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lam, v = np.linalg.eig(cov)
print("Eigenvalues:")
print(lam)
print("Eigenvectors:")
print(v)
# Sort
eigvals, eigvects = (list(t) for t in zip(*sorted(zip(lam, v))))

# And now we can see what this looks like!
x_s = np.linspace(np.min(X_adj), np.max(X_adj), 10)

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# Calculate means and normalize
x_mean = np.mean(X)
X_adj = [x-x_mean for x in X]
y_mean = np.mean(Y)
Y_adj = [y - y_mean for y in Y]
# Caculate Covariance Matrix
cov = np.cov(X_adj, Y_adj)
print(cov)

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# Define datasets
X = [1, 1.25, 1.8, 1.9, 2.21, 2.85]
Y = [1.1, 1.14, 1.8, 1.85, 2.25, 2.9]
print(X,Y)
# Let's see what this looks like
with plt.xkcd():
    plt.scatter(X, Y)
    plt.show()

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