<jupyter_start><jupyter_text>It would be nice to run this on a larger collection of points, but the way I have written the code, the computation explodes quickly. For visualization's sake, here's what the first two steps would look like with a larger sample size.<jupyter_code>angles=np.arange(0,6.28,1/100)
coords=[(cos(angle),sin(angle)) for angle in angles]
x_noise=np.random.random_sample(len(angles),)
y_noise=np.random.random_sample(len(angles),)
x_noise=[x/10 for x in x_noise]
y_noise=[y/10 for y in y_noise]
X=[coords[i][0]+x_noise[i] for i in range(len(angles))]
Y=[coords[i][1]+y_noise[i] for i in range(len(angles))]

plt.scatter(X,Y, s=1);
for i in range(len(angles)):
    for j in range(len(angles)):
        if (((((X[i]-X[j])**2)+((Y[i]-Y[j])**2))**(1/2))<0.06):
            pyplot.plot([X[i], X[j]], [Y[i], Y[j]], color='black')<jupyter_output><empty_output>
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