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LICENSE: SLEPc for Python
LICENSE: SLEPc for Python
Copyright (c) 2016, Lisandro Dalcin.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
slepc4py-3.7.0/docs/apiref/ 0000755 0001750 0000144 00000000000 12720314530 016244 5 ustar dalcinl users 0000000 0000000 slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.PEP.Scale-class.html 0000644 0001750 0000144 00000022333 12720314523 024363 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.PEP.Scale
Class Scale
PEP scaling strategy
NONE : No scaling.SCALAR : Parameter scaling.DIAGONAL : Diagonal scaling.BOTH : Both parameter and diagonal scaling.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
BOTH = 3
DIAGONAL = 2
NONE = 0
SCALAR = 1
__qualname__ = 'PEPScale'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_type_10.png 0000644 0001750 0000144 00000001564 12720314524 024335 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.BV
Class BV
a new object with type S, a subtype of T
applyMatrix (self ,
Vec x ,
Vec y )
copy (self ,
BV result =None )
create (self ,
comm =None )
destroy (self )
dot (self ,
BV Y )
dotVec (self ,
Vec v )
duplicate (self )
getColumn (self ,
int j )
getMatrix (self )
getOptionsPrefix (self )
getSizes (self )
getType (self )
insertVec (self ,
int j ,
Vec w )
insertVecs (self ,
int s ,
W ,
bool orth )
matMult (self ,
Mat A ,
BV Y =None )
matMultHermitianTranspose (self ,
Mat A ,
BV Y =None )
matProject (self ,
Mat A ,
BV Y )
multVec (self ,
alpha ,
beta ,
Vec y ,
q )
norm (self ,
norm_type =None )
normColumn (self ,
int j ,
norm_type =None )
orthogonalize (self ,
Mat R =None ,
**kargs )
orthogonalizeVec (self ,
Vec v )
restoreColumn (self ,
int j ,
Vec v )
scale (self ,
alpha )
scaleColumn (self ,
int j ,
alpha )
setActiveColumns (self ,
int l ,
int k )
setMatrix (self ,
Mat mat ,
bool indef )
setOptionsPrefix (self ,
prefix )
setOrthogonalization (self ,
type =None ,
refine =None ,
eta =None ,
block =None )
setRandom (self )
setSizes (self ,
sizes ,
m )
setSizesFromVec (self ,
Vec w ,
m )
setType (self ,
bv_type )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
applyMatrix (self ,
Vec x ,
Vec y )
Multiplies a vector with the matrix associated to the bilinear
form.
Parameters
x: Vec
The input vector.
y: Vec
The result vector.
Notes
If the bilinear form has no associated matrix this function
copies the vector.
Creates the BV object.
Parameters
comm: Comm, optional
MPI communicator; if not provided, it defaults to all
processes.
Destroys the BV object.
Overrides:
petsc4py.PETSc.Object.destroy
Computes the 'block-dot' product of two basis vectors objects.
M = Y^H*X (m_ij = y_i^H x_j) or M = Y^H*B*X
Parameters
Y: BV
Left basis vectors, can be the same as self, giving M = X^H X.
Returns
M: Mat
The resulting matrix.
Notes
This is the generalization of VecDot() for a collection of vectors, M =
Y^H*X. The result is a matrix M whose entry m_ij is equal to y_i^H x_j
(where y_i^H denotes the conjugate transpose of y_i).
X and Y can be the same object.
If a non-standard inner product has been specified with setMatrix(),
then the result is M = Y^H*B*X. In this case, both X and Y must have
the same associated matrix.
Only rows (resp. columns) of M starting from ly (resp. lx) are
computed, where ly (resp. lx) is the number of leading columns of Y
(resp. X).
Computes multiple dot products of a vector against all the column
vectors of a BV.
Parameters
v: Vec
A vector.
Returns
m: Vec
A vector with the results.
This is analogue to VecMDot(), but using BV to represent a collection
of vectors. The result is m = X^H*y, so m_i is equal to x_j^H y. Note
that here X is transposed as opposed to BVDot().
If a non-standard inner product has been specified with BVSetMatrix(),
then the result is m = X^H*B*y.
Returns the current active dimensions.
Returns
l: int
The leading number of columns.
k: int
The active number of columns.
Returns a Vec object that contains the entries of the requested column
of the basis vectors object.
Parameters
j: int
The index of the requested column.
Returns
v: Vec
The vector containing the jth column.
Notes
Modifying the returned Vec will change the BV entries as well.
Retrieves the matrix representation of the inner product.
Returns
mat: the matrix of the inner product
Gets the prefix used for searching for all BV options in the
database.
Returns
prefix: string
The prefix string set for this BV object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
getOrthogonalization (self )
Gets the orthogonalization settings from the BV object.
Returns the local and global sizes, and the number of columns.
Returns
sizes: two-tuple of int
The local and global sizes (n, N) .
m: int
The number of columns.
Gets the BV type of this object.
Returns
type: BV.Type enumerate
The inner product type currently being used.
Overrides:
petsc4py.PETSc.Object.getType
insertVec (self ,
int j ,
Vec w )
Insert a vector into the specified column.
Parameters
j: int
The column to be overwritten.
w: Vec
The vector to be copied.
insertVecs (self ,
int s ,
W ,
bool orth )
Insert a set of vectors into specified columns.
Parameters
s: int
The first column to be overwritten.
W: Vec or sequence of Vec.
Set of vectors to be copied.
orth:
Flag indicating if the vectors must be orthogonalized.
Returns
m: int
Number of linearly independent vectors.
Notes
Copies the contents of vectors W into self(:,s:s+n), where n is the
length of W. If orthogonalization flag is set then the vectors are
copied one by one then orthogonalized against the previous one. If any
are linearly dependent then it is discared and the value of m is
decreased.
matMult (self ,
Mat A ,
BV Y =None )
Computes the matrix-vector product for each column, Y = A*V.
Parameters
A: Mat
The matrix.
Returns
Y: BV
The result.
Notes
Only active columns (excluding the leading ones) are processed.
It is possible to choose whether the computation is done column by column
or using dense matrices using the options database keys:
-bv_matmult_vecs
-bv_matmult_mat
The default is bv_matmult_mat.
matMultHermitianTranspose (self ,
Mat A ,
BV Y =None )
Computes the matrix-vector product with the conjugate transpose of a
matrix for each column, Y=A^H*V.
Parameters
A: Mat
The matrix.
Returns
Y: BV
The result.
Notes
Only active columns (excluding the leading ones) are processed.
As opoosed to matMult(), this operation is always done by column by
column, with a sequence of calls to MatMultHermitianTranspose().
matProject (self ,
Mat A ,
BV Y )
Computes the projection of a matrix onto a subspace.
M = Y^H A X
Parameters
A: Mat or None
Matrix to be projected.
Y: BV
Left basis vectors, can be the same as self, giving M = X^H A X.
Returns
M: Mat
Projection of the matrix A onto the subspace.
multVec (self ,
alpha ,
beta ,
Vec y ,
q )
Computes y = beta*y + alpha*X*q.
Parameter
alpha: scalar
beta: scalar
q: scalar or sequence of scalars
Return
y: Vec
The result.
norm (self ,
norm_type =None )
Computes the matrix norm of the BV.
Parameters
norm_type: PETSC.NormType enumerate
The norm type.
Notes
All active columns (except the leading ones) are considered as a
matrix. The allowed norms are NORM_1, NORM_FROBENIUS, and
NORM_INFINITY.
This operation fails if a non-standard inner product has been specified
with BVSetMatrix().
normColumn (self ,
int j ,
norm_type =None )
Computes the matrix norm of the BV.
Parameters
j: int
Index of column.
norm_type: PETSc.NormType (int)
The norm type.
Notes
The norm of V[j] is computed (NORM_1, NORM_2, or NORM_INFINITY).
If a non-standard inner product has been specified with BVSetMatrix(),
then the returned value is sqrt(V[j]'* B*V[j])
orthogonalize (self ,
Mat R =None ,
**kargs )
Orthogonalize all columns (except leading ones),
that is, compute the QR decomposition.
Parameters
R: Mat or None
A sequential dense matrix.
Notes
The output satisfies V0 = V*R (where V0 represent the input V) and V'*V = I
orthogonalizeVec (self ,
Vec v )
Orthogonalize a vector with respect to a set of vectors.
Parameters
v: Vec
Vector to be orthogonalized, modified on return.
Returns
norm: float
The norm of the resulting vector.
lindep: boolean
Flag indicating that refinement did not improve the
quality of orthogonalization.
Notes
This function applies an orthogonal projector to project
vector v onto the orthogonal complement of the span of the
columns of the BV.
This routine does not normalize the resulting vector.
restoreColumn (self ,
int j ,
Vec v )
Restore a column obtained with BVGetColumn().
Parameters
j: int
The index of the requested column.
v: Vec
The vector obtained with BVGetColumn().
Notes
The arguments must match the corresponding call to BVGetColumn().
Multiply the entries by a scalar value.
Parameters
alpha: float
scaling factor.
Notes
All active columns (except the leading ones) are scaled.
scaleColumn (self ,
int j ,
alpha )
Scale column j by alpha
Parameters
j: int
column number to be scaled.
alpha: float
scaling factor.
setActiveColumns (self ,
int l ,
int k )
Specify the columns that will be involved in operations.
Parameters
l: int
The leading number of columns.
k: int
The active number of columns.
Sets BV options from the options database.
Notes
To see all options, run your program with the -help
Overrides:
petsc4py.PETSc.Object.setFromOptions
setMatrix (self ,
Mat mat ,
bool indef )
Sets the bilinear form to be used for inner products.
Parameters
mat: Mat, optional
The matrix of the inner product.
indef: bool, optional
Whether the matrix is indefinite
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all BV options in the
database.
Parameters
prefix: string
The prefix string to prepend to all BV option
requests.
Notes
A hyphen (- ) must NOT be given at the beginning of the
prefix name. The first character of all runtime options is
AUTOMATICALLY the hyphen.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
setOrthogonalization (self ,
type =None ,
refine =None ,
eta =None ,
block =None )
Specifies the method used for the orthogonalization of vectors
(classical or modified Gram-Schmidt with or without refinement),
and for the block-orthogonalization (simultaneous orthogonalization
of a set of vectors).
Parameters
type: BV.OrthogType enumerate, optional
The type of orthogonalization technique.
refine: BV.OrthogRefineType enumerate, optional
The type of refinement.
eta: float, optional
Parameter for selective refinement.
block: BV.OrthogBlockType enumerate, optional
The type of block orthogonalization.
Notes
The default settings work well for most problems.
The parameter eta should be a real value between 0 and
1 (or DEFAULT ). The value of eta is used only when
the refinement type is BV.OrthogRefineType.IFNEEDED .
When using several processors, BV.OrthogType.MGS is likely to
result in bad scalability.
If the method set for block orthogonalization is GS, then the
computation is done column by column with the vector orthogonalization.
Set the active columns of BV to random numbers.
Notes
All active columns (except the leading ones) are modified.
Sets the local and global sizes, and the number of columns.
Parameters
sizes: int or two-tuple of int
The global size N or a two-tuple (n, N)
with the local and global sizes.
m: int
The number of columns.
Notes
Either n or N (but not both) can be PETSc.DECIDE
or None to have it automatically set.
setSizesFromVec (self ,
Vec w ,
m )
Sets the local and global sizes, and the number of columns. Local and
global sizes are specified indirectly by passing a template vector.
Parameters
w: Vec
The template vector.
m: int
The number of columns.
Selects the type for the BV object.
Parameters
bv_type: BV.Type enumerate
The inner product type to be used.
view (self ,
Viewer viewer =None )
Prints the BV data structure.
Parameters
viewer: Viewer, optional
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
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slepc4py.lib
Package lib
Extension modules for different SLEPc configurations.
SLEPc can be configured with different options (eg. debug/optimized,
single/double precisionm, C/C++ compilers, external packages). Each
configuration variant is associated to a name, frequently available as
an environmental variable named PETSC_ARCH .
This package is a holds all the available variants of the SLEPc
extension module built agaist specific SLEPc configurations. It also
provides a convenience function using of the builtin imp module
for easily importing any of the available extension modules depending
on the value of a user-provided configuration name, the PETSC_ARCH
environmental variable, or a configuration file.
ImportSLEPc (arch =None )
getPathArchSLEPc (arch =None )
__package__ = 'slepc4py.lib'
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slepc4py.SLEPc.EPS.PowerShiftType
Class PowerShiftType
EPS Power shift type.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONSTANT = 0
RAYLEIGH = 1
WILKINSON = 2
__qualname__ = 'EPSPowerShiftType'
Inherited from object :
__class__
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slepc4py.SLEPc.EPS.Type
Class Type
EPS type
Native sparse eigensolvers.
POWER : Power Iteration, Inverse Iteration, RQI.SUBSPACE : Subspace Iteration.ARNOLDI : Arnoldi.LANCZOS : Lanczos.KRYLOVSCHUR : Krylov-Schur (default).GD : Generalized Davidson.JD : Jacobi-Davidson.RQCG : Rayleigh Quotient Conjugate Gradient.LOBPCG : Locally Optimal Block Preconditioned Conjugate Gradient.CISS : Contour Integral Spectrum Slicing.LAPACK : Wrappers to dense eigensolvers in Lapack.
Wrappers to sparse eigensolvers
(should be enabled during installation of SLEPc)
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ARNOLDI = 'arnoldi'
ARPACK = 'arpack'
BLOPEX = 'blopex'
BLZPACK = 'blzpack'
CISS = 'ciss'
FEAST = 'feast'
GD = 'gd'
JD = 'jd'
KRYLOVSCHUR = 'krylovschur'
LANCZOS = 'lanczos'
LAPACK = 'lapack'
LOBPCG = 'lobpcg'
POWER = 'power'
PRIMME = 'primme'
RQCG = 'rqcg'
SUBSPACE = 'subspace'
TRLAN = 'trlan'
__qualname__ = 'EPSType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.PEP.Basis-class.html 0000644 0001750 0000144 00000022424 12720314523 024376 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.PEP.Basis
Class Basis
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CHEBYSHEV1 = 1
CHEBYSHEV2 = 2
HERMITE = 5
LAGUERRE = 4
LEGENDRE = 3
MONOMIAL = 0
__qualname__ = 'PEPBasis'
Inherited from object :
__class__
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slepc4py
Package slepc4py
This package is an interface to SLEPc libraries.
SLEPc (the Scalable Library for Eigenvalue Problem Computations) is a
software library for the solution of large scale sparse eigenvalue
problems on parallel computers. It is an extension of PETSc and can
be used for either standard or generalized eigenproblems, with real or
complex arithmetic. It can also be used for computing a partial SVD of
a large, sparse, rectangular matrix.
Version:
3.7.0
Author:
Lisandro Dalcin
slepc4py.SLEPc Scalable Library for Eigenvalue Problem Computations.
slepc4py.lib Extension modules for different SLEPc configurations.
init (args =None ,
arch =None )
get_include ()
__credits__ = 'SLEPc Team <slepc-maint@upv.es>'
__package__ = None
init (args =None ,
arch =None )
Initialize SLEPc.
This function should be called only once, typically at
the very beginning of the bootstrap script of an application.
Parameters:
argsarch
Return the directory in the package that contains header files.
Extension modules that need to compile against slepc4py should use
this function to locate the appropriate include directory. Using
Python distutils (or perhaps NumPy distutils):
import petscc4py, slepc4py
Extension('extension_name', ...
include_dirs=[...,
petsc4py.get_include(),
slepc4py.get_include(),])
slepc4py.SLEPc.PEP
Class PEP
Basis
Conv
ConvergedReason
ErrorType
Extract
ProblemType
Refine
RefineScheme
Scale
Type
Which
a new object with type S, a subtype of T
appendOptionsPrefix (self ,
prefix )
cancelMonitor (self )
computeError (self ,
int i ,
etype =None )
create (self ,
comm =None )
destroy (self )
errorView (self ,
etype =None ,
Viewer viewer =None )
getBV (self )
getBasis (self )
getConvergenceTest (self )
getDimensions (self )
getEigenpair (self ,
int i ,
Vec Vr =None ,
Vec Vi =None )solve() .
getErrorEstimate (self ,
int i )
getLinearCompanionForm (self )
getLinearEPS (self )
getLinearExplicitMatrix (self )
getOperators (self )
getOptionsPrefix (self )
getRG (self )
getRefine (self )
getST (self )ST ) object associated to
the eigensolver object.
getScale (self ,
Vec Dl =None ,
Vec Dr =None )
getTolerances (self )
getTrackAll (self )
getType (self )
reset (self )
setBV (self ,
BV bv )
setBasis (self ,
basis )
setConvergenceTest (self ,
conv )
setDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
setInitialSpace (self ,
space )
setLinearCompanionForm (self ,
cform )
setLinearEPS (self ,
EPS eps )
setLinearExplicitMatrix (self ,
flag )
setOperators (self ,
operators )
setOptionsPrefix (self ,
prefix )
setProblemType (self ,
problem_type )
setRG (self ,
RG rg )
setRefine (self ,
ref ,
npart =None ,
tol =None ,
its =None ,
scheme =None )
setST (self ,
ST st )
setScale (self ,
scale ,
alpha =None ,
Vec Dl =None ,
Vec Dr =None ,
its =None ,
lbda =None )
setTolerances (self ,
tol =None ,
max_it =None )
setTrackAll (self ,
trackall )
setType (self ,
pep_type )
setUp (self )
solve (self )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
appendOptionsPrefix (self ,
prefix )
Appends to the prefix used for searching for all PEP options
in the database.
