Note

Click here to download the full example code

Corner Plots

The corner() function produces a full corner plot with FFT-KDE contours, CI bands, truth markers, and a clean legend. All examples share the same synthetic GW-inspired posterior so outputs are directly comparable.

See also: Cornetto.plot(), quick_corner()

Data input shapes

Cornetto accepts a dict[str, array]. Arrays can be 1-D (single chain) or 2-D (N_chains, N_samples) for multiple chains in one call.

import numpy as np
from cornetto import corner

rng = np.random.default_rng(0)
N = 20_000

# Single chain — plain 1-D arrays
data_1chain = {
    "mass_1": rng.normal(30, 4, N),
    "mass_2": rng.normal(25, 3, N),
}

fig, axes = corner(data_1chain, chain_labels=["GW150914"])

Two chains in one call — 2-D (N_chains, N_samples) arrays. Labels accept LaTeX via raw strings.

labels_2 = {
    "mass_1": r"$m_1\,[M_\odot]$",
    "mass_2": r"$m_2\,[M_\odot]$",
}
data_2chains = {
    "mass_1": np.stack([rng.normal(30, 4, N), rng.normal(85, 8, N)]),
    "mass_2": np.stack([rng.normal(25, 3, N), rng.normal(66, 6, N)]),
}

fig, axes = corner(data_2chains, labels=labels_2,
                   chain_labels=["GW150914", "GW190521"])

Shared synthetic posterior

Four parameters drawn from simple distributions, mimicking a compact-binary posterior.

data = {
    "mass_1": rng.normal(30, 4, N),
    "mass_2": rng.normal(25, 3, N),
    "spin":   rng.uniform(-1, 1, N),
    "dist":   rng.exponential(400, N),
}

labels = {
    "mass_1": r"$m_1\,[M_\odot]$",
    "mass_2": r"$m_2\,[M_\odot]$",
    "spin":   r"$\chi_{\mathrm{eff}}$",
    "dist":   r"$d_L\,[\mathrm{Mpc}]$",
}

Default corner plot

FFT-KDE contours, shaded CI band, truth markers on mass_1 and spin.

fig, axes = corner(
    data,
    labels=labels,
    truths={"mass_1": 30.0, "spin": 0.0},
    chain_labels=["GW200129"],
)

Coral color, dark theme

Use the built-in "coral" color and flip to a dark background.

fig, axes = corner(
    data,
    labels=labels,
    truths={"mass_1": 30.0},
    chain_labels=["GW200129"],
    color="coral",
    dark=True,
)

Teal color, contour lines only

fill_contours=False draws only the contour lines — useful for overlaying multiple distributions without filling.

fig, axes = corner(
    data,
    labels=labels,
    chain_labels=["GW200129"],
    color="teal",
    fill_contours=False,
    contour_lw=1.5,
)

Two-chain comparison

Pass a 2-D (N_chains, N_samples) array to overlay two events. chain_labels names each chain in the legend.

data_2 = {
    k: np.stack([data[k], rng.normal(data[k].mean() + 5, 3, N)])
    for k in ["mass_1", "mass_2"]
}

fig, axes = corner(
    data_2,
    labels={k: labels[k] for k in ["mass_1", "mass_2"]},
    chain_labels=["Event A", "Event B"],
    color="cornetto",
)

Three sigma levels

Add a third contour at 3σ (98.9 % 2-D probability mass).

fig, axes = corner(
    data,
    labels=labels,
    chain_labels=["GW200129"],
    sigmas=(1, 2, 3),
)

Total running time of the script: ( 0 minutes 6.543 seconds)

Download Python source code: plot_0_corner.py

Download Jupyter notebook: plot_0_corner.ipynb

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