semvec/pypi-packages huggingface dataset:
PSD-Generator 0.1.0
A library for processing images and saving layers as PSD
Requires Python: >=3.9

# PSD Generator

A Python library for processing images and saving layers as PSD files.

## Prerequisites

Make sure you have [ImageMagick](https://imagemagick.org/index.php) installed. You can install it using the following commands:

### On Ubuntu/Debian:
```bash
sudo apt-get update
sudo apt-get install imagemagick
```

### On macOS:
You can install ImageMagick using [Homebrew](https://brew.sh/):
```bash
brew install imagemagick
```

### On Windows:
Download and install ImageMagick from the [official website](https://imagemagick.org/script/download.php).

## Installation

You can install the library using pip:

```bash
pip install PSD_Generator
```

## Usage

Here is a basic example of how to use the library:

```python
from PSD_Generator.processor import process_image

image_url = "https://example.com/image.jpg"
result = process_image(image_url, method_type=1, bandwidth=10, is_dynamic=1, num_colors=50)
print(result)
```

## Configuration

The `process_image` function takes the following parameters:

- `image_path` (str): URL or local path to the image file.
- `method_type` (int): Method type for processing (0 for default, 1 for custom).
- `bandwidth` (int): Bandwidth parameter for MeanShift clustering (used if `method_type` is 1).
- `is_dynamic` (int): Flag to use dynamic color extraction (1 for dynamic, 0 for predefined).
- `num_colors` (int): Number of dominant colors to extract (used if `is_dynamic` is 1).

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
finalse-sdk 1.0.3
Sdk to access Finalse Cloud platform in Python based environment
Requires Python: >=3.5

<h1 align="center">Welcome to finalse-sdk 👋</h1>
<p align="center">

![PyPI](https://img.shields.io/pypi/v/finalse-sdk.svg)
![Python Versions](https://img.shields.io/pypi/pyversions/finalse-sdk.svg)
<a href="https://developers.finalse.com" target="_blank">
    <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" />
  </a>

  <a href="#" target="_blank">
    <img alt="License: Apache--2.0" src="https://img.shields.io/badge/License-Apache--2.0-yellow.svg" />
  </a>
</p>

> Sdk to access Finalse Cloud platform in Python based environment.

## Install

`pip install finalse-sdk` 

## Usage

Documentation is available on
[https://developers.finalse.com](https://developers.finalse.com)

### 🏠 [Homepage](https://finalse.com)

## Author

👤 **Finalse Cloud <opensource@finalse.com>**

- Github: [@finalse](https://github.com/finalse)

## Show your support

Give a ⭐️ if this project helped you!
PTPNperfbound 0.0.1
Performance bound solver for Probabilistic Timed Petri Nets
Requires Python: >=3.8
Keywords: PTPN, LPP, performance, bounds, slowest subnet

A performance bound solver for Probabilistic Timed Petri Nets

The solver computes:
1. Transitions maximum throughput bound 
2. Transitions minimum cycle times (in case of live nets) and identifies the slowest subnet.

The computation is performed using Linear Programming techniques.
torch-cuda-installer 0.1.0
A tool to automatically install PyTorch packages with CUDA support if available
Requires Python: >=3.6
Keywords: pytorch, torch, cuda, installer, deep-learning, nvidia


# PyTorch CUDA Installer

PyTorch CUDA Installer is a Python package that simplifies the process of installing PyTorch packages with CUDA support. It automatically detects the available CUDA version on your system and installs the appropriate PyTorch packages.

## Installation

You can install the PyTorch CUDA Installer using pip:

```
pip install torch-cuda-installer
```

## Usage

After installation, you can use the package in two ways:

1. As a command-line tool:

   ```
   torch-cuda-installer --torch --torchvision --torchaudio
   ```

2. As a Python module:

   ```python
   from torch_cuda_installer import install_pytorch

   install_pytorch(cuda_key=None, packages=['torch', 'torchvision', 'torchaudio'])
   ```

Use any combination of `--torch`, `--torchvision`, and `--torchaudio` to select which packages to install.
generateMFT 1.0.0
A project to generate MFT tables with varied errors
Requires Python: >=3.6

# MFT Test Generator

A Python tool for generating test Master File Table (MFT) files with configurable record counts and error rates.

## Features

- Generate MFT files with customizable number of records
- Introduce random errors at a specified rate
- Modular design for easy extension and maintenance

## Requirements

- Python 3.6+

## Quick Start

1. Clone the repository:

`git clone https://github.com/rowingdude/mft-test-generator.git`
`cd mft-test-generator`


2. Run the mft_generator script:

`python mft_generator.py`

3. Follow the prompts to generate your MFT test file.

## Usage

For basic usage, run `mft_generator.py` and follow the interactive prompts.

For programmatic use, import the generator in your Python script:

``` 
from mft_file_generator import generate_mft_file

generate_mft_file("test.bin", 100, 0.1)  # 100 records, 10% error rate

```