Plot Samples from Gaussian distributations

Gaussian Process.py

import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.offsetbox import AnchoredText
from scipy import stats


from ipywidgets import interact


sns.set(style='darkgrid', context='talk', )
sns.set_theme()

def plot_func(n_samples):
    mean = 0
    std = 12
    gaussian = stats.norm(mean, std)
    xs = np.linspace(*gaussian.ppf([0.001, 0.999]), 2000)
    ys = gaussian.pdf(xs)
    sample = np.random.normal(mean, std, n_samples)
    ax = plt.gca()
    _ = sns.lineplot(x=xs,y=ys, ax=ax, color='black')
    _ = sns.histplot(data=sample,stat='probability', color= 'pink', discrete =True, ax=ax)
    anc = AnchoredText(f"n_samples {n_samples}", loc="upper left", frameon=False)
    ax.add_artist(anc)
    
interact(plot_func, n_samples = (20,10000, 50))