Generate the image shown in this section

ch-13/types_of_randomness/show_random_distributions.py

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+from matplotlib import pyplot as plt
+import numpy as np
+
+distribution_size = (2, 500)
+uniform_dist = np.random.uniform(low=-1.5, high=1.5, size=distribution_size)
+gauss_dist = np.random.normal(loc=0.0, scale=0.5, size=distribution_size)
+
+def prepare_scatter(ax, data):
+    ax.set_xlim(-1.5, 1.5)
+    ax.set_ylim(-1.5, 1.5)
+    ax.set_aspect(1)
+    ax.scatter(data[0], data[1])
+    data_sd = data.std(1)
+    data_mean = data.mean(1)
+    print(data_mean, data_sd)
+    circle = plt.Circle(data_mean, data_sd[0], color='r', fc=(1,0.6,0.6,0.3))
+    ax.add_patch(circle)
+
+fig = plt.figure()
+gs = fig.add_gridspec(2, 2)
+uniform_scatter = fig.add_subplot(gs[0, 0])
+gauss_scatter = fig.add_subplot(gs[0, 1])
+uniform_hist = fig.add_subplot(gs[1, 0], sharex = uniform_scatter)
+gauss_hist = fig.add_subplot(gs[1, 1], sharex = gauss_scatter)
+# fig, ((uniform_scatter, gauss_scatter), (uniform_hist, gauss_hist)) = plt.subplots(2, 2)
+prepare_scatter(uniform_scatter, uniform_dist)
+prepare_scatter(gauss_scatter, gauss_dist)
+
+# Plot the histograms
+bins=16
+uniform_hist.hist(uniform_dist[0], bins=bins)
+gauss_hist.hist(gauss_dist[0], bins=bins)
+# fig.tight_layout()
+
+plt.show()