Parameters
prefix: string
The prefix string to prepend to all PEP option requests.
computeError (self ,
int i ,
etype =None )
Computes the error (based on the residual norm) associated with the i-th
computed eigenpair.
Parameters
i: int
Index of the solution to be considered.
etype: PEP.ErrorType enumerate
The error type to compute.
Returns
error: real
The error bound, computed in various ways from the
residual norm ||P(l)x||_2 l is the
eigenvalue and x is the eigenvector.
Notes
The index i should be a value between 0 and
nconv-1 getConverged() ).
Creates the PEP object.
Parameters
comm: Comm, optional.
MPI communicator. If not provided, it defaults to all
processes.
Destroys the PEP object.
Overrides:
petsc4py.PETSc.Object.destroy
errorView (self ,
etype =None ,
Viewer viewer =None )
Displays the errors associated with the computed solution
(as well as the eigenvalues).
Parameters
etype: PEP.ErrorType enumerate, optional
The error type to compute.
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Notes
By default, this function checks the error of all eigenpairs and prints
the eigenvalues if all of them are below the requested tolerance.
If the viewer has format ASCII_INFO_DETAIL then a table with
eigenvalues and corresponding errors is printed.
Obtain the basis vectors object associated to the eigensolver.
Returns
bv: BV
The basis vectors context.
Gets the type of polynomial basis used to
describe the polynomial eigenvalue problem.
Returns
basis: PEP.Basis enumerate
the basis that was previously set.
Gets the number of converged eigenpairs.
Returns
nconv: int
Number of converged eigenpairs.
Gets the reason why the solve() iteration was stopped.
Returns
reason: PEP.ConvergedReason enumerate
Negative value indicates diverged, positive value
converged.
Return the method used to compute the error estimate
used in the convergence test.
Returns
conv: PEP.Conv
The method used to compute the error estimate
used in the convergence test.
Gets the number of eigenvalues to compute and the dimension of
the subspace.
Returns
nev: int
Number of eigenvalues to compute.
ncv: int
Maximum dimension of the subspace to be used by the solver.
mpd: int
Maximum dimension allowed for the projected problem.
getEigenpair (self ,
int i ,
Vec Vr =None ,
Vec Vi =None )
Gets the i-th solution of the eigenproblem as computed by
solve() . The solution consists of both the eigenvalue and
the eigenvector.
Parameters
i: int
Index of the solution to be obtained.
Vr: Vec, optional
Placeholder for the returned eigenvector (real part).
Vi: Vec, optional
Placeholder for the returned eigenvector (imaginary part).
Returns
e: scalar (possibly complex)
The computed eigenvalue.
getErrorEstimate (self ,
int i )
Returns the error estimate associated to the i-th computed
eigenpair.
Parameters
i: int
Index of the solution to be considered.
Returns
error: real
Error estimate.
Gets the current iteration number. If the call to solve() is
complete, then it returns the number of iterations carried out
by the solution method.
Returns
its: int
Iteration number.
getLinearCompanionForm (self )
Returns the number of the companion form that will be used for the
linearization of a quadratic eigenproblem.
Returns
cform: integer
1 or 2 (first or second companion form).
Retrieve the eigensolver object (EPS) associated to the polynomial
eigenvalue solver.
Returns
eps: EPS
The linear eigensolver.
getLinearExplicitMatrix (self )
Returns the flag indicating if the matrices A and B for the linearization
are built explicitly.
Gets the matrices associated with the eigenvalue problem.
Returns
operators: tuple of Mat
The matrices associated with the eigensystem.
Gets the prefix used for searching for all PEP options in the
database.
Returns
prefix: string
The prefix string set for this PEP object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Gets the problem type from the PEP object.
Returns
problem_type: PEP.ProblemType enumerate
The problem type that was previously set.
Obtain the region object associated to the eigensolver.
Returns
rg: RG
The region context.
Gets the refinement strategy used by the PEP object,
and the associated parameters.
Returns
ref: PEP.Refine
The refinement type.
npart: int
The number of partitions of the communicator.
tol: real
The convergence tolerance.
its: int
The maximum number of refinement iterations.
scheme: PEP.RefineScheme
Scheme for solving linear systems
Obtain the spectral transformation (ST ) object associated to
the eigensolver object.
Returns
st: ST
The spectral transformation.
getScale (self ,
Vec Dl =None ,
Vec Dr =None )
Gets the strategy used for scaling the polynomial eigenproblem.
Parameters
Dl: Vec, optional
Placeholder for the returned left diagonal matrix.
Dr: Vec, optional
Placeholder for the returned right diagonal matrix.
Returns
scale: PEP.Scale enumerate
The scaling strategy.
alpha: real
The scaling factor.
its: integer
The number of iteration of diagonal scaling.
lbda: real
Approximation of the wanted eigenvalues (modulus).
Gets the tolerance and maximum iteration count used by the
default PEP convergence tests.
Returns
tol: float
The convergence tolerance.
max_it: int
The maximum number of iterations
Returns the flag indicating whether all residual norms must be
computed or not.
Returns
trackall: bool
Whether the solver compute all residuals or not.
Gets the PEP type of this object.
Returns
type: PEP.Type enumerate
The solver currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Returns which portion of the spectrum is to be sought.
Returns
which: PEP.Which enumerate
The portion of the spectrum to be sought by the solver.
Associates a basis vectors object to the eigensolver.
Parameters
bv: BV
The basis vectors context.
Specifies the type of polynomial basis used to
describe the polynomial eigenvalue problem.
Parameters
basis: PEP.Basis enumerate
the basis to be set.
setConvergenceTest (self ,
conv )
Specifies how to compute the error estimate
used in the convergence test.
Parameters
conv: PEP.Conv
The method used to compute the error estimate
used in the convergence test.
setDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
Sets the number of eigenvalues to compute and the dimension of
the subspace.
Parameters
nev: int, optional
Number of eigenvalues to compute.
ncv: int, optional
Maximum dimension of the subspace to be used by the
solver.
mpd: int, optional
Maximum dimension allowed for the projected problem.
Sets PEP options from the options database. This routine must
be called before setUp() if the user is to be allowed to set
the solver type.
Overrides:
petsc4py.PETSc.Object.setFromOptions
setInitialSpace (self ,
space )
Sets the initial space from which the eigensolver starts to
iterate.
Parameters
space: Vec or sequence of Vec
The initial space
setLinearCompanionForm (self ,
cform )
Choose between the two companion forms available for the linearization of
a quadratic eigenproblem.
Parameters
cform: integer
1 or 2 (first or second companion form).
setLinearEPS (self ,
EPS eps )
Associate an eigensolver object (EPS) to the polynomial eigenvalue solver.
Parameters
eps: EPS
The linear eigensolver.
setLinearExplicitMatrix (self ,
flag )
Indicate if the matrices A and B for the linearization of the problem
must be built explicitly.
Parameters
flag: boolean
boolean flag indicating if the matrices are built explicitly .
setOperators (self ,
operators )
Sets the matrices associated with the eigenvalue problem.
Parameters
operators: sequence of Mat
The matrices associated with the eigensystem.
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all PEP options in the
database.
Parameters
prefix: string
The prefix string to prepend to all PEP option requests.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
setProblemType (self ,
problem_type )
Specifies the type of the eigenvalue problem.
Parameters
problem_type: PEP.ProblemType enumerate
The problem type to be set.
Associates a region object to the eigensolver.
Parameters
rg: RG
The region context.
setRefine (self ,
ref ,
npart =None ,
tol =None ,
its =None ,
scheme =None )
Sets the refinement strategy used by the PEP object,
and the associated parameters.
Parameters
ref: PEP.Refine
The refinement type.
npart: int, optional
The number of partitions of the communicator.
tol: real, optional
The convergence tolerance.
its: int, optional
The maximum number of refinement iterations.
scheme: PEP.RefineScheme, optional
Scheme for linear system solves
Associates a spectral transformation object to the
eigensolver.
Parameters
st: ST
The spectral transformation.
setScale (self ,
scale ,
alpha =None ,
Vec Dl =None ,
Vec Dr =None ,
its =None ,
lbda =None )
Sets the scaling strategy to be used for scaling the polynomial problem
before attempting to solve.
Parameters
scale: PEP.Scale enumerate
The scaling strategy.
alpha: real, optional
The scaling factor.
Dl: Vec, optional
The left diagonal matrix.
Dr: Vec, optional
The right diagonal matrix.
its: integer, optional
The number of iteration of diagonal scaling.
lbda: real, optional
Approximation of the wanted eigenvalues (modulus).
setTolerances (self ,
tol =None ,
max_it =None )
Sets the tolerance and maximum iteration count used by the
default PEP convergence tests.
Parameters
tol: float, optional
The convergence tolerance.
max_it: int, optional
The maximum number of iterations
setTrackAll (self ,
trackall )
Specifies if the solver must compute the residual of all
approximate eigenpairs or not.
Parameters
trackall: bool
Whether compute all residuals or not.
Selects the particular solver to be used in the PEP object.
Parameters
pep_type: PEP.Type enumerate
The solver to be used.
setWhichEigenpairs (self ,
which )
Specifies which portion of the spectrum is to be sought.
Parameters
which: PEP.Which enumerate
The portion of the spectrum to be sought by the solver.
view (self ,
Viewer viewer =None )
Prints the PEP data structure.
Parameters
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
Help
API Documentation
This document contains the API (Application Programming Interface)
documentation for SLEPc for Python. Documentation for the Python
objects defined by the project is divided into separate pages for each
package, module, and class. The API documentation also includes two
pages containing information about the project as a whole: a trees
page, and an index page.
Object Documentation
Each Package Documentation page contains:
A description of the package.
A list of the modules and sub-packages contained by the
package.
A summary of the classes defined by the package.
A summary of the functions defined by the package.
A summary of the variables defined by the package.
A detailed description of each function defined by the
package.
A detailed description of each variable defined by the
package.
Each Module Documentation page contains:
A description of the module.
A summary of the classes defined by the module.
A summary of the functions defined by the module.
A summary of the variables defined by the module.
A detailed description of each function defined by the
module.
A detailed description of each variable defined by the
module.
Each Class Documentation page contains:
A class inheritance diagram.
A list of known subclasses.
A description of the class.
A summary of the methods defined by the class.
A summary of the instance variables defined by the class.
A summary of the class (static) variables defined by the
class.
A detailed description of each method defined by the
class.
A detailed description of each instance variable defined by the
class.
A detailed description of each class (static) variable defined
by the class.
Project Documentation
The Trees page contains the module and class hierarchies:
The module hierarchy lists every package and module, with
modules grouped into packages. At the top level, and within each
package, modules and sub-packages are listed alphabetically.
The class hierarchy lists every class, grouped by base
class. If a class has more than one base class, then it will be
listed under each base class. At the top level, and under each base
class, classes are listed alphabetically.
The Index page contains indices of terms and
identifiers:
The term index lists every term indexed by any object's
documentation. For each term, the index provides links to each
place where the term is indexed.
The identifier index lists the (short) name of every package,
module, class, method, function, variable, and parameter. For each
identifier, the index provides a short description, and a link to
its documentation.
The Table of Contents
The table of contents occupies the two frames on the left side of
the window. The upper-left frame displays the project
contents , and the lower-left frame displays the module
contents :
Project
API
Module
The project contents frame contains a list of all packages
and modules that are defined by the project. Clicking on an entry
will display its contents in the module contents frame. Clicking on a
special entry, labeled "Everything," will display the contents of
the entire project.
The module contents frame contains a list of every
submodule, class, type, exception, function, and variable defined by a
module or package. Clicking on an entry will display its
documentation in the API documentation frame. Clicking on the name of
the module, at the top of the frame, will display the documentation
for the module itself.
The "frames " and "no frames " buttons below the top
navigation bar can be used to control whether the table of contents is
displayed or not.
The Navigation Bar
A navigation bar is located at the top and bottom of every page.
It indicates what type of page you are currently viewing, and allows
you to go to related pages. The following table describes the labels
on the navigation bar. Note that not some labels (such as
[Parent]) are not displayed on all pages.
Label
Highlighted when...
Links to...
[Parent] (never highlighted) the parent of the current package [Package] viewing a package
the package containing the current object
[Module] viewing a module
the module containing the current object
[Class] viewing a class
the class containing the current object [Trees] viewing the trees page
the trees page [Index] viewing the index page
the index page [Help] viewing the help page
the help page
The "show private " and "hide private " buttons below
the top navigation bar can be used to control whether documentation
for private objects is displayed. Private objects are usually defined
as objects whose (short) names begin with a single underscore, but do
not end with an underscore. For example, "_x",
"__pprint", and "epydoc.epytext._tokenize"
are private objects; but "re.sub",
"__init__", and "type_" are not. However,
if a module defines the "__all__" variable, then its
contents are used to decide which objects are private.
A timestamp below the bottom navigation bar indicates when each
page was last updated.
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slepc4py.SLEPc.EPS.ErrorType
Class ErrorType
EPS error type to assess accuracy of computed solutions
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ABSOLUTE = 0
BACKWARD = 2
RELATIVE = 1
__qualname__ = 'EPSErrorType'
Inherited from object :
__class__
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slepc4py.SLEPc.MFN
Class MFN
a new object with type S, a subtype of T
appendOptionsPrefix (self ,
prefix )
cancelMonitor (self )
create (self ,
comm =None )
destroy (self )
getBV (self )
getDimensions (self )
getFN (self )
getOperator (self )
getOptionsPrefix (self )
getTolerances (self )
getType (self )
reset (self )
setBV (self ,
BV bv )
setDimensions (self ,
ncv )
setFN (self ,
FN fn )
setOperator (self ,
Mat A )
setOptionsPrefix (self ,
prefix )
setTolerances (self ,
tol =None ,
max_it =None )
setType (self ,
mfn_type )
setUp (self )
solve (self ,
Vec b ,
Vec x )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
appendOptionsPrefix (self ,
prefix )
Appends to the prefix used for searching for all MFN options
in the database.
Parameters
prefix: string
The prefix string to prepend to all MFN option requests.
Creates the MFN object.
Parameters
comm: Comm, optional.
MPI communicator. If not provided, it defaults to all
processes.
Destroys the MFN object.
Overrides:
petsc4py.PETSc.Object.destroy
Obtain the basis vector object associated to the MFN object.
Returns
bv: BV
The basis vectors context.
Gets the reason why the solve() iteration was stopped.
Returns
reason: MFN.ConvergedReason enumerate
Negative value indicates diverged, positive value
converged.
Gets the dimension of the subspace used by the solver.
Returns
ncv: int
Maximum dimension of the subspace to be used by the solver.
Obtain the math function object associated to the MFN object.
Returns
fn: FN
The math function context.
Gets the current iteration number. If the call to solve() is
complete, then it returns the number of iterations carried out
by the solution method.
Returns
its: int
Iteration number.
Gets the matrix associated with the MFN object.
Returns
A: Mat
The matrix for which the matrix function is to be computed.
Gets the prefix used for searching for all MFN options in the
database.
Returns
prefix: string
The prefix string set for this MFN object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Gets the tolerance and maximum iteration count used by the
default MFN convergence tests.
Returns
tol: float
The convergence tolerance.
max_it: int
The maximum number of iterations
Gets the MFN type of this object.
Returns
type: MFN.Type enumerate
The solver currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Associates a basis vector object to the MFN object.
Parameters
bv: BV
The basis vectors context.
Sets the dimension of the subspace to be used by the solver.
Parameters
ncv: int
Maximum dimension of the subspace to be used by the
solver.
Associates a math function object to the MFN object.
Parameters
fn: FN
The math function context.
Sets MFN options from the options database. This routine must
be called before setUp() if the user is to be allowed to set
the solver type.
Overrides:
petsc4py.PETSc.Object.setFromOptions
Sets the matrix associated with the MFN object.
Parameters
A: Mat
The problem matrix.
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all MFN options in the
database.
