110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
#!/usr/bin/env python
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import numpy as np
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from pyarpack import denseLURR as pyarpackSlv
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# Build laplacian.
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n = 4
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Aij = np.array([], dtype='float64')
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for k in range(n):
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for l in range(n):
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if l == k:
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Aij = np.append(Aij, np.float64( 200.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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elif l == k-1 or l == k+1:
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Aij = np.append(Aij, np.float64(-100.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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else:
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Aij = np.append(Aij, np.float64( 0.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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for idx, val in enumerate(Aij):
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print("A[", idx, "] =", val)
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A = (Aij, False) # raw format: Aij values, row ordered (or not).
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# Get and tune arpack solver.
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arpackSlv = pyarpackSlv.double() # Caution: double <=> np.array(..., dtype='float64')
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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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# 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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Aij = np.array([], dtype='float32')
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Bij = np.array([], dtype='float32')
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for k in range(n):
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for l in range(n):
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if l == k:
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Aij = np.append(Aij, np.float32( 200.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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Bij = np.append(Bij, np.float32( 33.3)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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elif l == k-1 or l == k+1:
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Aij = np.append(Aij, np.float32(-100.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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Bij = np.append(Bij, np.float32( 16.6)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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else:
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Aij = np.append(Aij, np.float32( 0.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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Bij = np.append(Bij, np.float32( 0.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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for idx, val in enumerate(Aij):
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print("A[", idx, "] =", val)
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for idx, val in enumerate(Bij):
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print("B[", idx, "] =", val)
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A = (Aij, False) # raw format: Aij values, row ordered (or not).
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B = (Bij, True) # raw format: Bij values, row ordered (or not).
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# Get and tune arpack solver.
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arpackSlv = pyarpackSlv.float() # Caution: float <=> np.array(..., dtype='float32')
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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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# 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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