1D bimodal example

This example shows how to use lightkde.kde_1d and how it compares to scipy.stats.gaussian_kde for a bimodal univariate case.

Import packages:

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, norm

from lightkde import kde_1d

Generate synthetic data from two univariate normal distributions:

np.random.seed(42)
sample = np.hstack((norm.rvs(size=2_000), 0.3 * norm.rvs(size=1_000) + 5))

Estimate kernel density using lightkde:

density_vec, x_vec = kde_1d(sample_vec=sample)

Estimate kernel density using scipy:

gkde = gaussian_kde(dataset=sample)
scipy_density_vec = gkde.evaluate(x_vec)

Plot the data against the kernel density estimates:

plt.plot(x_vec, density_vec, "--r", label="lightkde")
plt.plot(x_vec, scipy_density_vec, label="scipy.stats.gaussian_kde")
plt.hist(sample, bins=100, density=True, alpha=0.5, label="data")
plt.legend()
plt.show()

The scipy method oversmooths the kernel density and it is far from the histogram of the data that it is expected to follow.