Parameters
prefix: string
The prefix string to prepend to all MFN option requests.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
setTolerances (self ,
tol =None ,
max_it =None )
Sets the tolerance and maximum iteration count used by the
default MFN convergence tests.
Parameters
tol: float, optional
The convergence tolerance.
max_it: int, optional
The maximum number of iterations
Selects the particular solver to be used in the MFN object.
Parameters
mfn_type: MFN.Type enumerate
The solver to be used.
solve (self ,
Vec b ,
Vec x )
Solves the matrix function problem. Given a vector b, the
vector x = f(alpha*A)*b is returned.
Parameters
b: Vec
The right hand side vector.
x: Vec
The solution.
view (self ,
Viewer viewer =None )
Prints the MFN data structure.
Parameters
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
slepc4py.SLEPc.PEP.ErrorType
Class ErrorType
PEP error type to assess accuracy of computed solutions
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ABSOLUTE = 0
BACKWARD = 2
RELATIVE = 1
__qualname__ = 'PEPErrorType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.MFN.Type-class.html 0000644 0001750 0000144 00000021450 12720314522 024247 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.MFN.Type
Class Type
MFN type
Action of a matrix function on a vector.
KRYLOV : Restarted Krylov solver.EXPOKIT : Implementation of the method in Expokit.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
EXPOKIT = 'expokit'
KRYLOV = 'krylov'
__qualname__ = 'MFNType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/epydoc.css 0000644 0001750 0000144 00000037227 12720314517 020261 0 ustar dalcinl users 0000000 0000000
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slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc-module.html 0000644 0001750 0000144 00000041033 12720314520 023065 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc
Module SLEPc
Scalable Library for Eigenvalue Problem Computations.
COMM_NULL = <petsc4py.PETSc.Comm object at 0x7f795a9b56d0>
COMM_SELF = <petsc4py.PETSc.Comm object at 0x7f795a9b5710>
COMM_WORLD = <petsc4py.PETSc.Comm object at 0x7f795a9b5750>
DECIDE = -1
DEFAULT = -2
DETERMINE = -1
__arch__ = 'arch-linux2-c-debug'
__package__ = 'slepc4py'
__pyx_capi__ = {'PySlepcBV_Get': <capsule object "BV (PyObject...
__pyx_capi__
Value:
{'PySlepcBV_Get': <capsule object "BV (PyObject *)" at 0x7f795a9ced50>,
'PySlepcBV_New': <capsule object "PyObject *(BV)" at 0x7f795a9ced20>,
'PySlepcDS_Get': <capsule object "DS (PyObject *)" at 0x7f795a9cedb0>,
'PySlepcDS_New': <capsule object "PyObject *(DS)" at 0x7f795a9ced80>,
'PySlepcEPS_Get': <capsule object "EPS (PyObject *)" at 0x7f795a9ceed,
...
slepc4py.SLEPc.BV.OrthogType
Class OrthogType
BV orthogonalization types
CGS : Classical Gram-Schmidt.MGS : Modified Gram-Schmidt.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CGS = 0
MGS = 1
__qualname__ = 'BVOrthogType'
Inherited from object :
__class__
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SLEPc for Python
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.NEP.RefineScheme-class.html 0000644 0001750 0000144 00000021770 12720314522 025672 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.NEP.RefineScheme
Class RefineScheme
Scheme for solving linear systems during iterative refinement
SCHUR : Schur complement.MBE : Mixed block elimination.EXPLICIT : Build the explicit matrix.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
EXPLICIT = 3
MBE = 2
SCHUR = 1
__qualname__ = 'NEPRefineScheme'
Inherited from object :
__class__
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slepc4py.SLEPc.DS
Class DS
MatType
StateType
Type
a new object with type S, a subtype of T
allocate (self ,
ld )
create (self ,
comm =None )
destroy (self )
getMethod (self )
getOptionsPrefix (self )
getState (self )
getType (self )
reset (self )
setCompact (self ,
comp )
setDimensions (self ,
n =None ,
m =None ,
l =None ,
k =None )
setExtraRow (self ,
ext )
setMethod (self ,
meth )
setOptionsPrefix (self ,
prefix )
setRefined (self ,
ref )
setState (self ,
state )
setType (self ,
ds_type )
truncate (self ,
n )
updateExtraRow (self )solve().
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
Allocates memory for internal storage or matrices in DS.
Parameters
ld: integer
Leading dimension (maximum allowed dimension for the
matrices, including the extra row if present).
Creates the DS object.
Parameters
comm: Comm, optional
MPI communicator; if not provided, it defaults to all
processes.
Destroys the DS object.
Overrides:
petsc4py.PETSc.Object.destroy
Gets the compact storage flag.
Returns
comp: boolean
The flag.
Returns the current dimensions.
Returns
n: int
The new size.
m: int
The new column size (only for SVD).
l: int
Number of locked (inactive) leading columns.
k: int
Intermediate dimension (e.g., position of arrow).
t: int
Truncated length.
Gets the extra row flag.
Returns
comp: boolean
The flag.
getLeadingDimension (self )
Returns the leading dimension of the allocated matrices.
Returns
ld: integer
Leading dimension (maximum allowed dimension for the matrices).
Gets the method currently used in the DS.
Returns
meth: int
Identifier of the method.
Gets the prefix used for searching for all DS options in the
database.
Returns
prefix: string
The prefix string set for this DS object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Gets the refined vectors flag.
Returns
comp: boolean
The flag.
Returns the current state.
Gets the DS type of this object.
Returns
type: DS.Type enumerate
The direct solver type currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Switch to compact storage of matrices.
Parameters
comp: boolean
A boolean flag.
Notes
Compact storage is used in some DS types such as
DS.Type.HEP when the matrix is tridiagonal. This flag
can be used to indicate whether the user provides the
matrix entries via the compact form (the tridiagonal
DS.MatType.T ) or the non-compact one (DS.MatType.A ).
The default is False .
setDimensions (self ,
n =None ,
m =None ,
l =None ,
k =None )
Resize the matrices in the DS object.
Parameters
n: int, optional
The new size.
m: int, optional
The new column size (only for SVD).
l: int, optional
Number of locked (inactive) leading columns.
k: int, optional
Intermediate dimension (e.g., position of arrow).
Notes
The internal arrays are not reallocated.
The value m is not used except in the case of DS.SVD.
Sets a flag to indicate that the matrix has one extra row.
Parameters
ext: boolean
A boolean flag.
Notes
In Krylov methods it is useful that the matrix representing
the direct solver has one extra row, i.e., has dimension (n+1)
x n. If this flag is activated, all transformations applied to
the right of the matrix also affect this additional row. In
that case, (n+1) must be less or equal than the leading
dimension.
The default is False .
Sets DS options from the options database.
Notes
To see all options, run your program with the -help
Overrides:
petsc4py.PETSc.Object.setFromOptions
Selects the method to be used to solve the problem.
Parameters
meth: int
An index indentifying the method.
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all DS options in the
database.
Parameters
prefix: string
The prefix string to prepend to all DS option
requests.
Notes
A hyphen (- ) must NOT be given at the beginning of the
prefix name. The first character of all runtime options is
AUTOMATICALLY the hyphen.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
Sets a flag to indicate that refined vectors must be computed.
Parameters
ref: boolean
A boolean flag.
Notes
Normally the vectors returned in DS.MatType.X are eigenvectors
of the projected matrix. With this flag activated, vectors()
will return the right singular vector of the smallest singular
value of matrix At-theta*I, where At is the extended (n+1)xn
matrix and theta is the Ritz value. This is used in the
refined Ritz approximation.
The default is False .
Change the state of the DS object.
Notes
The state indicates that the dense system is in an initial
state (raw), in an intermediate state (such as tridiagonal,
Hessenberg or Hessenberg-triangular), in a condensed state
(such as diagonal, Schur or generalized Schur), or in a
truncated state.
This function is normally used to return to the raw state when
the condensed structure is destroyed.
Selects the type for the DS object.
Parameters
ds_type: DS.Type enumerate
The direct solver type to be used.
Truncates the system represented in the DS object.
Parameters
n: integer
The new size.
view (self ,
Viewer viewer =None )
Prints the DS data structure.
Parameters
viewer: Viewer, optional
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
slepc4py.SLEPc.NEP.ErrorType
Class ErrorType
NEP error type to assess accuracy of computed solutions
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ABSOLUTE = 0
BACKWARD = 2
RELATIVE = 1
__qualname__ = 'NEPErrorType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.EPS.Which-class.html 0000644 0001750 0000144 00000030304 12720314521 024374 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.EPS.Which
Class Which
EPS desired piece of spectrum
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ALL = 10
LARGEST_IMAGINARY = 5
LARGEST_MAGNITUDE = 1
LARGEST_REAL = 3
SMALLEST_IMAGINARY = 6
SMALLEST_MAGNITUDE = 2
SMALLEST_REAL = 4
TARGET_IMAGINARY = 9
TARGET_MAGNITUDE = 7
TARGET_REAL = 8
USER = 11
__qualname__ = 'EPSWhich'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.NEP-class.html 0000644 0001750 0000144 00000212556 12720314522 023342 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.NEP
Class NEP
a new object with type S, a subtype of T
appendOptionsPrefix (self ,
prefix )
cancelMonitor (self )
computeError (self ,
int i ,
etype =None )
create (self ,
comm =None )
destroy (self )
errorView (self ,
etype =None ,
Viewer viewer =None )
getBV (self )
getDimensions (self )
getEigenpair (self ,
int i ,
Vec Vr =None ,
Vec Vi =None )solve() .
getErrorEstimate (self ,
int i )
getOptionsPrefix (self )
getRG (self )
getTolerances (self )
getTrackAll (self )
getType (self )
reset (self )
setBV (self ,
BV bv )
setDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
setFunction (self ,
function ,
Mat F ,
Mat P =None ,
args =None ,
kargs =None )
setInitialSpace (self ,
space )
setJacobian (self ,
jacobian ,
Mat J ,
args =None ,
kargs =None )
setOptionsPrefix (self ,
prefix )
setRG (self ,
RG rg )
setSplitOperator (self ,
A ,
f ,
structure =None )
setTolerances (self ,
tol =None ,
maxit =None )
setTrackAll (self ,
trackall )
setType (self ,
nep_type )
setUp (self )
solve (self )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
appendOptionsPrefix (self ,
prefix )
Appends to the prefix used for searching for all NEP options
in the database.
Parameters
prefix: string
The prefix string to prepend to all NEP option requests.
computeError (self ,
int i ,
etype =None )
Computes the error (based on the residual norm) associated with the i-th
computed eigenpair.
Parameters
i: int
Index of the solution to be considered.
etype: NEP.ErrorType enumerate
The error type to compute.
Returns
error: real
The error bound, computed in various ways from the residual norm
||T(lambda)x||_2 lambda is the eigenvalue and
x is the eigenvector.
Creates the NEP object.
Parameters
comm: Comm, optional.
MPI communicator. If not provided, it defaults to all
processes.
Destroys the NEP object.
Overrides:
petsc4py.PETSc.Object.destroy
errorView (self ,
etype =None ,
Viewer viewer =None )
Displays the errors associated with the computed solution
(as well as the eigenvalues).
Parameters
etype: NEP.ErrorType enumerate, optional
The error type to compute.
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Notes
By default, this function checks the error of all eigenpairs and prints
the eigenvalues if all of them are below the requested tolerance.
If the viewer has format ASCII_INFO_DETAIL then a table with
eigenvalues and corresponding errors is printed.
Obtain the basis vectors object associated to the eigensolver.
Returns
bv: BV
The basis vectors context.
Gets the number of converged eigenpairs.
Returns
nconv: int
Number of converged eigenpairs.
Gets the reason why the solve() iteration was stopped.
Returns
reason: NEP.ConvergedReason enumerate
Negative value indicates diverged, positive value
converged.
Gets the number of eigenvalues to compute and the dimension of
the subspace.
Returns
nev: int
Number of eigenvalues to compute.
ncv: int
Maximum dimension of the subspace to be used by the solver.
mpd: int
Maximum dimension allowed for the projected problem.
getEigenpair (self ,
int i ,
Vec Vr =None ,
Vec Vi =None )
Gets the i-th solution of the eigenproblem as computed by
solve() . The solution consists of both the eigenvalue and
the eigenvector.
Parameters
i: int
Index of the solution to be obtained.
Vr: Vec, optional
Placeholder for the returned eigenvector (real part).
Vi: Vec, optional
Placeholder for the returned eigenvector (imaginary part).
Returns
e: scalar (possibly complex)
The computed eigenvalue.
getErrorEstimate (self ,
int i )
Returns the error estimate associated to the i-th computed
eigenpair.
Parameters
i: int
Index of the solution to be considered.
Returns
error: real
Error estimate.
Gets the current iteration number. If the call to solve() is
complete, then it returns the number of iterations carried out
by the solution method.
Returns
its: int
Iteration number.
Gets the prefix used for searching for all NEP options in the
database.
Returns
prefix: string
The prefix string set for this NEP object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Obtain the region object associated to the eigensolver.
Returns
rg: RG
The region context.
getRIILagPreconditioner (self )
Indicates how often the preconditioner is rebuilt.
Returns
lag: int
The lag parameter.
Gets the tolerance and maximum iteration count used by the
default NEP convergence tests.
Returns
tol: float
The convergence tolerance.
maxit: int
The maximum number of iterations.
Returns the flag indicating whether all residual norms must be
computed or not.
Returns
trackall: bool
Whether the solver compute all residuals or not.
Gets the NEP type of this object.
Returns
type: NEP.Type enumerate
The solver currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Returns which portion of the spectrum is to be sought.
Returns
which: NEP.Which enumerate
The portion of the spectrum to be sought by the solver.
Associates a basis vectors object to the eigensolver.
Parameters
bv: BV
The basis vectors context.
setDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
Sets the number of eigenvalues to compute and the dimension of
the subspace.
Parameters
nev: int, optional
Number of eigenvalues to compute.
ncv: int, optional
Maximum dimension of the subspace to be used by the
solver.
mpd: int, optional
Maximum dimension allowed for the projected problem.
Sets NEP options from the options database. This routine must
be called before setUp() if the user is to be allowed to set
the solver type.
Overrides:
petsc4py.PETSc.Object.setFromOptions
setFunction (self ,
function ,
Mat F ,
Mat P =None ,
args =None ,
kargs =None )
Sets the function to compute the nonlinear Function T(lambda)
as well as the location to store the matrix.
Parameters
function:
Function evaluation routine
F: Mat
Function matrix
P: Mat
preconditioner matrix (usually same as the Function)
setInitialSpace (self ,
space )
Sets the initial space from which the eigensolver starts to
iterate.
Parameters
space: Vec or sequence of Vec
The initial space
setJacobian (self ,
jacobian ,
Mat J ,
args =None ,
kargs =None )
Sets the function to compute Jacobian T'(lambda) as well
as the location to store the matrix.
Parameters
jacobian:
Jacobian evaluation routine
J: Mat
Jacobian matrix
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all NEP options in the
database.
Parameters
prefix: string
The prefix string to prepend to all NEP option requests.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
Associates a region object to the eigensolver.
Parameters
rg: RG
The region context.
setRIILagPreconditioner (self ,
lag )
Determines when the preconditioner is rebuilt in the
nonlinear solve.
Parameters
lag: int
0 indicates NEVER rebuild, 1 means rebuild every time the Jacobian is
computed within the nonlinear iteration, 2 means every second time
the Jacobian is built, etc.
setSplitOperator (self ,
A ,
f ,
structure =None )
Sets the operator of the nonlinear eigenvalue problem
in split form.
Parameters
A: Mat or sequence of Mat
Coefficient matrices of the split form.
f: sequence of FN
Scalar functions of the split form.
structure: PETSc.Mat.Structure enumerate, optional
Structure flag for matrices.
setTolerances (self ,
tol =None ,
maxit =None )
Sets the tolerance and maximum iteration count used in convergence tests.
Parameters
tol: float, optional
The convergence tolerance.
maxit: int, optional
The maximum number of iterations.
setTrackAll (self ,
trackall )
Specifies if the solver must compute the residual of all
approximate eigenpairs or not.
Parameters
trackall: bool
Whether compute all residuals or not.
Selects the particular solver to be used in the NEP object.
Parameters
nep_type: NEP.Type enumerate
The solver to be used.
setWhichEigenpairs (self ,
which )
Specifies which portion of the spectrum is to be sought.
Parameters
which: NEP.Which enumerate
The portion of the spectrum to be sought by the solver.
view (self ,
Viewer viewer =None )
Prints the NEP data structure.
