semvec/pypi-packages huggingface dataset:
easypymanager 1.2.1
EasyPyManager is a GUI application for managing and executing Python scripts. 
Requires Python: >=3.7

# EasyPyManager - Python Tool Manager

EasyPyManager is a GUI application for managing and executing Python scripts(.py/.pyc/.pyd/.so) files. It automatically extracts script metadata and provides features like grouping, searching, and dependency installation.

## Key Features

Import and manage Python scripts (.py/.pyc/.pyd/.so)
Automatic metadata extraction or manual input (name, version, description)
Group management for scripts
Real-time search functionality
Customizable Python and pip commands
One-click dependency installation
WHL file installation support
Drag-and-drop file import
Run scripts via double-click or right-click menu

## Installation

1. Ensure Python 3.7+ and pyqt5 are installed
2. Clone or download this project
3. Install:
   pip install easypymanager
pytorch-mvgc 0.1.0
A GPU-accelerated Multivariate Granger Causality implementation using PyTorch.
Requires Python: >=3.6

# PyTorch-MVGC

[![PyPI version](https://badge.fury.io/py/pytorch-mvgc.svg)](https://badge.fury.io/py/pytorch-mvgc)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**PyTorch-MVGC** is a GPU-accelerated Python implementation of **Multivariate Granger Causality (MVGC)** analysis. 

It is designed for high-dimensional time-series data (e.g., EEG, fMRI, MEA) where traditional CPU-based implementations are too slow. By leveraging PyTorch tensors and batched matrix operations, this library achieves significant speedups compared to standard NumPy or Matlab implementations.

## Features

- **🚀 GPU Acceleration**: Fully implemented in PyTorch, supporting CUDA acceleration out-of-the-box.
- **⚡ Batch Processing**: Optimized for processing multiple trials or sliding windows simultaneously.
- **🔧 Numerical Stability**: Implements Ridge regularization and Pseudo-inverse solvers to handle ill-conditioned matrices common in high-channel neural recordings (e.g., 128-channel EEG).
- **🐍 Python Native**: A seamless replacement for the Matlab MVGC toolbox for Python pipelines.

## Installation

```bash
pip install pytorch-mvgc
```

## Quick Start

```python
import numpy as np
import torch
from pytorch_mvgc import compute_mvgc

# 1. Generate sample data (3 channels, 1000 timepoints)
# System: Node 0 drives Node 1
n_time = 1000
X = np.zeros((3, n_time))
noise = 0.5 * np.random.randn(3, n_time)
for t in range(1, n_time):
    X[0, t] = 0.8 * X[0, t-1] + noise[0, t]
    X[1, t] = 0.5 * X[1, t-1] + 0.5 * X[0, t-1] + noise[1, t] # 0->1 Causal
    X[2, t] = 0.7 * X[2, t-1] + noise[2, t]

# 2. Compute MVGC
# Returns matrix F where F[i, j] denotes causality j -> i
F_matrix = compute_mvgc(X, p=1, device='cuda')

print("MVGC Matrix:\n", np.round(F_matrix, 4))
# Expected: F[1, 0] should be significant (> 0)
```