125 lines
5.2 KiB
Python
125 lines
5.2 KiB
Python
#!/usr/bin/env python
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import numpy as np
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from pyarpack import sparseQR as pyarpackSlv
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# Build laplacian.
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n = 4
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i = np.array([], dtype='@PYINT@')
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j = np.array([], dtype='@PYINT@')
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Aij = np.array([], dtype='complex128')
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for k in range(n):
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for l in [k-1, k, k+1]:
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if l < 0 or l > n-1:
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continue
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i = np.append(i, np.@PYINT@(k)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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j = np.append(j, np.@PYINT@(l)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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if l == k:
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Aij = np.append(Aij, np.complex128(np.complex( 200., 200.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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if l == k-1:
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Aij = np.append(Aij, np.complex128(np.complex(-101., -101.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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if l == k+1:
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Aij = np.append(Aij, np.complex128(np.complex( -99., -99.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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for k, l, Akl in zip(i, j, Aij):
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print("A[", k, ",", l, "] =", Akl)
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A = (n, i, j, Aij) # coo format: dimension, i 0-based indices, j 0-based indices, Aij values.
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# Get and tune arpack solver.
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arpackSlv = pyarpackSlv.complexDouble() # Caution: complexDouble <=> np.array(..., dtype='complex128')
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arpackSlv.verbose = 3 # Set to 0 to get a quiet solve.
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arpackSlv.debug = 1 # Set to 0 to get a quiet solve.
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arpackSlv.nbEV = 1
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arpackSlv.nbCV = 2*arpackSlv.nbEV + 1
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arpackSlv.mag = 'LM'
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arpackSlv.maxIt = 200
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arpackSlv.slvPvtThd = 1.e-6
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arpackSlv.symPb = False
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# Solve eigen problem.
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rc = arpackSlv.solve(A)
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assert rc == 0, "bad solve"
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rc = arpackSlv.checkEigVec(A)
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assert rc == 0, "bad checkEigVec"
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# Print out results (mode selected, eigen vectors, eigen values, ...).
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assert arpackSlv.nbEV == len(arpackSlv.val), "bad result"
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print("\nresults:\n")
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print("mode selected:", arpackSlv.mode)
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print("nb iterations:", arpackSlv.nbIt)
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print("Reverse Communication Interface time:", arpackSlv.rciTime, "s")
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for val, vec in zip(arpackSlv.val, arpackSlv.vec):
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print("eigen value:", val)
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print("eigen vector:")
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print(vec)
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#######################################################################################
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print("\n##########################################################################\n")
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#######################################################################################
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# Build laplacian.
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n = 8
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i = np.array([], dtype='@PYINT@')
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j = np.array([], dtype='@PYINT@')
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Aij = np.array([], dtype='complex64')
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Bij = np.array([], dtype='complex64')
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for k in range(n):
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for l in [k-1, k, k+1]:
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if l < 0 or l > n-1:
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continue
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i = np.append(i, np.@PYINT@(k+1)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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j = np.append(j, np.@PYINT@(l+1)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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if l == k:
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Aij = np.append(Aij, np.complex64(np.complex( 200., 200.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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Bij = np.append(Bij, np.complex64(np.complex( 33.3, 33.3))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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if l == k-1:
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Aij = np.append(Aij, np.complex64(np.complex(-101., -101.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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Bij = np.append(Bij, np.complex64(np.complex( 16.6, 16.6))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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if l == k+1:
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Aij = np.append(Aij, np.complex64(np.complex( -99., -99.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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Bij = np.append(Bij, np.complex64(np.complex( 16.6, 16.6))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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for k, l, Akl in zip(i, j, Aij):
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print("A[", k, ",", l, "] =", Akl)
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for k, l, Bkl in zip(i, j, Bij):
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print("B[", k, ",", l, "] =", Bkl)
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A = (n, i, j, Aij) # coo format: dimension, i 1-based indices, j 1-based indices, Aij values.
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B = (n, i, j, Bij) # coo format: dimension, i 1-based indices, j 1-based indices, Bij values.
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# Get and tune arpack solver.
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arpackSlv = pyarpackSlv.complexFloat() # Caution: complexFloat <=> np.array(..., dtype='complex64')
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arpackSlv.verbose = 3 # Set to 0 to get a quiet solve.
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arpackSlv.debug = 1 # Set to 0 to get a quiet solve.
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arpackSlv.nbEV = 2
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arpackSlv.nbCV = 2*arpackSlv.nbEV + 1
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arpackSlv.mag = 'LM'
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arpackSlv.maxIt = 200
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arpackSlv.slvPvtThd = 1.e-6
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arpackSlv.sigmaReal = 1
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arpackSlv.sigmaImag = 1
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arpackSlv.symPb = False
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# Solve eigen problem.
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rc = arpackSlv.solve(A, B)
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assert rc == 0, "bad solve"
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rc = arpackSlv.checkEigVec(A, B, 1.e-2)
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assert rc == 0, "bad checkEigVec"
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# Print out results (mode selected, eigen vectors, eigen values, ...).
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assert arpackSlv.nbEV == len(arpackSlv.val), "bad result"
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print("\nresults:\n")
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print("mode selected:", arpackSlv.mode)
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print("nb iterations:", arpackSlv.nbIt)
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print("Reverse Communication Interface time:", arpackSlv.rciTime, "s")
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for val, vec in zip(arpackSlv.val, arpackSlv.vec):
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print("eigen value:", val)
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print("eigen vector:")
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for v in range(n):
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print(vec[v])
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