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Swap pyarpackDenseLDLT and pyarpackDenseLUPP tests

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DenseLUPP tests solves now
 - complex128 non-symmetric problem
 - complex64 non-symmetric general problem

DenseLDLT solves now
- float64 symmetric problem
- float32 symmetric general problem
This commit is contained in:
Fabien Péan
2023-09-14 12:12:59 +02:00
committed by Franck HOUSSEN
parent 12300c1d4b
commit 447b4387e9
2 changed files with 41 additions and 39 deletions
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EXAMPLES/PYARPACK/pyarpackDenseLDLT.py.in
+18 -23
View File
@@ -6,24 +6,22 @@ from pyarpack import denseLDLT as pyarpackSlv
# Build laplacian.
n = 4
Aij = np.array([], dtype='complex128')
Aij = np.array([], dtype='float64')
for k in range(n):
for l in range(n):
if l == k:
Aij = np.append(Aij, np.complex128(complex( 200., 200.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k-1:
Aij = np.append(Aij, np.complex128(complex(-101., -101.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k+1:
Aij = np.append(Aij, np.complex128(complex( -99., -99.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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.complex128(complex( 0., 0.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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.complexDouble() # Caution: complexDouble <=> np.array(..., dtype='complex128')
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
@@ -32,7 +30,7 @@ arpackSlv.mag = 'LM'
arpackSlv.maxIt = 200
arpackSlv.slvOffset = 0.
arpackSlv.slvScale = 1.
arpackSlv.symPb = False
arpackSlv.symPb = True
# Solve eigen problem.
@@ -60,22 +58,19 @@ print("\n#######################################################################
# Build laplacian.
n = 8
Aij = np.array([], dtype='complex64')
Bij = np.array([], dtype='complex64')
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.complex64(complex( 200., 200.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 33.3, 33.3))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k-1:
Aij = np.append(Aij, np.complex64(complex(-101., -101.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 16.6, 16.6))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k+1:
Aij = np.append(Aij, np.complex64(complex( -99., -99.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 16.6, 16.6))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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.complex64(complex( 0., 0.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 0., 0.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
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):
@@ -85,7 +80,7 @@ B = (Bij, True) # raw format: Bij values, row ordered (or not).
# Get and tune arpack solver.
arpackSlv = pyarpackSlv.complexFloat() # Caution: complexFloat <=> np.array(..., dtype='complex64')
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
@@ -95,7 +90,7 @@ arpackSlv.maxIt = 200
arpackSlv.slvOffset = 0.
arpackSlv.slvScale = 1.
arpackSlv.sigmaReal = 1
arpackSlv.symPb = False
arpackSlv.symPb = True
# Solve eigen problem.
EXAMPLES/PYARPACK/pyarpackDenseLUPP.py.in
+23 -16
View File
@@ -6,22 +6,24 @@ from pyarpack import denseLUPP as pyarpackSlv
# Build laplacian.
n = 4
Aij = np.array([], dtype='float64')
Aij = np.array([], dtype='complex128')
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.
Aij = np.append(Aij, np.complex128(complex( 200., 200.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k-1:
Aij = np.append(Aij, np.complex128(complex(-101., -101.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k+1:
Aij = np.append(Aij, np.complex128(complex( -99., -99.))) # 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.
Aij = np.append(Aij, np.complex128(complex( 0., 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 = pyarpackSlv.complexDouble() # Caution: complexDouble <=> np.array(..., dtype='complex128')
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
@@ -29,6 +31,7 @@ arpackSlv.nbCV = 2*arpackSlv.nbEV + 1
arpackSlv.mag = 'LM'
arpackSlv.maxIt = 200
arpackSlv.slvPvtThd = 1.e-6
arpackSlv.symPb = False
# Solve eigen problem.
@@ -56,19 +59,22 @@ print("\n#######################################################################
# Build laplacian.
n = 8
Aij = np.array([], dtype='float32')
Bij = np.array([], dtype='float32')
Aij = np.array([], dtype='complex64')
Bij = np.array([], dtype='complex64')
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.
Aij = np.append(Aij, np.complex64(complex( 200., 200.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 33.3, 33.3))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k-1:
Aij = np.append(Aij, np.complex64(complex(-101., -101.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 16.6, 16.6))) # Casting value on append is MANDATORY or C++ won't get the expected type.
elif l == k+1:
Aij = np.append(Aij, np.complex64(complex( -99., -99.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 16.6, 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.
Aij = np.append(Aij, np.complex64(complex( 0., 0.))) # Casting value on append is MANDATORY or C++ won't get the expected type.
Bij = np.append(Bij, np.complex64(complex( 0., 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):
@@ -78,7 +84,7 @@ 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 = pyarpackSlv.complexFloat() # Caution: complexFloat <=> np.array(..., dtype='complex64')
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
@@ -87,6 +93,7 @@ arpackSlv.mag = 'LM'
arpackSlv.maxIt = 200
arpackSlv.slvPvtThd = 1.e-6
arpackSlv.sigmaReal = 1
arpackSlv.symPb = False
# Solve eigen problem.