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import numpy as np |
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import scipy.sparse as sp |
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import torch |
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def scipy_to_torch_sparse(scp_matrix): |
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values = scp_matrix.data |
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indices = np.vstack((scp_matrix.row, scp_matrix.col)) |
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i = torch.LongTensor(indices) |
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v = torch.FloatTensor(values) |
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shape = scp_matrix.shape |
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sparse_tensor = torch.sparse.FloatTensor(i, v, torch.Size(shape)) |
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return sparse_tensor |
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def mOrgan(N): |
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sub = np.zeros([N, N]) |
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for i in range(0, N): |
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sub[i, i-1] = 1 |
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sub[i, (i+1)%N] = 1 |
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return sub |
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def mOrganD(N): |
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N2 = int(np.ceil(N/2)) |
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sub = np.zeros([N2, N]) |
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for i in range(0, N2): |
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if (2*i+1) == N: |
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sub[i, 2*i] = 1 |
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else: |
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sub[i, 2*i] = 1/2 |
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sub[i, 2*i+1] = 1/2 |
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return sub |
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def mOrganU(N): |
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N2 = int(np.ceil(N/2)) |
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sub = np.zeros([N, N2]) |
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for i in range(0, N): |
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if i % 2 == 0: |
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sub[i, i//2] = 1 |
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else: |
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sub[i, i//2] = 1/2 |
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sub[i, (i//2 + 1) % N2] = 1/2 |
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return sub |
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def genMatrixesLungsHeart(): |
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RLUNG = 44 |
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LLUNG = 50 |
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HEART = 26 |
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Asub1 = mOrgan(RLUNG) |
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Asub2 = mOrgan(LLUNG) |
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Asub3 = mOrgan(HEART) |
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ADsub1 = mOrgan(int(np.ceil(RLUNG / 2))) |
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ADsub2 = mOrgan(int(np.ceil(LLUNG / 2))) |
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ADsub3 = mOrgan(int(np.ceil(HEART / 2))) |
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Dsub1 = mOrganD(RLUNG) |
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Dsub2 = mOrganD(LLUNG) |
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Dsub3 = mOrganD(HEART) |
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Usub1 = mOrganU(RLUNG) |
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Usub2 = mOrganU(LLUNG) |
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Usub3 = mOrganU(HEART) |
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p1 = RLUNG |
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p2 = p1 + LLUNG |
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p3 = p2 + HEART |
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p1_ = int(np.ceil(RLUNG / 2)) |
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p2_ = p1_ + int(np.ceil(LLUNG / 2)) |
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p3_ = p2_ + int(np.ceil(HEART / 2)) |
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A = np.zeros([p3, p3]) |
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A[:p1, :p1] = Asub1 |
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A[p1:p2, p1:p2] = Asub2 |
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A[p2:p3, p2:p3] = Asub3 |
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AD = np.zeros([p3_, p3_]) |
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AD[:p1_, :p1_] = ADsub1 |
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AD[p1_:p2_, p1_:p2_] = ADsub2 |
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AD[p2_:p3_, p2_:p3_] = ADsub3 |
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D = np.zeros([p3_, p3]) |
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D[:p1_, :p1] = Dsub1 |
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D[p1_:p2_, p1:p2] = Dsub2 |
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D[p2_:p3_, p2:p3] = Dsub3 |
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U = np.zeros([p3, p3_]) |
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U[:p1, :p1_] = Usub1 |
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U[p1:p2, p1_:p2_] = Usub2 |
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U[p2:p3, p2_:p3_] = Usub3 |
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return A, AD, D, U |
