semvec/pypi-packages huggingface dataset:
neurovlm 0.0.2
Neuro vision-language models.
Requires Python: <3.13,>=3.10

# NeuroVLM

NeuroVLM maps between neuroimaging activation maps and neuroscience text.

![model](https://github.com/neurovlm/neurovlm_data/blob/13dd7769f9603c036a9338b7da4adc2f3a03ec94/docs/model.png)

## Install

Minimal, inference-only installation:

```bash
pip install neurovlm
```

With optional dependencies needed to train and reproduce analyses:

```bash
pip install "neurovlm[full]"
```

## Quickstart

Fetch NeuroVLM's datasets:

```python
from neurovlm.data import fetch_data
fetch_data()
```

Use the `NeuroVLM` object for text-to-brain, brain-to-text, text-to-text, and brain-to-brain:

```python
from neurovlm import NeuroVLM
from neurovlm.data import load_latent

# Text-to-brain: generation
nvlm = NeuroVLM()
result = nvlm.text(["vision", "default mode network"]).to_brain(head="mse")
result.to_nifti() # returns list of nib.Nifti1Image
result.plot(0, threshold=0.25) # plot image for vision
result.plot(1, threshold=0.15) # plot image for DMN

# Text-to-brain: ranking and retrieval
nvlm = NeuroVLM()
result = nvlm.text("motor").to_brain(head='infonce')
top = result.top_k(2) # each row pairs to a neuorimage that is similar to the text query
top.plot_row(1, threshold=0.1) # WashU atlas
top.plot_row(2, threshold=2.5) # NeuroVault
top.plot_row(4, threshold=0.1) # PubMed

# Brain-to-text: ranking and retrieval
nvlm = NeuroVLM()
result = nvlm.brain(load_latent("networks_neuro")["Du"]["AUD"]).to_text()
result.top_k(5).query("cosine_similarity > 0.4") # return up to 5 examples per dataset
```

## Data and API

- Data/model fetch and loaders: `neurovlm.data`
- Full API reference: `docs/api.rst`

## License

Apache-2.0 (`LICENSE`).
panl 1.0.0
Analysis utility for structural panels with cutouts
Requires Python: <4.0,>=3.11
Keywords: structural, mechanics, BEM, panel, stress, aerospace

![Panl Logo](https://github.com/user-attachments/assets/394c4f2e-60f8-4c6b-8fb5-2df8f39c5de5)

# Panl

[![Tests](https://github.com/Czarified/panl/actions/workflows/tests.yml/badge.svg)](https://github.com/Czarified/panl/actions/workflows/tests.yml)
[![Python Version](https://img.shields.io/badge/python-3.11%2B-blue)](https://www.python.org/downloads/)
[![Poetry](https://img.shields.io/badge/dependency%20management-poetry-blue)](https://python-poetry.org/)

**Panl** is a Python library for analyzing structural panels with cutouts, providing tools for stress analysis, geometric optimization, and design validation.

## Features

- 🔧 Structural panel analysis with cutout support
- 📊 Stress concentration calculations
- 🎯 Geometric optimization
- ✅ Comprehensive testing with pytest
- 🎨 Code quality enforced with pre-commit hooks

## Installation

### From PyPI (once published)

```bash
pip install panl
```

### For Development

```bash
# Clone the repository
git clone https://github.com/Czarified/panl.git
cd panl

# Install Poetry if you haven't already
# Visit: https://python-poetry.org/docs/#installation

# Install dependencies
poetry install

# Install pre-commit hooks
poetry run pre-commit install
```

## Quick Start

```python
import panl

# Your code here
print(f"Panl version: {panl.__version__}")
```

## Development

This project uses several tools to maintain code quality:

- **Poetry**: Dependency management
- **Nox**: Task automation and testing across Python versions
- **pre-commit**: Git hooks for code quality
- **pytest**: Testing framework with coverage

### Running Tests

```bash
# Run tests with coverage
poetry run nox -s tests

# Run tests for specific Python version
poetry run nox -s tests-3.11
```

### Code Quality

```bash
# Run all linters
poetry run nox -s lint

# Format code with black
poetry run nox -s black

# Sort imports with isort
poetry run nox -s isort

# Run pre-commit hooks
poetry run nox -s pre-commit
```

### Available Nox Sessions

```bash
# List all available sessions
poetry run nox --list

# Run a specific session
poetry run nox -s <session-name>
```

## Requirements

- Python >= 3.11
- Poetry for dependency management

## Contributing

Contributions are welcome! Please ensure:

1. All tests pass: `poetry run nox -s tests`
2. Code is formatted: `poetry run nox -s black`
3. Linting passes: `poetry run nox -s lint`
4. Pre-commit hooks pass: `poetry run pre-commit run --all-files`

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Author

Benjamin Crews - your.email@example.com

This project is an Agentic Experiment by the author, and uses Gemini 3 Flash.