semvec/pypi-packages huggingface dataset:
mugem 3.0
Multiplicative Generative Method (MGM)
Requires Python: >=3.6

# Mugem

**Mugem (Multiplicative Generative Method)** is a lightweight Python package that generates pseudo-random numbers using the **Multiplicative Congruential Method (MCM)**. it is widely used random number generation algorithm defined by the recurrence relation:

It returns both the raw sequence of generated numbers and their normalized versions between `[0, 1)` using NumPy.

---

## Features

- Easy-to-use function interface  
- Pure Multiplicative Congruential Method (no increment term)  
- Fast random number generation  
- Normalized output with NumPy  
- User-defined input parameters  

---


## Installation

Install via pip:

```bash
pip install mugem
```


---

## Usage

```python
from mugem import mgm

# Define parameters
a = 2        # Multiplier
x0 = 5       # Seed value
m = 103      # Modulus
count = 10   # Number of values

# Generate pseudo-random numbers
y, u = mgm(a, x0, m, count)

print("Generated:", y)
print("Normalized:", u)
```

---

## Function Reference

```python
def mgm(a: int, x0: int, m: int, count: int = 10):
    """
    Multiplicative Congruential Method (MCM) to generate pseudo-random numbers.

    Parameters:
        a (int): Multiplier
        x0 (int): Initial seed value
        m (int): Modulus
        count (int): Total numbers to generate

    Returns:
        tuple:
            y (list): Raw generated integers
            u (ndarray): Normalized values in [0, 1)
    """
```

---

## Example Script

```python
from mugem import mgm

a, x0, m, count = 2, 5, 103, 10
y, u = mgm(a, x0, m, count)

print("Generated Sequence:", y)
print("Normalized Sequence:", u)
```

---

## Author

**Rohit Kumar Behera**

Email: rohitmbl24@gmail.com 

PyPI: [https://pypi.org/project/mgm/] 

Feel free to contribute, suggest improvements, or report issues.
---
process-memory-writer 0.1.0
A library for writing to process memory
Requires Python: >=3.11
Keywords: memory, process, automation, windows

# Process Memory Writer

`process_memory_writer` is a Python extension written in Rust using PyO3. It allows reading and writing memory of another process on Windows systems.

## Overview

The `MemoryWriter` class enables users to:

- **Open a process** by its executable name or PID.
- **Read memory** from the target process.
- **Write memory** to the target process.
- **Continuously write data** to a specific memory address in the process.

## Safety and Security Considerations

- Modifying another process's memory can be dangerous and may cause system instability.
- Ensure that you have the necessary permissions to manipulate the target process.
- Be aware of legal implications and software licenses when using this functionality.

## Installation

1. Ensure you have Rust and Python installed on your system.
2. Clone the repository.
3. Navigate to the project directory.
4. Create a virtual environment and activate it:
    ```sh
    python -m venv pyo3
    source pyo3/Scripts/activate  # On Windows
    ```
5. Install the required dependencies:
    ```sh
    pip install -r requirements.txt
    ```
6. Build the project using Maturin:
    ```sh
    maturin develop
    ```

## Usage

Here is an example of how to use the `MemoryWriter` class:

```python
from process_memory_writer import MemoryWriter

# Create a new MemoryWriter instance
writer = MemoryWriter()

# Open a process by name
success = writer.open_process("notepad.exe")
if success:
    print("Process opened successfully")
else:
    print("Failed to open process")

# Open a process by PID
success = writer.open_process(1234)
if success:
    print("Process opened successfully")
else:
    print("Failed to open process")

# Set memory data to write
writer.set_memory_data(0x12345678, b"data")

# Start continuous memory writing
writer.start()

# Stop continuous memory writing
writer.stop()
```
subana 0.0.3
This is a template repository for Python projects that use Poetry for their dependency management.
Requires Python: <4.0,>=3.11

# neural-subspace-analysis

[![Release](https://img.shields.io/github/v/release/p16i/neural-subspace-analysis)](https://img.shields.io/github/v/release/p16i/neural-subspace-analysis)
[![Build status](https://img.shields.io/github/actions/workflow/status/p16i/neural-subspace-analysis/main.yml?branch=main)](https://github.com/p16i/neural-subspace-analysis/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/p16i/neural-subspace-analysis/branch/main/graph/badge.svg)](https://codecov.io/gh/p16i/neural-subspace-analysis)
[![Commit activity](https://img.shields.io/github/commit-activity/m/p16i/neural-subspace-analysis)](https://img.shields.io/github/commit-activity/m/p16i/neural-subspace-analysis)
[![License](https://img.shields.io/github/license/p16i/neural-subspace-analysis)](https://img.shields.io/github/license/p16i/neural-subspace-analysis)

This is a template repository for Python projects that use Poetry for their dependency management.

- **Github repository**: <https://github.com/p16i/neural-subspace-analysis/>
- **Documentation** <https://p16i.github.io/neural-subspace-analysis/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:p16i/neural-subspace-analysis.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPI or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [PyPI](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/p16i/neural-subspace-analysis/settings/secrets/actions/new).
- Create a [new release](https://github.com/p16i/neural-subspace-analysis/releases/new) on Github.
- Create a new tag in the form `*.*.*`.
- For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).