Metadata-Version: 2.4
Name: rooftex
Version: 0.1.0
Summary: RoofteX - Rooftop Solar eXchange: rooftop PV potential, adoption dynamics, and profile generation
Author: RoofteX Development Team
License: MIT
Project-URL: Homepage, https://github.com/rooftex-dev/rooftex
Project-URL: Repository, https://github.com/rooftex-dev/rooftex
Keywords: rooftop solar,photovoltaic,renewable energy,adoption dynamics,solar profiles,distributed generation
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.22
Provides-Extra: viz
Requires-Dist: matplotlib>=3.4; extra == "viz"
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0; extra == "dev"
Requires-Dist: ruff>=0.1; extra == "dev"
Requires-Dist: build>=1.0; extra == "dev"
Requires-Dist: twine>=4.0; extra == "dev"
Provides-Extra: all
Requires-Dist: matplotlib>=3.4; extra == "all"
Requires-Dist: pytest>=7.0; extra == "all"
Requires-Dist: pytest-cov>=4.0; extra == "all"
Dynamic: license-file

# RoofteX — Rooftop Solar eXchange

[![Tests](https://github.com/msotocalvo/rooftex/actions/workflows/tests.yml/badge.svg)](https://github.com/msotocalvo/rooftex/actions/workflows/tests.yml)
[![DOI](https://zenodo.org/badge/1175040710.svg)](https://doi.org/10.5281/zenodo.18898422)

A Python library for rooftop solar potential assessment, adoption dynamics modeling,
and stochastic availability profile generation.

## Features

- **Rooftop Potential**: Estimate maximum rooftop PV capacity from population and dwelling data
- **Adoption Dynamics**: S-curve adoption modeling with urbanization and scenario parameters
- **Profile Generation**: Stochastic hourly solar profiles with cloud patterns and weather variability
- **Cost Learning Curve**: Technology cost projection with degradation and learning rates

## Installation

```bash
pip install rooftex
```

## Quick Start

```python
from rooftex import RooftopConfig, generate_profiles, calculate_potential

# Estimate potential from population
potential = calculate_potential(population=[50000, 30000, 80000])

# Generate hourly availability profiles
config = RooftopConfig(
    num_nodes=3,
    hours=8760,
    adoption_scenario="medium",
    target_year=2040,
)
result = generate_profiles(config)
print(result.availability.shape)      # (8760, 3)
print(result.adoption_factors.mean())  # ~0.3
```

## License

MIT
