#!/usr/bin/env python import numpy as np from pyarpack import denseLUPP as pyarpackSlv # Build laplacian. n = 4 Aij = np.array([], dtype='float64') for k in range(n): for l in range(n): if l == k: Aij = np.append(Aij, np.float64( 200.)) # Casting value on append is MANDATORY or C++ won't get the expected type. elif l == k-1 or l == k+1: Aij = np.append(Aij, np.float64(-100.)) # Casting value on append is MANDATORY or C++ won't get the expected type. else: Aij = np.append(Aij, np.float64( 0.)) # Casting value on append is MANDATORY or C++ won't get the expected type. for idx, val in enumerate(Aij): print("A[", idx, "] =", val) A = (Aij, False) # raw format: Aij values, row ordered (or not). # Get and tune arpack solver. arpackSlv = pyarpackSlv.double() # Caution: double <=> np.array(..., dtype='float64') arpackSlv.verbose = 3 # Set to 0 to get a quiet solve. arpackSlv.debug = 1 # Set to 0 to get a quiet solve. arpackSlv.nbEV = 1 arpackSlv.nbCV = 2*arpackSlv.nbEV + 1 arpackSlv.mag = 'LM' arpackSlv.maxIt = 200 arpackSlv.slvPvtThd = 1.e-6 # Solve eigen problem. rc = arpackSlv.solve(A) assert rc == 0, "bad solve" rc = arpackSlv.checkEigVec(A) assert rc == 0, "bad checkEigVec" # Print out results (mode selected, eigen vectors, eigen values, ...). assert arpackSlv.nbEV == len(arpackSlv.val), "bad result" print("\nresults:\n") print("mode selected:", arpackSlv.mode) print("nb iterations:", arpackSlv.nbIt) print("Reverse Communication Interface time:", arpackSlv.rciTime, "s") for val, vec in zip(arpackSlv.val, arpackSlv.vec): print("eigen value:", val) print("eigen vector:") print(vec) ####################################################################################### print("\n##########################################################################\n") ####################################################################################### # Build laplacian. n = 8 Aij = np.array([], dtype='float32') Bij = np.array([], dtype='float32') for k in range(n): for l in range(n): if l == k: Aij = np.append(Aij, np.float32( 200.)) # Casting value on append is MANDATORY or C++ won't get the expected type. Bij = np.append(Bij, np.float32( 33.3)) # Casting value on append is MANDATORY or C++ won't get the expected type. elif l == k-1 or l == k+1: Aij = np.append(Aij, np.float32(-100.)) # Casting value on append is MANDATORY or C++ won't get the expected type. Bij = np.append(Bij, np.float32( 16.6)) # Casting value on append is MANDATORY or C++ won't get the expected type. else: Aij = np.append(Aij, np.float32( 0.)) # Casting value on append is MANDATORY or C++ won't get the expected type. Bij = np.append(Bij, np.float32( 0.)) # Casting value on append is MANDATORY or C++ won't get the expected type. for idx, val in enumerate(Aij): print("A[", idx, "] =", val) for idx, val in enumerate(Bij): print("B[", idx, "] =", val) A = (Aij, False) # raw format: Aij values, row ordered (or not). B = (Bij, True) # raw format: Bij values, row ordered (or not). # Get and tune arpack solver. arpackSlv = pyarpackSlv.float() # Caution: float <=> np.array(..., dtype='float32') arpackSlv.verbose = 3 # Set to 0 to get a quiet solve. arpackSlv.debug = 1 # Set to 0 to get a quiet solve. arpackSlv.nbEV = 2 arpackSlv.nbCV = 2*arpackSlv.nbEV + 1 arpackSlv.mag = 'LM' arpackSlv.maxIt = 200 arpackSlv.slvPvtThd = 1.e-6 arpackSlv.sigmaReal = 1 # Solve eigen problem. rc = arpackSlv.solve(A, B) assert rc == 0, "bad solve" rc = arpackSlv.checkEigVec(A, B, 1.e-2) assert rc == 0, "bad checkEigVec" # Print out results (mode selected, eigen vectors, eigen values, ...). assert arpackSlv.nbEV == len(arpackSlv.val), "bad result" print("\nresults:\n") print("mode selected:", arpackSlv.mode) print("nb iterations:", arpackSlv.nbIt) print("Reverse Communication Interface time:", arpackSlv.rciTime, "s") for val, vec in zip(arpackSlv.val, arpackSlv.vec): print("eigen value:", val) print("eigen vector:") for v in range(n): print(vec[v])