semvec/pypi-packages huggingface dataset:
torchpick 0.0.1
This is a meta package to install PyTorch for different accelerator versions
Requires Python: >=3.9

# What is this?

This repository is a proof of concept on how we can change PyTorch's package installation
for the better. It should enable a path to where we can have users specify which
specific accelerator they would like to use by using the following installation command(s):

> [!NOTE]
> This doesn't work as of today this is just showcasing what this could look like if we rolled
> this as the default torch package.

```bash
# Based on accelerator type
pip install torch[cpu]
pip install torch[cuda]
pip install torch[rocm]
pip install torch[xpu]

# Based on accelerator version
pip install torch[cpu-cxx11-abi]
pip install torch[cuda12-4]
pip install torch[cuda12-1]
pip install torch[rocm6-2]
pip install torch[rocm6-1]
```

## How to build

### Pre-requisites:
* [uv](https://github.com/astral-sh/uv)
* python3.12 (with build module installed)

### Commands to run

```bash
# Build the wheel and rename to platform specific
./generate.py
```

## Work Todo
- [ ] Create automation to build on Linux, macOS, & Windows through Github Actions
- [ ] Automate this to automatically reflect PyTorch's binary build matrix
autolaunch 1.0.9
Bibliothèque pour enregistrer et lancer des applications Python depuis n'importe où
Requires Python: >=3.6

# autolaunch

`autolaunch` est une bibliothÃ¨que Python permettant d'enregistrer automatiquement des applications Python pour qu'elles puissent Ãªtre lancÃ©es depuis n'importe oÃ¹ en utilisant simplement leur nom.

## Installation
```bash
pip install autolaunch
pyfastdoop 1.0.1
A Python wrapper for fastdoop library with Spark
Requires Python: >=3.6

# pyFastdoop

A Python wrapper for the [Fastdoop library](https://github.com/umbfer/fastdoop/), allowing easy integration of Fastdoop's Hadoop InputFormat with PySpark to process FASTA files.

---

## Installation

You can install the `pyfastdoop`  using:

```bash
pip install pyfastdoop
python -m pyfastdoop.install
```

or compile source code:
```bash
git clone https://github.com/riccardoc95/pyfastdoop
cd pyfastdoop
python setup.py install
python -m pyfastdoop.install
```

---

## Test

```bash
cd test
spark-submit test_module.py
```

---

## Dependencies

- **PySpark**: This package requires PySpark (>= 3.0.0, < 4.0.0) to function, as it integrates with Spark for distributed processing.
  
---

## Usage

### 1. **pyFastdoop**

To use this wrapper, create an instance of the `pyFastdoop` class. You can either provide a SparkContext (if you already have one) or specify the path to the Fastdoop JAR file to create a new SparkContext.

```python
from pyfastdoop import pyFastdoop

# Example when you need to provide a JAR path
fastdoop = pyFastdoop()

# Example with an existing SparkContext
from pyspark import SparkContext
sc = SparkContext()  # Create an existing SparkContext
fastdoop = pyFastdoop(sc=sc)
```

### 2. **Reading FASTA files**

You can read FASTA files (or any file supported by Fastdoop) using the `read_fasta()` method.

```python
# Read FASTA file and get the RDD
rdd = fastdoop.read_fasta("input_file.fasta", type="short") # type = short, long, fastq

# Print the contents of the RDD
fastdoop.print_rdd(rdd)
```

### 3. **Stop the SparkContext**

Once you're done processing, you can stop the SparkContext to release resources:

```python
wrapper.stop()
```

---

## Example

```python
from pyfastdoop import pyFastdoop

# Initialize FastdoopWrapper with JAR path
fastdoop = pyFastdoop()

# Read FASTA file
rdd = fastdoop.read_fasta("input_file.fasta")

# Print the contents of the RDD
fastdoop.print_rdd(rdd)

# Stop SparkContext
fastdoop.stop()
```


---

## Contributing

Feel free to contribute! Open a pull request or create an issue on the GitHub repository.
AutoXplainAI 0.2.0
An Automatic Model Explanation Framework
Requires Python: >=3.7

# AutoXplainAI

AutoXplainAI is an Automatic Model Explanation Framework that simplifies the process of interpreting machine learning models. It provides tools for generating explanations and visualizing them in an intuitive way.

## Features
- Automatic generation of model explanations
- Visualization of feature importances
- Support for multiple explanation methods

## Installation
```
pip install autoxplainai
```

## Usage
```python
from autoxplainai.explainer import Explainer
explainer = Explainer(model)
explanations = explainer.explain(X_test)
```
pgzhelper-rw 1.0.10
Pygame Zero Helper enhance Pygame Zero with additional capabilities
Requires Python: >=3.7
Keywords: roboticsware, neopia, pygame zero

# pgzhelper
Pygame Zero Helper was originally created to enhance Pygame Zero with additional capabilities that were available in Scratch (eg. move_forward, point_towards, distance_to), but it has since evolved to include a large number of collision detection functions that are useful in games.

The capabilities provided by pgzhelp can be roughly divided into...

* Actor enhancements
    * Additional capabilities for a Pygame Zero Actor
    * Examples: 
        * Movements: move_forward, point_towards
        * Animation: images, animate, fps
        * Image: scale, flip_x, flip_y
* Collision detection
    * Detect collision between points, lines, circles, rect (AABB), rect (OBB)
    * Examples:
        * Collide.circle_line() : Check if a circle is colliding with a line
        * Collide.obb_points() : Check if an OBB rect is colliding with any of the given points
* Utility
    * Small utility functions
    * Examples:
        * For Pygame Zero: toggle_fullscreen, hide_mouse
        * Movement and directions: distance_to, direction_to

## Wiki
Please see the wiki on github for more details.
pyBlindRL 0.1.2rc172.post1
A Python implementation of blind Richardson-Lucy deconvolution
Requires Python: >=3.10

# pyBlindRL

This repository implements blind Richardson-Lucy deconvolution [1] using a PyTorch-based pipeline.

[1] D. A. Fish, A. M. Brinicombe, E. R. Pike, and J. G. Walker, "Blind deconvolution by means of the Richardson–Lucy algorithm," J. Opt. Soc. Am. A 12, 58-65 (1995).

## Template Citation

This project's layout and CI are based on the great Python project template provided by [Microsoft](https://github.com/microsoft/python-package-template).