from sklearn.decomposition import PCA
from sklearn.datasets import make_swiss_roll

# Create synthetic data
X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)

# Instantiate a PCA object
pca = PCA(n_components=1)

# Fit the PCA object on the data
pca.fit(X)

# Calculate the EVA of each PC
eva = pca.explained_variance_ratio_

print(eva)