greapy 0.3.1

A python-based implementation of the General Relativistic Entropic Acceleration (GREA) theory

A python-based implementation of the General Relativistic Entropic Acceleration (GREA) theory

General Relativistic Entropic Acceleration is a theoretical framework that offers an alternative explanation for the observed accelerated expansion of the universe through entropic forces, without invoking a cosmological constant or dark energy. GREApy provides the core GREA background cosmology, growth factor computation, a Cobaya theory wrapper for Bayesian parameter estimation, and post-processing utilities for MCMC chains.

• Free software: MIT License
• Documentation: https://rcalderonb6.github.io/greapy

Quick start

import numpy as np
from greapy import GREA

# Initialise with default (best-fit) parameters
model = GREA()

# Hubble parameter as a function of redshift
z = np.linspace(0, 2, 100)
H_z = model.H(z)  # km/s/Mpc

# Key derived quantities
print(model.w0)       # effective dark energy equation-of-state today
print(model.rdrag)    # sound horizon at the drag epoch (Mpc)
print(model.alpha)    # GREA alpha parameter

Installation

uv pip install greapy          # recommended
pip install greapy             # alternative

For optional plotting and Cobaya support:

uv pip install "greapy[extra]"
pip install "greapy[extra]"

See the documentation for full installation details.

Citation

If you use GREApy in your research, please cite:

@article{Calderon:2025dhj,
    author = "Calderon, R. and others",
    title = "{Constraining GREA, an alternative theory accounting for the present cosmic acceleration}",
    eprint = "2509.21491",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.CO",
    reportNumber = "DESI-2024-0464, IFT-UAM/CSIC-25-99, FERMILAB-PUB-25-0737-PPD",
    month = "9",
    year = "2025"
}