Parameters
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
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slepc4py.SLEPc.PEP.Which
Class Which
PEP desired part of spectrum
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
LARGEST_IMAGINARY = 5
LARGEST_MAGNITUDE = 1
LARGEST_REAL = 3
SMALLEST_IMAGINARY = 6
SMALLEST_MAGNITUDE = 2
SMALLEST_REAL = 4
TARGET_IMAGINARY = 9
TARGET_MAGNITUDE = 7
TARGET_REAL = 8
USER = 10
__qualname__ = 'PEPWhich'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/toc-slepc4py.lib-module.html 0000644 0001750 0000144 00000002456 12720314520 023516 0 ustar dalcinl users 0000000 0000000
lib
Module lib
Functions
ImportSLEPc getPathArchSLEPc Variables
__package__ [hide private ]
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_mattype.png 0000644 0001750 0000144 00000002335 12720314520 024530 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.NEP.Type
Class Type
NEP type
Nonlinear eigensolvers.
RII : Residual inverse iteration.SLP : Successive linear problems.NARNOLDI : Nonlinear Arnoldi.CISS : Contour integral spectrum slice.INTERPOL : Polynomial interpolation.NLEIGS : Fully rational Krylov method for nonlinear eigenproblems.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CISS = 'ciss'
INTERPOL = 'interpol'
NARNOLDI = 'narnoldi'
NLEIGS = 'nleigs'
RII = 'rii'
SLP = 'slp'
__qualname__ = 'NEPType'
Inherited from object :
__class__
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slepc4py.SLEPc.DS.MatType
Class MatType
To refer to one of the matrices stored internally in DS
A : first matrix of eigenproblem/singular value problem.B : second matrix of a generalized eigenproblem.C : third matrix of a quadratic eigenproblem.T : tridiagonal matrix.D : diagonal matrix.Q : orthogonal matrix of (right) Schur vectors.Z : orthogonal matrix of left Schur vectors.X : right eigenvectors.Y : left eigenvectors.U : left singular vectors.VT : right singular vectors.W : workspace matrix.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
A = 0
B = 1
C = 2
D = 4
Q = 5
T = 3
U = 9
VT = 10
W = 11
X = 7
Y = 8
Z = 6
__qualname__ = 'DSMatType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/toc-slepc4py.SLEPc-module.html 0000644 0001750 0000144 00000006164 12720314520 023656 0 ustar dalcinl users 0000000 0000000
SLEPc
Module SLEPc
Classes
BV DS EPS FN MFN NEP PEP RG ST SVD Sys Functions
Variables
COMM_NULL COMM_SELF COMM_WORLD DECIDE DEFAULT DETERMINE __arch__ __package__ __pyx_capi__ [hide private ]
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_type_2.png 0000644 0001750 0000144 00000001564 12720314520 024252 0 ustar dalcinl users 0000000 0000000 PNG
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Class Hierarchy
[ Module Hierarchy
| Class Hierarchy ]
Class Hierarchy
object :
The most base type
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.PEP.ConvergedReason-class.html 0000644 0001750 0000144 00000024760 12720314523 026426 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.PEP.ConvergedReason
Class ConvergedReason
PEP convergence reasons
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONVERGED_ITERATING = 0
CONVERGED_TOL = 1
CONVERGED_USER = 2
DIVERGED_BREAKDOWN = -2
DIVERGED_ITS = -1
DIVERGED_SYMMETRY_LOST = -3
ITERATING = 0
__qualname__ = 'PEPConvergedReason'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/toc-slepc4py-module.html 0000644 0001750 0000144 00000002561 12720314520 022746 0 ustar dalcinl users 0000000 0000000
slepc4py
Module slepc4py
Functions
get_include init Variables
__credits__ __package__ [hide private ]
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_sys.png 0000644 0001750 0000144 00000001610 12720314524 023662 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.SVD.Which
Class Which
SVD desired piece of spectrum
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
LARGEST = 0
SMALLEST = 1
__qualname__ = 'SVDWhich'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc._p_mem-class.html 0000644 0001750 0000144 00000020750 12720314524 024147 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc._p_mem
Class _p_mem
a new object with type S, a subtype of T
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
slepc4py.SLEPc.Sys
Class Sys
a new object with type S, a subtype of T
getVersion (type cls ,
patch =False ,
devel =False ,
date =False ,
author =False )
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
slepc4py.SLEPc.BV.BlockType
Class BlockType
BV block-orthogonalization types
GS : Gram-Schmidt.CHOL : Cholesky.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CHOL = 1
GS = 0
__qualname__ = 'BVOrthogBlockType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.NEP.ConvergedReason-class.html 0000644 0001750 0000144 00000023356 12720314522 026423 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.NEP.ConvergedReason
Class ConvergedReason
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONVERGED_ITERATING = 0
CONVERGED_TOL = 1
CONVERGED_USER = 2
DIVERGED_BREAKDOWN = -2
DIVERGED_ITS = -1
DIVERGED_LINEAR_SOLVE = -4
ITERATING = 0
__qualname__ = 'NEPConvergedReason'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/identifier-index.html 0000644 0001750 0000144 00000517563 12720314520 022401 0 ustar dalcinl users 0000000 0000000
Identifier Index
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slepc4py.SLEPc.EPS.ProblemType
Class ProblemType
EPS problem type
HEP : Hermitian eigenproblem.
NHEP : Non-Hermitian eigenproblem.
GHEP : Generalized Hermitian eigenproblem.
GNHEP : Generalized Non-Hermitian eigenproblem.
PGNHEP : Generalized Non-Hermitian eigenproblemwith positive definite B .
GHIEP : Generalized Hermitian-indefinite eigenproblem.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
GHEP = 2
GHIEP = 6
GNHEP = 4
HEP = 1
NHEP = 3
PGNHEP = 5
__qualname__ = 'EPSProblemType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.EPS.Extraction-class.html 0000644 0001750 0000144 00000025303 12720314521 025455 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.EPS.Extraction
Class Extraction
EPS extraction technique
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
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HARMONIC = 1
HARMONIC_LARGEST = 4
HARMONIC_RELATIVE = 2
HARMONIC_RIGHT = 3
REFINED = 5
REFINED_HARMONIC = 6
RITZ = 0
__qualname__ = 'EPSExtraction'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_orthogtype.png 0000644 0001750 0000144 00000003213 12720314520 025245 0 ustar dalcinl users 0000000 0000000 PNG
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Kۜ XL,=]==x2t:Ydi0~~~8ZP +*slepc4py.SLEPc.EPS.Balance
Class Balance
EPS type of balancing used for non-Hermitian problems
NONE : None.ONESIDE : One-sided eigensolver, only right eigenvectors.TWOSIDE : Two-sided eigensolver, right and left eigenvectors.USER : User-defined.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
NONE = 0
ONESIDE = 1
TWOSIDE = 2
USER = 3
__qualname__ = 'EPSBalance'
Inherited from object :
__class__
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Module Hierarchy
[ Module Hierarchy
| Class Hierarchy ]
Module Hierarchy
slepc4py This package is an interface to SLEPc libraries.
slepc4py.SLEPc Scalable Library for Eigenvalue Problem Computations.
slepc4py.lib Extension modules for different SLEPc configurations.
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_lanczosreo.png 0000644 0001750 0000144 00000004263 12720314521 025227 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.FN.CombineType
Class CombineType
FN type of combination of child functions
ADD : Addition f(x) = f1(x)+f2(x)MULTIPLY : Multiplication f(x) = f1(x)*f2(x)DIVIDE : Division f(x) = f1(x)/f2(x)COMPOSE : Composition f(x) = f2(f1(x))
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
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ADD = 0
COMPOSE = 3
DIVIDE = 2
MULTIPLY = 1
__qualname__ = 'FNCombineType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_convergedr_3.png 0000644 0001750 0000144 00000003725 12720314522 025433 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.RG.Type
Class Type
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ELLIPSE = 'ellipse'
INTERVAL = 'interval'
POLYGON = 'polygon'
RING = 'ring'
__qualname__ = 'RGType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/toc-everything.html 0000644 0001750 0000144 00000022347 12720314520 022110 0 ustar dalcinl users 0000000 0000000
Everything
Everything
All Classes
slepc4py.SLEPc.BV slepc4py.SLEPc.BV.BlockType slepc4py.SLEPc.BV.OrthogType slepc4py.SLEPc.BV.RefineType slepc4py.SLEPc.BV.Type slepc4py.SLEPc.DS slepc4py.SLEPc.DS.MatType slepc4py.SLEPc.DS.StateType slepc4py.SLEPc.DS.Type slepc4py.SLEPc.EPS slepc4py.SLEPc.EPS.Balance slepc4py.SLEPc.EPS.Conv slepc4py.SLEPc.EPS.ConvergedReason slepc4py.SLEPc.EPS.ErrorType slepc4py.SLEPc.EPS.Extraction slepc4py.SLEPc.EPS.LanczosReorthogType slepc4py.SLEPc.EPS.PowerShiftType slepc4py.SLEPc.EPS.ProblemType slepc4py.SLEPc.EPS.Type slepc4py.SLEPc.EPS.Which slepc4py.SLEPc.FN slepc4py.SLEPc.FN.CombineType slepc4py.SLEPc.FN.Type slepc4py.SLEPc.MFN slepc4py.SLEPc.MFN.ConvergedReason slepc4py.SLEPc.MFN.Type slepc4py.SLEPc.NEP slepc4py.SLEPc.NEP.ConvergedReason slepc4py.SLEPc.NEP.ErrorType slepc4py.SLEPc.NEP.Refine slepc4py.SLEPc.NEP.RefineScheme slepc4py.SLEPc.NEP.Type slepc4py.SLEPc.NEP.Which slepc4py.SLEPc.PEP slepc4py.SLEPc.PEP.Basis slepc4py.SLEPc.PEP.Conv slepc4py.SLEPc.PEP.ConvergedReason slepc4py.SLEPc.PEP.ErrorType slepc4py.SLEPc.PEP.Extract slepc4py.SLEPc.PEP.ProblemType slepc4py.SLEPc.PEP.Refine slepc4py.SLEPc.PEP.RefineScheme slepc4py.SLEPc.PEP.Scale slepc4py.SLEPc.PEP.Type slepc4py.SLEPc.PEP.Which slepc4py.SLEPc.RG slepc4py.SLEPc.RG.Type slepc4py.SLEPc.ST slepc4py.SLEPc.ST.MatMode slepc4py.SLEPc.ST.Type slepc4py.SLEPc.SVD slepc4py.SLEPc.SVD.ConvergedReason slepc4py.SLEPc.SVD.ErrorType slepc4py.SLEPc.SVD.Type slepc4py.SLEPc.SVD.Which slepc4py.SLEPc.Sys All Functions
slepc4py.get_include slepc4py.init slepc4py.lib.ImportSLEPc slepc4py.lib.getPathArchSLEPc All Variables
slepc4py.SLEPc.COMM_NULL slepc4py.SLEPc.COMM_SELF slepc4py.SLEPc.COMM_WORLD slepc4py.SLEPc.DECIDE slepc4py.SLEPc.DEFAULT slepc4py.SLEPc.DETERMINE slepc4py.SLEPc.__arch__ slepc4py.SLEPc.__package__ slepc4py.SLEPc.__pyx_capi__ slepc4py.__credits__ slepc4py.__package__ slepc4py.lib.__package__ [hide private ]
slepc4py-3.7.0/docs/apiref/class_hierarchy_for__p_mem.png 0000644 0001750 0000144 00000001516 12720314524 024305 0 ustar dalcinl users 0000000 0000000 PNG
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,))S ^F \ IENDB` slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.EPS.LanczosReorthogType-class.html 0000644 0001750 0000144 00000024064 12720314521 027325 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.EPS.LanczosReorthogType
Class LanczosReorthogType
EPS Lanczos reorthogonalization type
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
DELAYED = 5
FULL = 1
LOCAL = 0
PARTIAL = 4
PERIODIC = 3
SELECTIVE = 2
__qualname__ = 'EPSLanczosReorthogType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.PEP.Type-class.html 0000644 0001750 0000144 00000024331 12720314523 024255 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.PEP.Type
Class Type
PEP type
Polynomial eigensolvers.
LINEAR : Linearization via EPS.QARNOLDI : Q-Arnoldi for quadratic problems.TOAR : Two-level orthogonal Arnoldi.STOAR : Symmetric TOAR.JD : Polynomial Jacobi-Davidson.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
JD = 'jd'
LINEAR = 'linear'
QARNOLDI = 'qarnoldi'
STOAR = 'stoar'
TOAR = 'toar'
__qualname__ = 'PEPType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.SVD.Type-class.html 0000644 0001750 0000144 00000024356 12720314524 024275 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.SVD.Type
Class Type
SVD types
CROSS : Eigenproblem with the cross-product matrix.CYCLIC : Eigenproblem with the cyclic matrix.LAPACK : Wrappers to dense SVD solvers in Lapack.LANCZOS : Lanczos.TRLANCZOS : Thick-restart Lanczos.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CROSS = 'cross'
CYCLIC = 'cyclic'
LANCZOS = 'lanczos'
LAPACK = 'lapack'
TRLANCZOS = 'trlanczos'
__qualname__ = 'SVDType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.BV.Type-class.html 0000644 0001750 0000144 00000022302 12720314520 024131 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.BV.Type
Class Type
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONTIGUOUS = 'contiguous'
MAT = 'mat'
SVEC = 'svec'
VECS = 'vecs'
__qualname__ = 'BVType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.SVD-class.html 0000644 0001750 0000144 00000246240 12720314524 023353 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.SVD
Class SVD
a new object with type S, a subtype of T
appendOptionsPrefix (self ,
prefix )
cancelMonitor (self )
computeError (self ,
int i ,
etype =None )
create (self ,
comm =None )
destroy (self )
errorView (self ,
etype =None ,
Viewer viewer =None )
getBV (self )
getConverged (self )
getCrossEPS (self )EPS ) associated to the
singular value solver.
getCyclicEPS (self )EPS ) associated to the
singular value solver.
getDimensions (self )
getImplicitTranspose (self )
getOperator (self )
getOptionsPrefix (self )
getSingularTriplet (self ,
int i ,
Vec U =None ,
Vec V =None )solve() .
getTolerances (self )
getType (self )
getVectors (self ,
int i ,
Vec U ,
Vec V )solve() .
reset (self )
setBV (self ,
BV V ,
BV U =None )
setCrossEPS (self ,
EPS eps )EPS ) to the singular value
solver.
setCyclicEPS (self ,
EPS eps )EPS ) to the singular value
solver.
setCyclicExplicitMatrix (self ,
flag =True )H(A) = [ 0 A ; A^T 0
] must be computed explicitly.
setDimensions (self ,
nsv =None ,
ncv =None ,
mpd =None )
setImplicitTranspose (self ,
mode )
setInitialSpace (self ,
space )
setLanczosOneSide (self ,
flag =True )
setOperator (self ,
Mat A )
setOptionsPrefix (self ,
prefix )
setTRLanczosOneSide (self ,
flag =True )
setTolerances (self ,
tol =None ,
max_it =None )
setType (self ,
svd_type )
setUp (self )
solve (self )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
bv
max_it
tol
transpose_mode
which
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
appendOptionsPrefix (self ,
prefix )
Appends to the prefix used for searching for all SVD options
in the database.
Parameters
prefix: string
The prefix string to prepend to all SVD option requests.
computeError (self ,
int i ,
etype =None )
Computes the error (based on the residual norm) associated with the i-th
singular triplet.
Parameters
i: int
Index of the solution to be considered.
etype: SVD.ErrorType enumerate
The error type to compute.
Returns
e: real
The relative error bound, computed in various ways from the residual norm
sqrt(n1^2+n2^2) where n1 = ||A*v-sigma*u||_2 ,
n2 = ||A^T*u-sigma*v||_2 , sigma is the singular
value, u and v are the left and right singular
vectors.
Notes
The index i should be a value between 0 and
nconv-1 getConverged() ).
Creates the SVD object.
Parameters
comm: Comm, optional
MPI communicator; if not provided, it defaults to all
processes.
Destroys the SVD object.
Overrides:
petsc4py.PETSc.Object.destroy
errorView (self ,
etype =None ,
Viewer viewer =None )
Displays the errors associated with the computed solution
(as well as the eigenvalues).
Parameters
etype: SVD.ErrorType enumerate, optional
The error type to compute.
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Notes
By default, this function checks the error of all eigenpairs and prints
the eigenvalues if all of them are below the requested tolerance.
If the viewer has format ASCII_INFO_DETAIL then a table with
eigenvalues and corresponding errors is printed.
Obtain the basis vectors objects associated to the SVD object.
Returns
V: BV
The basis vectors context for right singular vectors.
U: BV
The basis vectors context for left singular vectors.
Gets the number of converged singular triplets.
Returns
nconv: int
Number of converged singular triplets.
