How to get (random) number from Gaussian Mixture model(GMM)?

I want to get a list of random numbers from GMM.

I've plotted a distribution like this. And I was wondering whether there is some way to get the value from the distribution curve, such as getting number 0.94, 0.96, 0.95.... How to get it?

I am not sure whether the belowing is useful for u or not. I got the code from Minimal reproduction

from matplotlib import rc
from sklearn import mixture
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
import matplotlib.ticker as tkr
import scipy.stats as stats

# x = open("prueba.dat").read().splitlines()

# create the data
x = np.concatenate((np.random.normal(5, 5, 1000),np.random.normal(10, 2, 1000)))

f = np.ravel(x).astype(np.float)
f=f.reshape(-1,1)
g = mixture.GaussianMixture(n_components=3,covariance_type='full')
g.fit(f)
weights = g.weights_
means = g.means_
covars = g.covariances_

plt.hist(f, bins=100, histtype='bar', density=True, ec='red', alpha=0.5)

# f_axis = f.copy().ravel()
# f_axis.sort()
# plt.plot(f_axis,weights[0]*stats.norm.pdf(f_axis,means[0],np.sqrt(covars[0])).ravel(), c='red')

plt.rcParams['agg.path.chunksize'] = 10000

plt.grid()
plt.show()

Thanks!

According to the documentation you can simply do g.sample(n_samples=64) on the fitted model to generate 64 samples