37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
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import utils
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import numpy as np
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from sklearn.decomposition import FastICA
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import matplotlib.pyplot as plt
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from matplotlib.gridspec import GridSpec
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def ica(n_components, slab_width, slab_height):
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slabs, i_min, i_max = utils.load_slabs(slab_width, slab_height)
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X = utils.as_np_array(slabs).T
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n_freqs, n_pixels = X.shape[1], X.shape[0]
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p = FastICA(n_components = n_components, whiten="arbitrary-variance")
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Y = p.fit_transform(X)
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A = p.mixing_
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new_coords = [Y[:,i].reshape((slab_height, slab_width)) for i in range(n_components)]
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f = plt.figure(layout="constrained")
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gs = GridSpec(2, 3, figure=f)
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axes = [f.add_subplot(gs[0,0]), f.add_subplot(gs[1,0]),
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f.add_subplot(gs[0,1]), f.add_subplot(gs[0,2]),
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f.add_subplot(gs[1,1]), f.add_subplot(gs[1,2])
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]
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axes[0].scatter(Y[:,0], Y[:,1], s=20, alpha=0.02)
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axes[1].imshow(slabs[15]["data"], vmin=i_min, vmax=i_max)
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axes[1].set_title(f"data @ {slabs[15]["e"]}cm-1")
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for c in range(n_components):
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axes[c+2].imshow(new_coords[c])
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axes[c+2].set_title(f"Component #{c+1}")
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plt.show()
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#ica(3, 512, 512)
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