cornetto 0.2.0

Fast, smooth corner plots for MCMC chains.

cornetto

Fast, smooth corner plots for MCMC chains.

[!NOTE] Early release — cornetto is built for research use and works well for everyday analysis, but the API may still evolve and some features are still maturing. Feedback and contributions are very welcome.

Cornetto takes a dict[str, array] of posterior samples and produces publication-ready corner plots. Under the hood it uses KDExpress — a JAX-based FFT-KDE library — so contours are smooth and rendering stays fast even at 50k+ samples.

Install

pip install cornetto

Requires Python ≥ 3.10, NumPy, Matplotlib, SciPy, and KDExpress.

Usage

import numpy as np
from cornetto import corner

# data is a plain dict: parameter name → 1-D sample array
data = {
    "mass_1": chain["mass_1"],   # shape (N_samples,)
    "mass_2": chain["mass_2"],
    "chi_eff": chain["chi_eff"],
}

fig, axes = corner(
    data,
    labels={"mass_1": r"$m_1\,[M_\odot]$",
            "mass_2": r"$m_2\,[M_\odot]$",
            "chi_eff": r"$\chi_{\mathrm{eff}}$"},
    truths={"mass_1": 35.6},
    chain_labels=["GW200129"],
)
fig.savefig("posterior.pdf", bbox_inches="tight")

Multiple chains (e.g. two events, or prior vs posterior) are just 2-D arrays:

data = {
    "mass_1": np.stack([chain_A["mass_1"], chain_B["mass_1"]]),  # (2, N)
    "mass_2": np.stack([chain_A["mass_2"], chain_B["mass_2"]]),
}
corner(data, chain_labels=["GW150914", "GW190521"])

For fast iteration during analysis, quick_corner skips KDE entirely:

from cornetto import quick_corner
fig, axes = quick_corner(data)   # histograms only, sub-second

Documentation

Full guide, API reference, and benchmarks at cornetto.readthedocs.io.

License

MIT — see LICENSE.