Notes
This function should be called after solve() has finished.
Gets the reason why the solve() iteration was stopped.
Returns
reason: SVD.ConvergedReason enumerate
Negative value indicates diverged, positive value
converged.
Retrieve the eigensolver object (EPS ) associated to the
singular value solver.
Returns
eps: EPS
The eigensolver object.
Retrieve the eigensolver object (EPS ) associated to the
singular value solver.
Returns
eps: EPS
The eigensolver object.
getCyclicExplicitMatrix (self )
Returns the flag indicating if H(A) = [ 0 A ; A^T 0 ] is
built explicitly.
Returns
flag: boolean
True if H(A) is built explicitly.
Gets the number of singular values to compute and the
dimension of the subspace.
Returns
nsv: int
Number of singular values to compute.
ncv: int
Maximum dimension of the subspace to be used by the
solver.
mpd: int
Maximum dimension allowed for the projected problem.
getImplicitTranspose (self )
Gets the mode used to handle the transpose of the matrix
associated with the singular value problem.
Returns
impl: boolean
How to handle the transpose (implicitly or not).
Gets the current iteration number. If the call to solve() is
complete, then it returns the number of iterations carried out
by the solution method.
Returns
its: int
Iteration number.
Gets the matrix associated with the singular value problem.
Returns
A: Mat
The matrix associated with the singular value problem.
Gets the prefix used for searching for all SVD options in the
database.
Returns
prefix: string
The prefix string set for this SVD object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
getSingularTriplet (self ,
int i ,
Vec U =None ,
Vec V =None )
Gets the i-th triplet of the singular value decomposition as
computed by solve() . The solution consists of the singular
value and its left and right singular vectors.
Parameters
i: int
Index of the solution to be obtained.
U: Vec
Placeholder for the returned left singular vector.
V: Vec
Placeholder for the returned right singular vector.
Returns
s: float
The computed singular value.
Gets the tolerance and maximum iteration count used by the
default SVD convergence tests.
Returns
tol: float
The convergence tolerance.
max_it: int
The maximum number of iterations
Gets the SVD type of this object.
Returns
type: SVD.Type enumerate
The solver currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Gets the i-th singular value as computed by solve() .
Parameters
i: int
Index of the solution to be obtained.
Returns
s: float
The computed singular value.
getVectors (self ,
int i ,
Vec U ,
Vec V )
Gets the i-th left and right singular vectors as computed by
solve() .
Parameters
i: int
Index of the solution to be obtained.
U: Vec
Placeholder for the returned left singular vector.
V: Vec
Placeholder for the returned right singular vector.
getWhichSingularTriplets (self )
Returns which singular triplets are to be sought.
Returns
which: SVD.Which enumerate
The singular values to be sought (either largest or
smallest).
setBV (self ,
BV V ,
BV U =None )
Associates basis vectors objects to the SVD solver.
Parameters
V: BV
The basis vectors context for right singular vectors.
U: BV
The basis vectors context for left singular vectors.
setCrossEPS (self ,
EPS eps )
Associate an eigensolver object (EPS ) to the singular value
solver.
Parameters
eps: EPS
The eigensolver object.
setCyclicEPS (self ,
EPS eps )
Associate an eigensolver object (EPS ) to the singular value
solver.
Parameters
eps: EPS
The eigensolver object.
setCyclicExplicitMatrix (self ,
flag =True )
Indicate if the eigensolver operator H(A) = [ 0 A ; A^T 0
] must be computed explicitly.
Parameters
flag: boolean
True if H(A) is built explicitly.
setDimensions (self ,
nsv =None ,
ncv =None ,
mpd =None )
Sets the number of singular values to compute and the
dimension of the subspace.
Parameters
nsv: int, optional
Number of singular values to compute.
ncv: int, optional
Maximum dimension of the subspace to be used by the
solver.
mpd: int, optional
Maximum dimension allowed for the projected problem.
Notes
Use DECIDE for ncv and mpd to assign a reasonably good
value, which is dependent on the solution method.
The parameters ncv and mpd are intimately related, so that
the user is advised to set one of them at most. Normal usage
is the following:
In cases where nsv is small, the user sets ncv
(a reasonable default is 2 * nsv).
In cases where nsv is large, the user sets mpd.
The value of ncv should always be between nsv and (nsv +
mpd), typically ncv = nsv + mpd. If nsv is not too
large, mpd = nsv is a reasonable choice, otherwise a
smaller value should be used.
Sets SVD options from the options database. This routine must
be called before setUp() if the user is to be allowed to set
the solver type.
Notes
To see all options, run your program with the -help
Overrides:
petsc4py.PETSc.Object.setFromOptions
setImplicitTranspose (self ,
mode )
Indicates how to handle the transpose of the matrix
associated with the singular value problem.
Parameters
impl: boolean
How to handle the transpose (implicitly or not).
Notes
By default, the transpose of the matrix is explicitly built
(if the matrix has defined the MatTranspose operation).
If this flag is set to true, the solver does not build the
transpose, but handles it implicitly via MatMultTranspose().
setInitialSpace (self ,
space )
Sets the initial space from which the SVD solver starts to
iterate.
Parameters
space: an sequence of Vec
The initial space.
setLanczosOneSide (self ,
flag =True )
Indicate if the variant of the Lanczos method to be used is
one-sided or two-sided.
Parameters
flag: boolean
True if the method is one-sided.
Notes
By default, a two-sided variant is selected, which is
sometimes slightly more robust. However, the one-sided variant
is faster because it avoids the orthogonalization associated
to left singular vectors. It also saves the memory required
for storing such vectors.
Sets the matrix associated with the singular value problem.
Parameters
A: Mat
The matrix associated with the singular value problem.
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all SVD options in the
database.
Parameters
prefix: string
The prefix string to prepend to all SVD option
requests.
Notes
A hyphen (-) must NOT be given at the beginning of the prefix
name. The first character of all runtime options is
AUTOMATICALLY the hyphen.
For example, to distinguish between the runtime options for
two different SVD contexts, one could call:
S1.setOptionsPrefix("svd1_")
S2.setOptionsPrefix("svd2_")
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
setTRLanczosOneSide (self ,
flag =True )
Indicate if the variant of the thick-restart Lanczos method to
be used is one-sided or two-sided.
Parameters
flag: boolean
True if the method is one-sided.
Notes
By default, a two-sided variant is selected, which is
sometimes slightly more robust. However, the one-sided variant
is faster because it avoids the orthogonalization associated
to left singular vectors.
setTolerances (self ,
tol =None ,
max_it =None )
Sets the tolerance and maximum iteration count used by the
default SVD convergence tests.
Parameters
tol: float, optional
The convergence tolerance.
max_it: int, optional
The maximum number of iterations
Notes
Use DECIDE for max_it to assign a reasonably good value,
which is dependent on the solution method.
Selects the particular solver to be used in the SVD object.
Parameters
svd_type: SVD.Type enumerate
The solver to be used.
Notes
See SVD.Type for available methods. The default is CROSS.
Normally, it is best to use setFromOptions() and then set
the SVD type from the options database rather than by using
this routine. Using the options database provides the user
with maximum flexibility in evaluating the different available
methods.
Sets up all the internal data structures necessary for the
execution of the singular value solver.
Notes
This function need not be called explicitly in most cases,
since solve() calls it. It can be useful when one wants to
measure the set-up time separately from the solve time.
setWhichSingularTriplets (self ,
which )
Specifies which singular triplets are to be sought.
Parameters
which: SVD.Which enumerate
The singular values to be sought (either largest or
smallest).
view (self ,
Viewer viewer =None )
Prints the SVD data structure.
Parameters
viewer: Viewer, optional
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
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slepc4py.SLEPc.PEP.Refine
Class Refine
PEP refinement strategy
NONE : No refinement.SIMPLE : Refine eigenpairs one by one.MULTIPLE : Refine all eigenpairs simultaneously (invariant pair).
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
MULTIPLE = 2
NONE = 0
SIMPLE = 1
__qualname__ = 'PEPRefine'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_basis.png 0000644 0001750 0000144 00000001557 12720314523 024156 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.FN.Type
Class Type
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
COMBINE = 'combine'
EXP = 'exp'
INVSQRT = 'invsqrt'
LOG = 'log'
PHI = 'phi'
RATIONAL = 'rational'
SQRT = 'sqrt'
__qualname__ = 'FNType'
Inherited from object :
__class__
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slepc4py.SLEPc.PEP.Conv
Class Conv
PEP convergence test
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ABS = 0
NORM = 2
REL = 1
USER = 3
__qualname__ = 'PEPConv'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/redirect.html 0000644 0001750 0000144 00000005364 12720314524 020746 0 ustar dalcinl users 0000000 0000000 Epydoc Redirect Page
Epydoc Auto-redirect page
When javascript is enabled, this page will redirect URLs of
the form redirect.html#dotted.name to the
documentation for the object with the given fully-qualified
dotted name.
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.SVD.ConvergedReason-class.html 0000644 0001750 0000144 00000024110 12720314524 026424 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.SVD.ConvergedReason
Class ConvergedReason
SVD convergence reasons
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONVERGED_ITERATING = 0
CONVERGED_TOL = 1
CONVERGED_USER = 2
DIVERGED_BREAKDOWN = -2
DIVERGED_ITS = -1
ITERATING = 0
__qualname__ = 'SVDConvergedReason'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.NEP.Refine-class.html 0000644 0001750 0000144 00000021643 12720314522 024544 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.NEP.Refine
Class Refine
NEP refinement strategy
NONE : No refinement.SIMPLE : Refine eigenpairs one by one.MULTIPLE : Refine all eigenpairs simultaneously (invariant pair).
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
MULTIPLE = 2
NONE = 0
SIMPLE = 1
__qualname__ = 'NEPRefine'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.PEP.ProblemType-class.html 0000644 0001750 0000144 00000021746 12720314523 025605 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.PEP.ProblemType
Class ProblemType
PEP problem type
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
GENERAL = 1
GYROSCOPIC = 3
HERMITIAN = 2
__qualname__ = 'PEPProblemType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.NEP.Which-class.html 0000644 0001750 0000144 00000025271 12720314522 024377 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.NEP.Which
Class Which
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ALL = 10
LARGEST_IMAGINARY = 5
LARGEST_MAGNITUDE = 1
LARGEST_REAL = 3
SMALLEST_IMAGINARY = 6
SMALLEST_MAGNITUDE = 2
SMALLEST_REAL = 4
TARGET_IMAGINARY = 9
TARGET_MAGNITUDE = 7
TARGET_REAL = 8
USER = 11
__qualname__ = 'NEPWhich'
Inherited from object :
__class__
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slepc4py.__credits__ slepc4py-module.html#__credits__
slepc4py.get_include slepc4py-module.html#get_include
slepc4py.init slepc4py-module.html#init
slepc4py.__package__ slepc4py-module.html#__package__
slepc4py.SLEPc slepc4py.SLEPc-module.html
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slepc4py.SLEPc.COMM_NULL slepc4py.SLEPc-module.html#COMM_NULL
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slepc4py.SLEPc.DECIDE slepc4py.SLEPc-module.html#DECIDE
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slepc4py.SLEPc.DETERMINE slepc4py.SLEPc-module.html#DETERMINE
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slepc4py.SLEPc.__pyx_capi__ slepc4py.SLEPc-module.html#__pyx_capi__
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slepc4py.SLEPc.COMM_SELF slepc4py.SLEPc-module.html#COMM_SELF
slepc4py.lib slepc4py.lib-module.html
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slepc4py.lib.getPathArchSLEPc slepc4py.lib-module.html#getPathArchSLEPc
slepc4py.SLEPc.BV slepc4py.SLEPc.BV-class.html
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slepc4py.SLEPc.BV.getActiveColumns slepc4py.SLEPc.BV-class.html#getActiveColumns
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slepc4py.SLEPc.BV.scale slepc4py.SLEPc.BV-class.html#scale
slepc4py.SLEPc.BV.orthogonalizeVec slepc4py.SLEPc.BV-class.html#orthogonalizeVec
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slepc4py.SLEPc.BV.setMatrix slepc4py.SLEPc.BV-class.html#setMatrix
slepc4py.SLEPc.BV.insertVec slepc4py.SLEPc.BV-class.html#insertVec
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slepc4py.SLEPc.BV.getType slepc4py.SLEPc.BV-class.html#getType
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slepc4py.SLEPc.BV.__new__ slepc4py.SLEPc.BV-class.html#__new__
slepc4py.SLEPc.BV.destroy slepc4py.SLEPc.BV-class.html#destroy
