier

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Glacier.
boto3-pydantic-geo-routes 1.0.2
Pydantic models for AWS Geo_routes
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, geo_routes

# boto3-pydantic-geo_routes

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Geo_routes.
boto3-pydantic-geo-places 1.0.2
Pydantic models for AWS Geo_places
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, geo_places

# boto3-pydantic-geo_places

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Geo_places.
boto3-pydantic-geo-maps 1.0.2
Pydantic models for AWS Geo_maps
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, geo_maps

# boto3-pydantic-geo_maps

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Geo_maps.
pipfixer 1.2.4
Auto-installs missing Python imports from a script
Requires Python: >=3.6

# ⚡ pipfixer — Advanced Python Dependency Auto-Fixer

**pipfixer** is a robust CLI tool that scans your Python files for `import` statements, detects missing dependencies, and automatically installs them via `pip`.  
With advanced flags and reporting features, it’s the perfect utility to clean, optimize, and document your Python environments with precision.

---

## 🧩 Why pipfixer?

- Tired of `ModuleNotFoundError`?
- Need to generate `requirements.txt` without manual tracking?
- Want a smart way to clean unused packages?
- Wish you had a visual HTML dependency report?

🎉 **pipfixer does all that and more.**

---

## 🔧 Key Features

| Category              | Feature Description |
|-----------------------|----------------------|
| 📦 Dependency Fixing  | Detect and install missing `pip` modules |
| 🤖 Smart Automation   | Fully autonomous with `--yes` or interactive installs |
| 🔍 Deep Analysis      | AST-based import detection (handles `import`, `from ... import ...`) |
| 💾 Output & Reports   | Generate JSON, HTML, logs, requirements.txt |
| 🧹 Cleanup Tools      | Detect and uninstall unused installed packages |
| ⚙️ Custom Pip Control | Add extra pip args like `--no-cache-dir`, etc. |
| 🚀 Multi-File Support | Auto-scans all `.py` files in current folder |
| 🧪 Dry Run Mode       | Preview missing modules without making changes |
| 🛡️ Safe Handling      | Ignore warnings during installation if needed |

---

## 📦 Installation

From PyPI:
```bash
pip install pipfixer
automatic-youtube-maker-story 0.5
Gerador de video de 1 minuto automático, utilizando ComfyUI, narration-xtts2, story-children-video-maker e story-chilren-gemma3
Requires Python: <4.0,>=3.11

# Automatic-Youtube-Maker
**Descrição breve:** Gerador de video de 1 minuto automatico, utilizando ComfyUI, narration-xtts2, story-children-video-maker e story-chilren-gemma3 .

## Índice

- [Introdução](#introdução)
- [Instalação](#instalação)
  - [Pré-requisitos](#pré-requisitos)
  - [Configuração de path_history](#configuração-de-path_history)
  - [Instalando Dependências](#instalando-dependências)

## Introdução

[Em construlçai]

## Instalação

### Instale Automatic-Youtube-Maker


```shell
pip install Automatic-Youtube-Maker

```

### Configuração de path_history

Pelo menos uma vez é necessário implementar a função **install_packeges.start()** para definir o caminho.


```python
import automatic_youtube_maker as automatic

automatic.install_packeges.start()

```
### Pré-requisitos


- Python = "3.8+"

### Notes Versão

## 0.5
Suporte Windows 11 - Modulos Testados: video, historia, narração, editor, youtube

## 0.4
Suporte Linux 22.04 (Teste Lubuntu) - Modulos Testados: video, historia, narração, editor

## 0.3
Implementando suporte Wrapper ComfyUI
boto3-pydantic-gameliftstreams 1.0.2
Pydantic models for AWS Gameliftstreams
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, gameliftstreams

# boto3-pydantic-gameliftstreams

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Gameliftstreams.