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import numpy as np
import matplotlib.pyplot as plt
from otsu2018 import load_Otsu2018_spectra, Clustering
if __name__ == '__main__':
sds = load_Otsu2018_spectra('CommonData/spectrum_m.csv', every_nth=1)
wl = np.arange(380, 731, 10)
mean = np.mean(sds, axis=0)
data_matrix = sds - mean
covariance_matrix = np.dot(data_matrix.T, data_matrix)
eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix)
for i, v in enumerate(eigenvalues):
w = eigenvectors[:, i]
color = 'C%d' % (i % 10)
scale = 0.5 / np.max(np.abs(w))
baseline = i + 1
plt.plot((min(wl), max(wl)), (baseline, baseline), color + ':')
plt.plot(wl, baseline + scale * w, color + '-')
plt.annotate('λ = %g' % v, color=color, xy=(max(wl), baseline),
xytext=(max(wl), baseline + 0.05), ha='right')
plt.title('PCA decomposition')
plt.xlabel('Wavelength [nm]')
plt.ylabel('Eigenvector number')
plt.xlim(370, 740)
plt.ylim(30.5, 36.5)
plt.show()
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