slepc4py.SLEPc.BV.setOrthogonalization slepc4py.SLEPc.BV-class.html#setOrthogonalization
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slepc4py.SLEPc.BV.dotVec slepc4py.SLEPc.BV-class.html#dotVec
slepc4py.SLEPc.BV.dot slepc4py.SLEPc.BV-class.html#dot
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slepc4py.SLEPc.DS.setCompact slepc4py.SLEPc.DS-class.html#setCompact
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slepc4py.SLEPc.DS.getMethod slepc4py.SLEPc.DS-class.html#getMethod
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slepc4py.SLEPc.DS.destroy slepc4py.SLEPc.DS-class.html#destroy
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slepc4py.SLEPc.EPS.getRG slepc4py.SLEPc.EPS-class.html#getRG
slepc4py.SLEPc.EPS.setDS slepc4py.SLEPc.EPS-class.html#setDS
slepc4py.SLEPc.EPS.computeError slepc4py.SLEPc.EPS-class.html#computeError
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slepc4py.SLEPc.EPS.setOperators slepc4py.SLEPc.EPS-class.html#setOperators
slepc4py.SLEPc.EPS.getConvergedReason slepc4py.SLEPc.EPS-class.html#getConvergedReason
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slepc4py.SLEPc.EPS.setFromOptions slepc4py.SLEPc.EPS-class.html#setFromOptions
slepc4py.SLEPc.EPS.isGeneralized slepc4py.SLEPc.EPS-class.html#isGeneralized
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slepc4py.SLEPc.EPS.getBV slepc4py.SLEPc.EPS-class.html#getBV
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slepc4py.SLEPc.EPS.setKrylovSchurDetectZeros slepc4py.SLEPc.EPS-class.html#setKrylovSchurDetectZeros
slepc4py.SLEPc.EPS.updateKrylovSchurSubcommMats slepc4py.SLEPc.EPS-class.html#updateKrylovSchurSubcommMats
slepc4py.SLEPc.EPS.Extraction slepc4py.SLEPc.EPS.Extraction-class.html
slepc4py.SLEPc.EPS.ConvergedReason slepc4py.SLEPc.EPS.ConvergedReason-class.html
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slepc4py.SLEPc.EPS.Type slepc4py.SLEPc.EPS.Type-class.html
slepc4py.SLEPc.EPS.setLanczosReorthogType slepc4py.SLEPc.EPS-class.html#setLanczosReorthogType
slepc4py.SLEPc.EPS.getBalance slepc4py.SLEPc.EPS-class.html#getBalance
slepc4py.SLEPc.EPS.setUp slepc4py.SLEPc.EPS-class.html#setUp
slepc4py.SLEPc.EPS.getST slepc4py.SLEPc.EPS-class.html#getST
slepc4py.SLEPc.EPS.getDS slepc4py.SLEPc.EPS-class.html#getDS
slepc4py.SLEPc.EPS.getTolerances slepc4py.SLEPc.EPS-class.html#getTolerances
slepc4py.SLEPc.EPS.getOptionsPrefix slepc4py.SLEPc.EPS-class.html#getOptionsPrefix
slepc4py.SLEPc.EPS.setArnoldiDelayed slepc4py.SLEPc.EPS-class.html#setArnoldiDelayed
slepc4py.SLEPc.EPS.Conv slepc4py.SLEPc.EPS.Conv-class.html
slepc4py.SLEPc.EPS.bv slepc4py.SLEPc.EPS-class.html#bv
slepc4py.SLEPc.EPS.setTolerances slepc4py.SLEPc.EPS-class.html#setTolerances
slepc4py.SLEPc.EPS.getEigenpair slepc4py.SLEPc.EPS-class.html#getEigenpair
slepc4py.SLEPc.EPS.getKrylovSchurDimensions slepc4py.SLEPc.EPS-class.html#getKrylovSchurDimensions
slepc4py.SLEPc.EPS.getConverged slepc4py.SLEPc.EPS-class.html#getConverged
slepc4py.SLEPc.EPS.setKrylovSchurLocking slepc4py.SLEPc.EPS-class.html#setKrylovSchurLocking
slepc4py.SLEPc.EPS.getTarget slepc4py.SLEPc.EPS-class.html#getTarget
slepc4py.SLEPc.EPS.getPowerShiftType slepc4py.SLEPc.EPS-class.html#getPowerShiftType
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slepc4py.SLEPc.EPS.tol slepc4py.SLEPc.EPS-class.html#tol
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slepc4py.SLEPc.EPS.getProblemType slepc4py.SLEPc.EPS-class.html#getProblemType
slepc4py.SLEPc.EPS.getEigenvalue slepc4py.SLEPc.EPS-class.html#getEigenvalue
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slepc4py.SLEPc.EPS.Balance slepc4py.SLEPc.EPS.Balance-class.html
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slepc4py.SLEPc.EPS.getKrylovSchurSubcommPairs slepc4py.SLEPc.EPS-class.html#getKrylovSchurSubcommPairs
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slepc4py.SLEPc.EPS.getWhichEigenpairs slepc4py.SLEPc.EPS-class.html#getWhichEigenpairs
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slepc4py.SLEPc.EPS.getConvergenceTest slepc4py.SLEPc.EPS-class.html#getConvergenceTest
slepc4py.SLEPc.EPS.setTrueResidual slepc4py.SLEPc.EPS-class.html#setTrueResidual
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slepc4py.SLEPc.MFN.create slepc4py.SLEPc.MFN-class.html#create
slepc4py.SLEPc.MFN.appendOptionsPrefix slepc4py.SLEPc.MFN-class.html#appendOptionsPrefix
slepc4py.SLEPc.MFN.destroy slepc4py.SLEPc.MFN-class.html#destroy
slepc4py.SLEPc.MFN.getConvergedReason slepc4py.SLEPc.MFN-class.html#getConvergedReason
slepc4py.SLEPc.MFN.setOptionsPrefix slepc4py.SLEPc.MFN-class.html#setOptionsPrefix
slepc4py.SLEPc.MFN.setUp slepc4py.SLEPc.MFN-class.html#setUp
slepc4py.SLEPc.MFN.getType slepc4py.SLEPc.MFN-class.html#getType
slepc4py.SLEPc.MFN.getTolerances slepc4py.SLEPc.MFN-class.html#getTolerances
slepc4py.SLEPc.MFN.getDimensions slepc4py.SLEPc.MFN-class.html#getDimensions
slepc4py.SLEPc.MFN.getOptionsPrefix slepc4py.SLEPc.MFN-class.html#getOptionsPrefix
slepc4py.SLEPc.MFN.setFromOptions slepc4py.SLEPc.MFN-class.html#setFromOptions
slepc4py.SLEPc.MFN.getFN slepc4py.SLEPc.MFN-class.html#getFN
slepc4py.SLEPc.MFN.reset slepc4py.SLEPc.MFN-class.html#reset
slepc4py.SLEPc.MFN.solve slepc4py.SLEPc.MFN-class.html#solve
slepc4py.SLEPc.MFN.getBV slepc4py.SLEPc.MFN-class.html#getBV
slepc4py.SLEPc.MFN.setOperator slepc4py.SLEPc.MFN-class.html#setOperator
slepc4py.SLEPc.MFN.getIterationNumber slepc4py.SLEPc.MFN-class.html#getIterationNumber
slepc4py.SLEPc.MFN.view slepc4py.SLEPc.MFN-class.html#view
slepc4py.SLEPc.MFN.ConvergedReason slepc4py.SLEPc.MFN.ConvergedReason-class.html
slepc4py.SLEPc.MFN.ConvergedReason.DIVERGED_BREAKDOWN slepc4py.SLEPc.MFN.ConvergedReason-class.html#DIVERGED_BREAKDOWN
slepc4py.SLEPc.MFN.ConvergedReason.ITERATING slepc4py.SLEPc.MFN.ConvergedReason-class.html#ITERATING
slepc4py.SLEPc.MFN.ConvergedReason.CONVERGED_ITS slepc4py.SLEPc.MFN.ConvergedReason-class.html#CONVERGED_ITS
slepc4py.SLEPc.MFN.ConvergedReason.__qualname__ slepc4py.SLEPc.MFN.ConvergedReason-class.html#__qualname__
slepc4py.SLEPc.MFN.ConvergedReason.CONVERGED_ITERATING slepc4py.SLEPc.MFN.ConvergedReason-class.html#CONVERGED_ITERATING
slepc4py.SLEPc.MFN.ConvergedReason.DIVERGED_ITS slepc4py.SLEPc.MFN.ConvergedReason-class.html#DIVERGED_ITS
slepc4py.SLEPc.MFN.ConvergedReason.CONVERGED_TOL slepc4py.SLEPc.MFN.ConvergedReason-class.html#CONVERGED_TOL
slepc4py.SLEPc.MFN.Type slepc4py.SLEPc.MFN.Type-class.html
slepc4py.SLEPc.MFN.Type.__qualname__ slepc4py.SLEPc.MFN.Type-class.html#__qualname__
slepc4py.SLEPc.MFN.Type.KRYLOV slepc4py.SLEPc.MFN.Type-class.html#KRYLOV
slepc4py.SLEPc.MFN.Type.EXPOKIT slepc4py.SLEPc.MFN.Type-class.html#EXPOKIT
slepc4py.SLEPc.NEP slepc4py.SLEPc.NEP-class.html
slepc4py.SLEPc.NEP.setWhichEigenpairs slepc4py.SLEPc.NEP-class.html#setWhichEigenpairs
slepc4py.SLEPc.NEP.cancelMonitor slepc4py.SLEPc.NEP-class.html#cancelMonitor
slepc4py.SLEPc.NEP.getRG slepc4py.SLEPc.NEP-class.html#getRG
slepc4py.SLEPc.NEP.computeError slepc4py.SLEPc.NEP-class.html#computeError
slepc4py.SLEPc.NEP.errorView slepc4py.SLEPc.NEP-class.html#errorView
slepc4py.SLEPc.NEP.RefineScheme slepc4py.SLEPc.NEP.RefineScheme-class.html
slepc4py.SLEPc.NEP.appendOptionsPrefix slepc4py.SLEPc.NEP-class.html#appendOptionsPrefix
slepc4py.SLEPc.NEP.getConvergedReason slepc4py.SLEPc.NEP-class.html#getConvergedReason
slepc4py.SLEPc.NEP.setInitialSpace slepc4py.SLEPc.NEP-class.html#setInitialSpace
slepc4py.SLEPc.NEP.setOptionsPrefix slepc4py.SLEPc.NEP-class.html#setOptionsPrefix
slepc4py.SLEPc.NEP.getDimensions slepc4py.SLEPc.NEP-class.html#getDimensions
slepc4py.SLEPc.NEP.setFromOptions slepc4py.SLEPc.NEP-class.html#setFromOptions
slepc4py.SLEPc.NEP.getBV slepc4py.SLEPc.NEP-class.html#getBV
slepc4py.SLEPc.NEP.view slepc4py.SLEPc.NEP-class.html#view
slepc4py.SLEPc.NEP.ConvergedReason slepc4py.SLEPc.NEP.ConvergedReason-class.html
slepc4py.SLEPc.NEP.setJacobian slepc4py.SLEPc.NEP-class.html#setJacobian
slepc4py.SLEPc.NEP.Which slepc4py.SLEPc.NEP.Which-class.html
slepc4py.SLEPc.NEP.Type slepc4py.SLEPc.NEP.Type-class.html
slepc4py.SLEPc.NEP.setUp slepc4py.SLEPc.NEP-class.html#setUp
slepc4py.SLEPc.NEP.getOptionsPrefix slepc4py.SLEPc.NEP-class.html#getOptionsPrefix
slepc4py.SLEPc.NEP.Refine slepc4py.SLEPc.NEP.Refine-class.html
slepc4py.SLEPc.NEP.setTolerances slepc4py.SLEPc.NEP-class.html#setTolerances
slepc4py.SLEPc.NEP.getEigenpair slepc4py.SLEPc.NEP-class.html#getEigenpair
slepc4py.SLEPc.NEP.getConverged slepc4py.SLEPc.NEP-class.html#getConverged
slepc4py.SLEPc.NEP.setType slepc4py.SLEPc.NEP-class.html#setType
slepc4py.SLEPc.NEP.setFunction slepc4py.SLEPc.NEP-class.html#setFunction
slepc4py.SLEPc.NEP.create slepc4py.SLEPc.NEP-class.html#create
slepc4py.SLEPc.NEP.getType slepc4py.SLEPc.NEP-class.html#getType
slepc4py.SLEPc.NEP.getTrackAll slepc4py.SLEPc.NEP-class.html#getTrackAll
slepc4py.SLEPc.NEP.reset slepc4py.SLEPc.NEP-class.html#reset
slepc4py.SLEPc.NEP.ErrorType slepc4py.SLEPc.NEP.ErrorType-class.html
slepc4py.SLEPc.NEP.getIterationNumber slepc4py.SLEPc.NEP-class.html#getIterationNumber
slepc4py.SLEPc.NEP.setDimensions slepc4py.SLEPc.NEP-class.html#setDimensions
slepc4py.SLEPc.NEP.getWhichEigenpairs slepc4py.SLEPc.NEP-class.html#getWhichEigenpairs
slepc4py.SLEPc.NEP.setBV slepc4py.SLEPc.NEP-class.html#setBV
slepc4py.SLEPc.NEP.setTrackAll slepc4py.SLEPc.NEP-class.html#setTrackAll
slepc4py.SLEPc.NEP.__new__ slepc4py.SLEPc.NEP-class.html#__new__
slepc4py.SLEPc.NEP.getErrorEstimate slepc4py.SLEPc.NEP-class.html#getErrorEstimate
slepc4py.SLEPc.NEP.setRG slepc4py.SLEPc.NEP-class.html#setRG
slepc4py.SLEPc.NEP.destroy slepc4py.SLEPc.NEP-class.html#destroy
slepc4py.SLEPc.NEP.getRIILagPreconditioner slepc4py.SLEPc.NEP-class.html#getRIILagPreconditioner
slepc4py.SLEPc.NEP.getTolerances slepc4py.SLEPc.NEP-class.html#getTolerances
slepc4py.SLEPc.NEP.setSplitOperator slepc4py.SLEPc.NEP-class.html#setSplitOperator
slepc4py.SLEPc.NEP.setRIILagPreconditioner slepc4py.SLEPc.NEP-class.html#setRIILagPreconditioner
slepc4py.SLEPc.NEP.solve slepc4py.SLEPc.NEP-class.html#solve
slepc4py.SLEPc.NEP.ConvergedReason slepc4py.SLEPc.NEP.ConvergedReason-class.html
slepc4py.SLEPc.NEP.ConvergedReason.DIVERGED_BREAKDOWN slepc4py.SLEPc.NEP.ConvergedReason-class.html#DIVERGED_BREAKDOWN
slepc4py.SLEPc.NEP.ConvergedReason.CONVERGED_USER slepc4py.SLEPc.NEP.ConvergedReason-class.html#CONVERGED_USER
slepc4py.SLEPc.NEP.ConvergedReason.CONVERGED_TOL slepc4py.SLEPc.NEP.ConvergedReason-class.html#CONVERGED_TOL
slepc4py.SLEPc.NEP.ConvergedReason.ITERATING slepc4py.SLEPc.NEP.ConvergedReason-class.html#ITERATING
slepc4py.SLEPc.NEP.ConvergedReason.CONVERGED_ITERATING slepc4py.SLEPc.NEP.ConvergedReason-class.html#CONVERGED_ITERATING
slepc4py.SLEPc.NEP.ConvergedReason.DIVERGED_ITS slepc4py.SLEPc.NEP.ConvergedReason-class.html#DIVERGED_ITS
slepc4py.SLEPc.NEP.ConvergedReason.__qualname__ slepc4py.SLEPc.NEP.ConvergedReason-class.html#__qualname__
slepc4py.SLEPc.NEP.ConvergedReason.DIVERGED_LINEAR_SOLVE slepc4py.SLEPc.NEP.ConvergedReason-class.html#DIVERGED_LINEAR_SOLVE
slepc4py.SLEPc.NEP.ErrorType slepc4py.SLEPc.NEP.ErrorType-class.html
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slepc4py.SLEPc.NEP.ErrorType.BACKWARD slepc4py.SLEPc.NEP.ErrorType-class.html#BACKWARD
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slepc4py.SLEPc.NEP.Refine.MULTIPLE slepc4py.SLEPc.NEP.Refine-class.html#MULTIPLE
slepc4py.SLEPc.NEP.Refine.SIMPLE slepc4py.SLEPc.NEP.Refine-class.html#SIMPLE
slepc4py.SLEPc.NEP.Refine.__qualname__ slepc4py.SLEPc.NEP.Refine-class.html#__qualname__
slepc4py.SLEPc.NEP.RefineScheme slepc4py.SLEPc.NEP.RefineScheme-class.html
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slepc4py.SLEPc.NEP.RefineScheme.MBE slepc4py.SLEPc.NEP.RefineScheme-class.html#MBE
slepc4py.SLEPc.NEP.RefineScheme.EXPLICIT slepc4py.SLEPc.NEP.RefineScheme-class.html#EXPLICIT
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slepc4py.SLEPc.NEP.Type slepc4py.SLEPc.NEP.Type-class.html
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slepc4py.SLEPc.NEP.Type.CISS slepc4py.SLEPc.NEP.Type-class.html#CISS
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slepc4py.SLEPc.NEP.Type.RII slepc4py.SLEPc.NEP.Type-class.html#RII
slepc4py.SLEPc.NEP.Type.NLEIGS slepc4py.SLEPc.NEP.Type-class.html#NLEIGS
slepc4py.SLEPc.NEP.Which slepc4py.SLEPc.NEP.Which-class.html
slepc4py.SLEPc.NEP.Which.TARGET_MAGNITUDE slepc4py.SLEPc.NEP.Which-class.html#TARGET_MAGNITUDE
slepc4py.SLEPc.NEP.Which.LARGEST_IMAGINARY slepc4py.SLEPc.NEP.Which-class.html#LARGEST_IMAGINARY
slepc4py.SLEPc.NEP.Which.USER slepc4py.SLEPc.NEP.Which-class.html#USER
slepc4py.SLEPc.NEP.Which.SMALLEST_REAL slepc4py.SLEPc.NEP.Which-class.html#SMALLEST_REAL
slepc4py.SLEPc.NEP.Which.SMALLEST_MAGNITUDE slepc4py.SLEPc.NEP.Which-class.html#SMALLEST_MAGNITUDE
slepc4py.SLEPc.NEP.Which.SMALLEST_IMAGINARY slepc4py.SLEPc.NEP.Which-class.html#SMALLEST_IMAGINARY
slepc4py.SLEPc.NEP.Which.LARGEST_REAL slepc4py.SLEPc.NEP.Which-class.html#LARGEST_REAL
slepc4py.SLEPc.NEP.Which.ALL slepc4py.SLEPc.NEP.Which-class.html#ALL
slepc4py.SLEPc.NEP.Which.LARGEST_MAGNITUDE slepc4py.SLEPc.NEP.Which-class.html#LARGEST_MAGNITUDE
slepc4py.SLEPc.NEP.Which.__qualname__ slepc4py.SLEPc.NEP.Which-class.html#__qualname__
slepc4py.SLEPc.NEP.Which.TARGET_IMAGINARY slepc4py.SLEPc.NEP.Which-class.html#TARGET_IMAGINARY
slepc4py.SLEPc.NEP.Which.TARGET_REAL slepc4py.SLEPc.NEP.Which-class.html#TARGET_REAL
slepc4py.SLEPc.PEP slepc4py.SLEPc.PEP-class.html
slepc4py.SLEPc.PEP.setWhichEigenpairs slepc4py.SLEPc.PEP-class.html#setWhichEigenpairs
