Smooth Gaussians in corner plot

How to get smooth Gaussians in corner plot? The option smooth=True did not help. Have I used the option incorrectly? The following is my code (which is the example from here). In the plot, only the contour lines are smoothened but not the Gaussian or the shaded region within the contours (which appear like squares). I have also, attached the plots with and without the smooth=True option.

Thanks in advance.

import matplotlib.pyplot as plt
import tqdm

np.random.seed(123)

m_true = -0.9594
b_true = 4.294
f_true = 0.534

N = 50
x = np.sort(10 * np.random.rand(N))
yerr = 0.1 + 0.5 * np.random.rand(N)
y = m_true * x + b_true
y += np.abs(f_true * y) * np.random.randn(N)
y += yerr * np.random.randn(N)

x0 = np.linspace(0, 10, 500)

def log_likelihood(theta):
    m, b, log_f = theta
    model = m * x + b
    sigma2 = yerr ** 2 + model ** 2 * np.exp(2 * log_f)
    return -0.5 * np.sum((y - model) ** 2 / sigma2 + np.log(sigma2))

def log_prior(theta):
    m, b, log_f = theta
    if -5.0 < m < 0.5 and 0.0 < b < 10.0 and -10.0 < log_f < 1.0:
        return 0.0
    return -np.inf

m1=np.array([-1.2,-1.05,-0.9])
b1=np.array([4.2,4.8,5.4])
logf1=np.array([-1,-0.75,-0.5])

para_spc=np.array([m1,b1,logf1])
def log_probability(theta):
    lp = log_prior(theta)
    if not np.isfinite(lp):
        return -np.inf
    return lp + log_likelihood(theta)

import emcee

pos = np.array([-1, 4.52, -0.79]) + 1e-4 * np.random.randn(32, 3)
nwalkers, ndim = pos.shape

sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability)
sampler.run_mcmc(pos, 1000, progress=True);

fig, axes = plt.subplots(3, figsize=(10, 7), sharex=True)
samples = sampler.get_chain()
labels = ["$m$", "$b$", "$\log f$"]

for i in range(ndim):
    ax = axes[i]
    ax.plot(samples[:, :, i], "k", alpha=0.3)
    ax.set_xlim(0, len(samples))
    ax.set_ylabel(labels[i])
    ax.yaxis.set_label_coords(-0.1, 0.5)

axes[-1].set_xlabel("step number");

settings=dict(fontsize=18)
import corner
flat_samples = sampler.get_chain(discard=100, thin=15, flat=True)
fig = corner.corner(
    flat_samples,labels=labels, truths=[m_true, b_true, np.log(f_true)],labelpad=0.01,label_kwargs=settings, smooth=True)

axes = np.array(fig.axes).reshape((ndim, ndim))

plt.show()

[dfm]

Oh - I see what you’re looking for now! No, there’s no KDE option. These parameters smooth out sampling noise but you’re always going to see a histogram with bin sizes set by the bins parameter. If you want something different, you’ll need to plot it manually using matplotlib directly. To do that, you can use the method described here to access the axis objects directly and then plot whatever you want: https://corner.readthedocs.io/en/latest/pages/custom.html

Unfortunately I can’t give you more concrete suggestions.

[shikharmittal04]

Thanks.