36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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energies = [2803, 2811, 2819, 2826, 2834, 2842, 2850, 2858, 2866, 2874,
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2882, 2890, 2897, 2905, 2913, 2921, 2929, 2937, 2945, 2953,
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2961, 2969, 2977, 2985, 2993, 3001, 3009, 3018, 3026, 3034,
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3042, 3050]
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def load_slabs(width=512, height=512):
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slabs = []
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i_min, i_max = np.inf, 0
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for e in energies:
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s = np.genfromtxt(f"test_sample/HeLa_F-SRS_512x512_{e}cm-1.txt", delimiter=",")[0:height,0:width]
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slabs.append({"e":e, "data":s, "total":int(s.sum())})
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i_min, i_max = min(i_min, s.min()), max(i_max, s.max())
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return slabs, i_min, i_max
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def as_np_array(slabs):
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return np.array(list(map(lambda s:s["data"].flat, slabs)))
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def compute_errors(slabs, d=np.linalg.norm):
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n = slabs.size(1)
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errors = np.zeros(n, n)
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for i in range(n):
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for j in range(i+1, n):
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errors[i,j] = errors[j,i] = d(slabs[i] - slabs[j])
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return errors
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def scale(X):
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scaler = StandardScaler().set_output(transform="pandas")
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Y = scaler.fit_transform(X)
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print(Y.shape)
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return Y
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