slepc4py.SLEPc.PEP.cancelMonitor slepc4py.SLEPc.PEP-class.html#cancelMonitor
slepc4py.SLEPc.PEP.getRG slepc4py.SLEPc.PEP-class.html#getRG
slepc4py.SLEPc.PEP.computeError slepc4py.SLEPc.PEP-class.html#computeError
slepc4py.SLEPc.PEP.errorView slepc4py.SLEPc.PEP-class.html#errorView
slepc4py.SLEPc.PEP.RefineScheme slepc4py.SLEPc.PEP.RefineScheme-class.html
slepc4py.SLEPc.PEP.appendOptionsPrefix slepc4py.SLEPc.PEP-class.html#appendOptionsPrefix
slepc4py.SLEPc.PEP.setOperators slepc4py.SLEPc.PEP-class.html#setOperators
slepc4py.SLEPc.PEP.getConvergedReason slepc4py.SLEPc.PEP-class.html#getConvergedReason
slepc4py.SLEPc.PEP.setInitialSpace slepc4py.SLEPc.PEP-class.html#setInitialSpace
slepc4py.SLEPc.PEP.setOptionsPrefix slepc4py.SLEPc.PEP-class.html#setOptionsPrefix
slepc4py.SLEPc.PEP.getDimensions slepc4py.SLEPc.PEP-class.html#getDimensions
slepc4py.SLEPc.PEP.setFromOptions slepc4py.SLEPc.PEP-class.html#setFromOptions
slepc4py.SLEPc.PEP.setST slepc4py.SLEPc.PEP-class.html#setST
slepc4py.SLEPc.PEP.getBV slepc4py.SLEPc.PEP-class.html#getBV
slepc4py.SLEPc.PEP.setRefine slepc4py.SLEPc.PEP-class.html#setRefine
slepc4py.SLEPc.PEP.view slepc4py.SLEPc.PEP-class.html#view
slepc4py.SLEPc.PEP.Scale slepc4py.SLEPc.PEP.Scale-class.html
slepc4py.SLEPc.PEP.ConvergedReason slepc4py.SLEPc.PEP.ConvergedReason-class.html
slepc4py.SLEPc.PEP.Which slepc4py.SLEPc.PEP.Which-class.html
slepc4py.SLEPc.PEP.Type slepc4py.SLEPc.PEP.Type-class.html
slepc4py.SLEPc.PEP.getLinearExplicitMatrix slepc4py.SLEPc.PEP-class.html#getLinearExplicitMatrix
slepc4py.SLEPc.PEP.getLinearEPS slepc4py.SLEPc.PEP-class.html#getLinearEPS
slepc4py.SLEPc.PEP.setUp slepc4py.SLEPc.PEP-class.html#setUp
slepc4py.SLEPc.PEP.getST slepc4py.SLEPc.PEP-class.html#getST
slepc4py.SLEPc.PEP.getOptionsPrefix slepc4py.SLEPc.PEP-class.html#getOptionsPrefix
slepc4py.SLEPc.PEP.setConvergenceTest slepc4py.SLEPc.PEP-class.html#setConvergenceTest
slepc4py.SLEPc.PEP.setLinearExplicitMatrix slepc4py.SLEPc.PEP-class.html#setLinearExplicitMatrix
slepc4py.SLEPc.PEP.getBasis slepc4py.SLEPc.PEP-class.html#getBasis
slepc4py.SLEPc.PEP.Extract slepc4py.SLEPc.PEP.Extract-class.html
slepc4py.SLEPc.PEP.getConvergenceTest slepc4py.SLEPc.PEP-class.html#getConvergenceTest
slepc4py.SLEPc.PEP.Refine slepc4py.SLEPc.PEP.Refine-class.html
slepc4py.SLEPc.PEP.setTolerances slepc4py.SLEPc.PEP-class.html#setTolerances
slepc4py.SLEPc.PEP.getEigenpair slepc4py.SLEPc.PEP-class.html#getEigenpair
slepc4py.SLEPc.PEP.Basis slepc4py.SLEPc.PEP.Basis-class.html
slepc4py.SLEPc.PEP.getConverged slepc4py.SLEPc.PEP-class.html#getConverged
slepc4py.SLEPc.PEP.getLinearCompanionForm slepc4py.SLEPc.PEP-class.html#getLinearCompanionForm
slepc4py.SLEPc.PEP.setType slepc4py.SLEPc.PEP-class.html#setType
slepc4py.SLEPc.PEP.getOperators slepc4py.SLEPc.PEP-class.html#getOperators
slepc4py.SLEPc.PEP.create slepc4py.SLEPc.PEP-class.html#create
slepc4py.SLEPc.PEP.getProblemType slepc4py.SLEPc.PEP-class.html#getProblemType
slepc4py.SLEPc.PEP.getType slepc4py.SLEPc.PEP-class.html#getType
slepc4py.SLEPc.PEP.getTrackAll slepc4py.SLEPc.PEP-class.html#getTrackAll
slepc4py.SLEPc.PEP.reset slepc4py.SLEPc.PEP-class.html#reset
slepc4py.SLEPc.PEP.Conv slepc4py.SLEPc.PEP.Conv-class.html
slepc4py.SLEPc.PEP.ErrorType slepc4py.SLEPc.PEP.ErrorType-class.html
slepc4py.SLEPc.PEP.getIterationNumber slepc4py.SLEPc.PEP-class.html#getIterationNumber
slepc4py.SLEPc.PEP.setDimensions slepc4py.SLEPc.PEP-class.html#setDimensions
slepc4py.SLEPc.PEP.getWhichEigenpairs slepc4py.SLEPc.PEP-class.html#getWhichEigenpairs
slepc4py.SLEPc.PEP.setBasis slepc4py.SLEPc.PEP-class.html#setBasis
slepc4py.SLEPc.PEP.setScale slepc4py.SLEPc.PEP-class.html#setScale
slepc4py.SLEPc.PEP.setBV slepc4py.SLEPc.PEP-class.html#setBV
slepc4py.SLEPc.PEP.setTrackAll slepc4py.SLEPc.PEP-class.html#setTrackAll
slepc4py.SLEPc.PEP.__new__ slepc4py.SLEPc.PEP-class.html#__new__
slepc4py.SLEPc.PEP.getErrorEstimate slepc4py.SLEPc.PEP-class.html#getErrorEstimate
slepc4py.SLEPc.PEP.setRG slepc4py.SLEPc.PEP-class.html#setRG
slepc4py.SLEPc.PEP.destroy slepc4py.SLEPc.PEP-class.html#destroy
slepc4py.SLEPc.PEP.setProblemType slepc4py.SLEPc.PEP-class.html#setProblemType
slepc4py.SLEPc.PEP.getTolerances slepc4py.SLEPc.PEP-class.html#getTolerances
slepc4py.SLEPc.PEP.ProblemType slepc4py.SLEPc.PEP.ProblemType-class.html
slepc4py.SLEPc.PEP.getScale slepc4py.SLEPc.PEP-class.html#getScale
slepc4py.SLEPc.PEP.setLinearEPS slepc4py.SLEPc.PEP-class.html#setLinearEPS
slepc4py.SLEPc.PEP.solve slepc4py.SLEPc.PEP-class.html#solve
slepc4py.SLEPc.PEP.setLinearCompanionForm slepc4py.SLEPc.PEP-class.html#setLinearCompanionForm
slepc4py.SLEPc.PEP.getRefine slepc4py.SLEPc.PEP-class.html#getRefine
slepc4py.SLEPc.PEP.Basis slepc4py.SLEPc.PEP.Basis-class.html
slepc4py.SLEPc.PEP.Basis.CHEBYSHEV1 slepc4py.SLEPc.PEP.Basis-class.html#CHEBYSHEV1
slepc4py.SLEPc.PEP.Basis.CHEBYSHEV2 slepc4py.SLEPc.PEP.Basis-class.html#CHEBYSHEV2
slepc4py.SLEPc.PEP.Basis.LEGENDRE slepc4py.SLEPc.PEP.Basis-class.html#LEGENDRE
slepc4py.SLEPc.PEP.Basis.LAGUERRE slepc4py.SLEPc.PEP.Basis-class.html#LAGUERRE
slepc4py.SLEPc.PEP.Basis.MONOMIAL slepc4py.SLEPc.PEP.Basis-class.html#MONOMIAL
slepc4py.SLEPc.PEP.Basis.HERMITE slepc4py.SLEPc.PEP.Basis-class.html#HERMITE
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slepc4py.SLEPc.SVD.ErrorType.RELATIVE slepc4py.SLEPc.SVD.ErrorType-class.html#RELATIVE
slepc4py.SLEPc.SVD.ErrorType.ABSOLUTE slepc4py.SLEPc.SVD.ErrorType-class.html#ABSOLUTE
slepc4py.SLEPc.SVD.Type slepc4py.SLEPc.SVD.Type-class.html
slepc4py.SLEPc.SVD.Type.CYCLIC slepc4py.SLEPc.SVD.Type-class.html#CYCLIC
slepc4py.SLEPc.SVD.Type.LAPACK slepc4py.SLEPc.SVD.Type-class.html#LAPACK
slepc4py.SLEPc.SVD.Type.CROSS slepc4py.SLEPc.SVD.Type-class.html#CROSS
slepc4py.SLEPc.SVD.Type.__qualname__ slepc4py.SLEPc.SVD.Type-class.html#__qualname__
slepc4py.SLEPc.SVD.Type.LANCZOS slepc4py.SLEPc.SVD.Type-class.html#LANCZOS
slepc4py.SLEPc.SVD.Type.TRLANCZOS slepc4py.SLEPc.SVD.Type-class.html#TRLANCZOS
slepc4py.SLEPc.SVD.Which slepc4py.SLEPc.SVD.Which-class.html
slepc4py.SLEPc.SVD.Which.__qualname__ slepc4py.SLEPc.SVD.Which-class.html#__qualname__
slepc4py.SLEPc.SVD.Which.SMALLEST slepc4py.SLEPc.SVD.Which-class.html#SMALLEST
slepc4py.SLEPc.SVD.Which.LARGEST slepc4py.SLEPc.SVD.Which-class.html#LARGEST
slepc4py.SLEPc.Sys slepc4py.SLEPc.Sys-class.html
slepc4py.SLEPc.Sys.__new__ slepc4py.SLEPc.Sys-class.html#__new__
slepc4py.SLEPc.Sys.getVersionInfo slepc4py.SLEPc.Sys-class.html#getVersionInfo
slepc4py.SLEPc.Sys.getVersion slepc4py.SLEPc.Sys-class.html#getVersion
slepc4py.SLEPc._p_mem slepc4py.SLEPc._p_mem-class.html
slepc4py.SLEPc._p_mem.__new__ slepc4py.SLEPc._p_mem-class.html#__new__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.DS.StateType-class.html 0000644 0001750 0000144 00000022603 12720314520 025135 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.DS.StateType
Class StateType
DS state types
RAW : Not processed yet.INTERMEDIATE : Reduced to Hessenberg or tridiagonal form (or equivalent).CONDENSED : Reduced to Schur or diagonal form (or equivalent).TRUNCATED : Condensed form truncated to a smaller size.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONDENSED = 2
INTERMEDIATE = 1
RAW = 0
TRUNCATED = 3
__qualname__ = 'DSStateType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_errortype_2.png 0000644 0001750 0000144 00000002271 12720314522 025322 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.MFN.ConvergedReason
Class ConvergedReason
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONVERGED_ITERATING = 0
CONVERGED_ITS = 3
CONVERGED_TOL = 2
DIVERGED_BREAKDOWN = -4
DIVERGED_ITS = -3
ITERATING = 0
__qualname__ = 'MFNConvergedReason'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/class_hierarchy_for_refine.png 0000644 0001750 0000144 00000001615 12720314522 024317 0 ustar dalcinl users 0000000 0000000 PNG
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slepc4py.SLEPc.EPS.ConvergedReason
Class ConvergedReason
EPS convergence reasons
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CONVERGED_ITERATING = 0
CONVERGED_TOL = 1
CONVERGED_USER = 2
DIVERGED_BREAKDOWN = -2
DIVERGED_ITS = -1
DIVERGED_SYMMETRY_LOST = -3
ITERATING = 0
__qualname__ = 'EPSConvergedReason'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.BV.RefineType-class.html 0000644 0001750 0000144 00000021752 12720314520 025272 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.BV.RefineType
Class RefineType
BV orthogonalization refinement types
IFNEEDED : Reorthogonalize if a criterion is satisfied.NEVER : Never reorthogonalize.ALWAYS : Always reorthogonalize.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ALWAYS = 2
IFNEEDED = 0
NEVER = 1
__qualname__ = 'BVOrthogRefineType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/toc.html 0000644 0001750 0000144 00000003121 12720314520 017713 0 ustar dalcinl users 0000000 0000000
Table of Contents
Table of Contents
Everything
Modules
slepc4py slepc4py.SLEPc slepc4py.lib [hide private ]
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.DS.Type-class.html 0000644 0001750 0000144 00000025216 12720314520 024137 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.DS.Type
Class Type
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
GHEP = 'ghep'
GHIEP = 'ghiep'
GNHEP = 'gnhep'
HEP = 'hep'
NEP = 'nep'
NHEP = 'nhep'
PEP = 'pep'
SVD = 'svd'
__qualname__ = 'DSType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.PEP.Extract-class.html 0000644 0001750 0000144 00000022636 12720314523 024754 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.PEP.Extract
Class Extract
Extraction strategy used to obtain eigenvectors of the PEP from the
eigenvectors of the linearization
NONE : Use the first block.NORM : Use the first or last block depending on norm of H.RESIDUAL : Use the block with smallest residual.STRUCTURED : Combine all blocks in a certain way.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
NONE = 1
NORM = 2
RESIDUAL = 3
STRUCTURED = 4
__qualname__ = 'PEPExtract'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.RG-class.html 0000644 0001750 0000144 00000102755 12720314523 023230 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.RG
Class RG
a new object with type S, a subtype of T
create (self ,
comm =None )
destroy (self )
getComplement (self )
getOptionsPrefix (self )
getType (self )
isTrivial (self )
setComplement (self ,
comp )
setOptionsPrefix (self ,
prefix )
setType (self ,
rg_type )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
Creates the RG object.
Parameters
comm: Comm, optional
MPI communicator; if not provided, it defaults to all
processes.
Destroys the RG object.
Overrides:
petsc4py.PETSc.Object.destroy
Returns the flag indicating whether the region is complemented or not.
Returns
flg: bool
Whether the region is complemented or not.
getEllipseParameters (self )
Gets the parameters that define the ellipse region.
Returns
center: float (real or complex)
The center.
radius: float
The radius.
vscale: float
The vertical scale.
getIntervalEndpoints (self )
Gets the parameters that define the interval region.
Returns
a: float
The left endpoint in the real axis.
b: float
The right endpoint in the real axis.
c: float
The upper endpoint in the imaginary axis.
d: float
The lower endpoint in the imaginary axis.
Gets the prefix used for searching for all RG options in the
database.
Returns
prefix: string
The prefix string set for this RG object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Gets the RG type of this object.
Returns
type: RG.Type enumerate
The inner product type currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Tells whether it is the trivial region (whole complex plane).
Returns
flag: boolean
True if the region is equal to the whole complex plane, e.g.,
an interval region with all four endpoints unbounded or an
ellipse with infinite radius.
setComplement (self ,
comp )
Sets a flag to indicate that the region is the complement
of the specified one.
Parameters
comp: bool
Activate/deactivate the complementation of the region.
setEllipseParameters (self ,
center ,
radius ,
vscale )
Sets the parameters defining the ellipse region.
Parameters
center: float (real or complex)
The center.
radius: float
The radius.
vscale: float
The vertical scale.
Sets RG options from the options database.
Notes
To see all options, run your program with the -help
Overrides:
petsc4py.PETSc.Object.setFromOptions
setIntervalEndpoints (self ,
a ,
b ,
c ,
d )
Sets the parameters defining the interval region.
Parameters
a: float
The left endpoint in the real axis.
b: float
The right endpoint in the real axis.
c: float
The upper endpoint in the imaginary axis.
d: float
The lower endpoint in the imaginary axis.
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all RG options in the
database.
Parameters
prefix: string
The prefix string to prepend to all RG option
requests.
Notes
A hyphen (- ) must NOT be given at the beginning of the
prefix name. The first character of all runtime options is
AUTOMATICALLY the hyphen.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
Selects the type for the RG object.
Parameters
rg_type: RG.Type enumerate
The inner product type to be used.
view (self ,
Viewer viewer =None )
Prints the RG data structure.
Parameters
viewer: Viewer, optional
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
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slepc4py.SLEPc.PEP.RefineScheme
Class RefineScheme
Scheme for solving linear systems during iterative refinement
SCHUR : Schur complement.MBE : Mixed block elimination.EXPLICIT : Build the explicit matrix.
