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PrimeFeat

primefeat Logo

Detect and characterize features in the primordial power spectrum from MCMC cosmological parameter chains

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Key Features

  • Fast power spectrum computation from MCMC chains with intelligent caching
  • Statistical significance testing using Gaussian Process methods
  • Dimensionality reduction with PCA to reveal dominant modes
  • Publication-ready plots with custom matplotlib styling
  • YAML-based workflow for managing multiple chains

Quick Start

import primefeat as pf
import numpy as np

# Load chains from config
chains = pf.get_chains(kmin=1e-4)

# Compute power spectrum posteriors
k = np.logspace(-4, 0, 100)
samples = {
    label: pf.compute_Pk_samples(
        k, chain,
        nbins=20,
        k_start=1e-4,
        k_end=0.23,
        include_powerlaw=True
    )
    for label, chain in chains.items()
}

# Plot
fig = pf.plot.posteriors_PPS(
    k, samples,
    colors=['#2E86AB', '#A23B72', '#F18F01'],
    mode="full"
)

Installation

pip install primefeat

Or from source:

git clone https://github.com/rcalderonb6/primefeat.git
cd primefeat
pip install -e .

Citation

If you use PrimeFeat in your research, please cite:

@software{primefeat2025,
  author = {Calderon, Rodrigo},
  title = {PrimeFeat: Primordial Power Spectrum Feature Analysis},
  year = {2025},
  url = {https://github.com/rcalderonb6/primefeat}
}

License

MIT License - see LICENSE for details.