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#!/usr/bin/env python
import numpy as np
from pyarpack import sparseLDLT as pyarpackSlv
# Build laplacian.
n = 4
i = np.array([], dtype='@PYINT@')
j = np.array([], dtype='@PYINT@')
Aij = np.array([], dtype='float64')
for k in range(n):
for l in [k-1, k, k+1]:
if l < 0 or l > n-1:
continue
i = np.append(i, np.@PYINT@(k)) # Casting value on append is MANDATORY or C++ won't get the expected type.
j = np.append(j, np.@PYINT@(l)) # Casting value on append is MANDATORY or C++ won't get the expected type.
if l == k:
Aij = np.append(Aij, np.float64( 200.)) # Casting value on append is MANDATORY or C++ won't get the expected type.
if 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.
for k, l, Akl in zip(i, j, Aij):
print("A[", k, ",", l, "] =", Akl)
A = (n, i, j, Aij) # coo format: dimension, i 0-based indices, j 0-based indices, Aij values.
# 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.slvOffset = 0.
arpackSlv.slvScale = 1.
arpackSlv.symPb = True
# 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
i = np.array([], dtype='@PYINT@')
j = np.array([], dtype='@PYINT@')
Aij = np.array([], dtype='float32')
Bij = np.array([], dtype='float32')
for k in range(n):
for l in [k-1, k, k+1]:
if l < 0 or l > n-1:
continue
i = np.append(i, np.@PYINT@(k+1)) # Casting value on append is MANDATORY or C++ won't get the expected type.
j = np.append(j, np.@PYINT@(l+1)) # Casting value on append is MANDATORY or C++ won't get the expected type.
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.
if 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.
for k, l, Akl in zip(i, j, Aij):
print("A[", k, ",", l, "] =", Akl)
for k, l, Bkl in zip(i, j, Bij):
print("B[", k, ",", l, "] =", Bkl)
A = (n, i, j, Aij) # coo format: dimension, i 1-based indices, j 1-based indices, Aij values.
B = (n, i, j, Bij) # coo format: dimension, i 1-based indices, j 1-based indices, Bij values.
# 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.slvOffset = 0.
arpackSlv.slvScale = 1.
arpackSlv.sigmaReal = 1
arpackSlv.symPb = True
# 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])
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