--- a
+++ b/experiments/simulations/visualize_oned_warp.py
@@ -0,0 +1,37 @@
+import numpy as np
+from scipy.stats import multivariate_normal as mvn
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.gaussian_process.kernels import RBF
+
+import matplotlib
+
+font = {"size": 30}
+matplotlib.rc("font", **font)
+matplotlib.rcParams["text.usetex"] = True
+
+
+lengthscale = 1.0
+amplitude = 1.0
+noise_stddev = 1e-6
+
+xlims = [-5, 5]
+n = 100
+X = np.linspace(xlims[0], xlims[1], n)
+X = np.expand_dims(X, 1)
+
+## Draw function
+K_XX = amplitude * RBF(length_scale=lengthscale)(X, X) + noise_stddev * np.eye(n)
+mean = X.squeeze()
+# mean = np.zeros(n)
+Y = mvn(mean, K_XX).rvs()
+
+# import ipdb; ipdb.set_trace()
+plt.figure(figsize=(7, 6))
+plt.plot(X, Y, linewidth=5)
+plt.xlabel("Observed spatial coordinate")
+plt.ylabel("Warped spatial coordinate")
+plt.title(r"$\sigma^2 = {}, \ell = {}$".format(amplitude, lengthscale))
+plt.tight_layout()
+plt.savefig("../../plots/mean_function_example.png")
+plt.show()