Fabien Péan
447b4387e9
DenseLUPP tests solves now - complex128 non-symmetric problem - complex64 non-symmetric general problem DenseLDLT solves now - float64 symmetric problem - float32 symmetric general problem
114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
#!/usr/bin/env python
|
|
|
|
import numpy as np
|
|
from pyarpack import denseLDLT 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.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
|
|
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.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])
|