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
EXPLICIT = 3
MBE = 2
SCHUR = 1
__qualname__ = 'PEPRefineScheme'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.ST.Type-class.html 0000644 0001750 0000144 00000024174 12720314524 024165 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.ST.Type
Class Type
ST types
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
CAYLEY = 'cayley'
PRECOND = 'precond'
SHELL = 'shell'
SHIFT = 'shift'
SINVERT = 'sinvert'
__qualname__ = 'STType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.EPS-class.html 0000644 0001750 0000144 00000475654 12720314521 023360 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.EPS
Class EPS
a new object with type S, a subtype of T
appendOptionsPrefix (self ,
prefix )
cancelMonitor (self )
computeError (self ,
int i ,
etype =None )
create (self ,
comm =None )
destroy (self )
errorView (self ,
etype =None ,
Viewer viewer =None )
getArnoldiDelayed (self )
getBV (self )
getBalance (self )
getConvergenceTest (self )
getDS (self )
getDimensions (self )
getEigenpair (self ,
int i ,
Vec Vr =None ,
Vec Vi =None )solve() .
getErrorEstimate (self ,
int i )
getExtraction (self )
getInterval (self )
getKrylovSchurDimensions (self )
getKrylovSchurSubcommInfo (self )
getKrylovSchurSubcommMats (self )
getKrylovSchurSubcommPairs (self ,
int i ,
Vec V )
getOperators (self )
getOptionsPrefix (self )
getRG (self )
getRQCGReset (self )
getST (self )ST ) object associated to
the eigensolver object.
getTarget (self )
getTolerances (self )
getTrackAll (self )
getTrueResidual (self )
getType (self )
isGeneralized (self )
isHermitian (self )
isPositive (self )
reset (self )
setArnoldiDelayed (self ,
delayed )
setBV (self ,
BV bv )
setBalance (self ,
balance =None ,
iterations =None ,
cutoff =None )
setConvergenceTest (self ,
conv )
setDS (self ,
DS ds )
setDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
setExtraction (self ,
extraction )
setInitialSpace (self ,
space )
setInterval (self ,
inta ,
intb )
setKrylovSchurDetectZeros (self ,
detect )
setKrylovSchurDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
setKrylovSchurLocking (self ,
lock )
setKrylovSchurPartitions (self ,
npart )
setKrylovSchurRestart (self ,
keep )
setLanczosReorthogType (self ,
reorthog )
setOperators (self ,
Mat A ,
Mat B =None )
setOptionsPrefix (self ,
prefix )
setPowerShiftType (self ,
shift )
setProblemType (self ,
problem_type )
setRG (self ,
RG rg )
setRQCGReset (self ,
nrest )
setST (self ,
ST st )
setTarget (self ,
target )
setTolerances (self ,
tol =None ,
max_it =None )
setTrackAll (self ,
trackall )
setTrueResidual (self ,
trueres )
setType (self ,
eps_type )
setUp (self )
solve (self )
updateKrylovSchurSubcommMats (self ,
s =1.0 ,
a =1.0 ,
Mat Au =None ,
t =1.0 ,
b =1.0 ,
Mat Bu =None ,
structure =None ,
globalup =False )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
bv
extraction
max_it
problem_type
st
target
tol
which
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
appendOptionsPrefix (self ,
prefix )
Appends to the prefix used for searching for all EPS options
in the database.
Parameters
prefix: string
The prefix string to prepend to all EPS option requests.
computeError (self ,
int i ,
etype =None )
Computes the error (based on the residual norm) associated with the i-th
computed eigenpair.
Parameters
i: int
Index of the solution to be considered.
etype: EPS.ErrorType enumerate
The error type to compute.
Returns
e: real
The error bound, computed in various ways from the residual norm
||Ax-kBx||_2 k is the eigenvalue and
x is the eigenvector.
Notes
The index i should be a value between 0 and
nconv-1 getConverged() ).
Creates the EPS object.
Parameters
comm: MPI_Comm, optional
MPI communicator; if not provided, it defaults to all
processes.
Destroys the EPS object.
Overrides:
petsc4py.PETSc.Object.destroy
errorView (self ,
etype =None ,
Viewer viewer =None )
Displays the errors associated with the computed solution
(as well as the eigenvalues).
Parameters
etype: EPS.ErrorType enumerate, optional
The error type to compute.
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Notes
By default, this function checks the error of all eigenpairs and prints
the eigenvalues if all of them are below the requested tolerance.
If the viewer has format ASCII_INFO_DETAIL then a table with
eigenvalues and corresponding errors is printed.
Gets the type of reorthogonalization used during the Arnoldi
iteration.
Returns
delayed: boolean
True if delayed reorthogonalization is to be used.
Obtain the basis vector objects associated to the eigensolver.
Returns
bv: BV
The basis vectors context.
Gets the balancing type used by the EPS object,
and the associated parameters.
Returns
balance: EPS.Balance enumerate
The balancing method
iterations: integer
Number of iterations of the balancing algorithm
cutoff: real
Cutoff value
Gets the number of converged eigenpairs.
Returns
nconv: int
Number of converged eigenpairs.
Notes
This function should be called after solve() has finished.
Gets the reason why the solve() iteration was stopped.
Returns
reason: EPS.ConvergedReason enumerate
Negative value indicates diverged, positive value
converged.
Return the method used to compute the error estimate
used in the convergence test.
Returns
conv: EPS.Conv
The method used to compute the error estimate
used in the convergence test.
Obtain the direct solver associated to the eigensolver.
Returns
ds: DS
The direct solver context.
Gets the number of eigenvalues to compute and the dimension of
the subspace.
Returns
nev: int
Number of eigenvalues to compute.
ncv: int
Maximum dimension of the subspace to be used by the
solver.
mpd: int
Maximum dimension allowed for the projected problem.
getEigenpair (self ,
int i ,
Vec Vr =None ,
Vec Vi =None )
Gets the i-th solution of the eigenproblem as computed by
solve() . The solution consists of both the eigenvalue and
the eigenvector.
Parameters
i: int
Index of the solution to be obtained.
Vr: Vec
Placeholder for the returned eigenvector (real part).
Vi: Vec
Placeholder for the returned eigenvector (imaginary part).
Returns
e: scalar (possibly complex)
The computed eigenvalue.
Notes
The index i should be a value between 0 and
nconv-1 getConverged() ). Eigenpairs are indexed
according to the ordering criterion established with
setWhichEigenpairs() .
getEigenvalue (self ,
int i )
Gets the i-th eigenvalue as computed by solve() .
Parameters
i: int
Index of the solution to be obtained.
Returns
e: scalar (possibly complex)
The computed eigenvalue.
Notes
The index i should be a value between 0 and
nconv-1 getConverged() ). Eigenpairs are indexed
according to the ordering criterion established with
setWhichEigenpairs() .
getEigenvector (self ,
int i ,
Vec Vr ,
Vec Vi =None )
Gets the i-th eigenvector as computed by solve() .
Parameters
i: int
Index of the solution to be obtained.
Vr: Vec
Placeholder for the returned eigenvector (real part).
Vi: Vec, optional
Placeholder for the returned eigenvector (imaginary part).
Notes
The index i should be a value between 0 and
nconv-1 getConverged() ). Eigenpairs are indexed
according to the ordering criterion established with
setWhichEigenpairs() .
getErrorEstimate (self ,
int i )
Returns the error estimate associated to the i-th computed
eigenpair.
Parameters
i: int
Index of the solution to be considered.
Returns
e: real
Error estimate.
Notes
This is the error estimate used internally by the
eigensolver. The actual error bound can be computed with
computeError() .
Gets the extraction type used by the EPS object.
Returns
extraction: EPS.Extraction enumerate
The method of extraction.
Gets the computational interval for spectrum slicing.
Returns
inta: float
The left end of the interval.
intb: float
The right end of the interval.
Notes
If the interval was not set by the user, then zeros are returned.
getInvariantSubspace (self )
Gets an orthonormal basis of the computed invariant subspace.
Returns
subspace: list of Vec
Basis of the invariant subspace.
Notes
This function should be called after solve() has finished.
The returned vectors span an invariant subspace associated
with the computed eigenvalues. An invariant subspace X of
A` satisfies ``A x in X for all x in X (a
similar definition applies for generalized eigenproblems).
Gets the current iteration number. If the call to solve() is
complete, then it returns the number of iterations carried out
by the solution method.
Returns
its: int
Iteration number.
getKrylovSchurDetectZeros (self )
Gets the flag that enforces zero detection in spectrum slicing.
Returns
detect: bool
The zero detection flag.
getKrylovSchurDimensions (self )
Gets the dimensions used for each subsolve step in case of doing
spectrum slicing for a computational interval.
Returns
nev: int
Number of eigenvalues to compute.
ncv: int
Maximum dimension of the subspace to be used by the solver.
mpd: int
Maximum dimension allowed for the projected problem.
getKrylovSchurLocking (self )
Gets the locking flag used in the Krylov-Schur method.
Returns
lock: bool
The locking flag.
getKrylovSchurPartitions (self )
Gets the number of partitions of the communicator in case of
spectrum slicing.
Returns
npart: int
The number of partitions.
getKrylovSchurRestart (self )
Gets the restart parameter used in the Krylov-Schur method.
Returns
keep: float
The number of vectors to be kept at restart.
getKrylovSchurSubcommInfo (self )
Gets information related to the case of doing spectrum slicing
for a computational interval with multiple communicators.
Returns
k: int
Number of the subinterval for the calling process.
n: int
Number of eigenvalues found in the k-th subinterval.
v: Vec
A vector owned by processes in the subcommunicator with dimensions
compatible for locally computed eigenvectors.
Notes
This function is only available for spectrum slicing runs.
The returned Vec should be destroyed by the user.
getKrylovSchurSubcommMats (self )
Gets the eigenproblem matrices stored internally in the subcommunicator
to which the calling process belongs.
Returns
A: Mat
The matrix associated with the eigensystem.
B: Mat
The second matrix in the case of generalized eigenproblems.
Notes
This is the analog of getOperators() , but returns the matrices distributed
differently (in the subcommunicator rather than in the parent communicator).
These matrices should not be modified by the user.
getKrylovSchurSubcommPairs (self ,
int i ,
Vec V )
Gets the i-th eigenpair stored internally in the multi-communicator
to which the calling process belongs.
Parameters
i: int
Index of the solution to be obtained.
V: Vec
Placeholder for the returned eigenvector.
Returns
e: scalar
The computed eigenvalue.
Notes
The index i should be a value between 0 and n-1 n is the number of vectors in the local subinterval,
see getKrylovSchurSubcommInfo() .
getLanczosReorthogType (self )
Gets the type of reorthogonalization used during the Lanczos
iteration.
Gets the matrices associated with the eigenvalue problem.
Returns
A: Mat
The matrix associated with the eigensystem.
B: Mat
The second matrix in the case of generalized eigenproblems.
Gets the prefix used for searching for all EPS options in the
database.
Returns
prefix: string
The prefix string set for this EPS object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Gets the type of shifts used during the power iteration.
Gets the problem type from the EPS object.
Returns
problem_type: EPS.ProblemType enumerate
The problem type that was previously set.
Obtain the region object associated to the eigensolver.
Returns
rg: RG
The region context.
Gets the reset parameter used in the RQCG method.
Returns
nrest: integer
The number of iterations between resets.
Obtain the spectral transformation (ST ) object associated to
the eigensolver object.
Returns
st: ST
The spectral transformation.
Gets the value of the target.
Returns
target: float (real or complex)
The value of the target.
Notes
If the target was not set by the user, then zero is returned.
Gets the tolerance and maximum iteration count used by the
default EPS convergence tests.
Returns
tol: float
The convergence tolerance.
max_it: int
The maximum number of iterations
Returns the flag indicating whether all residual norms must be
computed or not.
Returns
trackall: bool
Whether the solver compute all residuals or not.
Returns the flag indicating whether true residual must be
computed explicitly or not.
Returns
trueres: bool
Whether the solver compute all residuals or not.
Gets the EPS type of this object.
Returns
type: EPS.Type enumerate
The solver currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Returns which portion of the spectrum is to be sought.
Returns
which: EPS.Which enumerate
The portion of the spectrum to be sought by the solver.
Tells whether the EPS object corresponds to a generalized
eigenvalue problem.
Returns
flag: boolean
True if two matrices were set with setOperators() .
Tells whether the EPS object corresponds to a Hermitian
eigenvalue problem.
Returns
flag: boolean
True if the problem type set with setProblemType() was
Hermitian.
Tells whether the EPS object corresponds to an eigenvalue problem
type that requires a positive (semi-) definite matrix B.
Returns
flag: boolean
True if the problem type set with setProblemType() was
positive.
setArnoldiDelayed (self ,
delayed )
Activates or deactivates delayed reorthogonalization in the
Arnoldi iteration.
Parameters
delayed: boolean
True if delayed reorthogonalization is to be used.
Notes
This call is only relevant if the type was set to
EPS.Type.ARNOLDI with setType() .
Delayed reorthogonalization is an aggressive optimization for
the Arnoldi eigensolver than may provide better scalability,
but sometimes makes the solver converge less than the default
algorithm.
Associates a basis vectors object to the eigensolver.
Parameters
bv: BV
The basis vectors context.
setBalance (self ,
balance =None ,
iterations =None ,
cutoff =None )
Specifies the balancing technique to be employed by the
eigensolver, and some parameters associated to it.
Parameters
balance: EPS.Balance enumerate
The balancing method
iterations: integer
Number of iterations of the balancing algorithm
cutoff: real
Cutoff value
setConvergenceTest (self ,
conv )
Specifies how to compute the error estimate
used in the convergence test.
Parameters
conv: EPS.Conv
The method used to compute the error estimate
used in the convergence test.
Associates a direct solver object to the eigensolver.
Parameters
ds: DS
The direct solver context.
setDeflationSpace (self ,
space )
Add vectors to the basis of the deflation space.
Parameters
space: a Vec or an array of Vec
Set of basis vectors to be added to the deflation
space.
Notes
When a deflation space is given, the eigensolver seeks the
eigensolution in the restriction of the problem to the
orthogonal complement of this space. This can be used for
instance in the case that an invariant subspace is known
beforehand (such as the nullspace of the matrix).
The vectors do not need to be mutually orthonormal, since they
are explicitly orthonormalized internally.
These vectors do not persist from one solve() call to the other,
so the deflation space should be set every time.
setDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
Sets the number of eigenvalues to compute and the dimension of
the subspace.
Parameters
nev: int, optional
Number of eigenvalues to compute.
ncv: int, optional
Maximum dimension of the subspace to be used by the
solver.
mpd: int, optional
Maximum dimension allowed for the projected problem.
Notes
Use DECIDE for ncv and mpd to assign a reasonably good
value, which is dependent on the solution method.
The parameters ncv and mpd are intimately related, so that
the user is advised to set one of them at most. Normal usage
is the following:
In cases where nev is small, the user sets ncv
(a reasonable default is 2 * nev).
In cases where nev is large, the user sets mpd.
The value of ncv should always be between nev and (nev +
mpd), typically ncv = nev + mpd. If nev is not too
large, mpd = nev is a reasonable choice, otherwise a
smaller value should be used.
setExtraction (self ,
extraction )
Sets the extraction type used by the EPS object.
Parameters
extraction: EPS.Extraction enumerate
The extraction method to be used by the solver.
Notes
Not all eigensolvers support all types of extraction. See the
SLEPc documentation for details.
By default, a standard Rayleigh-Ritz extraction is used. Other
extractions may be useful when computing interior eigenvalues.
Harmonic-type extractions are used in combination with a
target . See setTarget() .
Sets EPS options from the options database. This routine must
be called before setUp() if the user is to be allowed to set
the solver type.
Notes
To see all options, run your program with the -help
Overrides:
petsc4py.PETSc.Object.setFromOptions
setInitialSpace (self ,
space )
Sets the initial space from which the eigensolver starts to
iterate.
Parameters
space: Vec or sequence of Vec
The initial space
Notes
Some solvers start to iterate on a single vector (initial vector).
In that case, the other vectors are ignored.
In contrast to setDeflationSpace() , these vectors do not persist
from one solve() call to the other, so the initial space should be
set every time.
The vectors do not need to be mutually orthonormal, since they are
explicitly orthonormalized internally.
Common usage of this function is when the user can provide a rough
approximation of the wanted eigenspace. Then, convergence may be faster.
setInterval (self ,
inta ,
intb )
Defines the computational interval for spectrum slicing.
Parameters
inta: float
The left end of the interval.
intb: float
The right end of the interval.
Notes
Spectrum slicing is a technique employed for computing all
eigenvalues of symmetric eigenproblems in a given interval.
This function provides the interval to be considered. It must
be used in combination with EPS.Which.ALL , see
setWhichEigenpairs() .
setKrylovSchurDetectZeros (self ,
detect )
Sets a flag to enforce detection of zeros during the factorizations
throughout the spectrum slicing computation.
Parameters
detect: bool
True if zeros must checked for.
Notes
A zero in the factorization indicates that a shift coincides with
an eigenvalue.
This flag is turned off by default, and may be necessary in some cases,
especially when several partitions are being used. This feature currently
requires an external package for factorizations with support for zero
detection, e.g. MUMPS.
setKrylovSchurDimensions (self ,
nev =None ,
ncv =None ,
mpd =None )
Sets the dimensions used for each subsolve step in case of doing
spectrum slicing for a computational interval. The meaning of the
parameters is the same as in setDimensions() .
Parameters
nev: int, optional
Number of eigenvalues to compute.
ncv: int, optional
Maximum dimension of the subspace to be used by the solver.
mpd: int, optional
Maximum dimension allowed for the projected problem.
setKrylovSchurLocking (self ,
lock )
Choose between locking and non-locking variants of the
Krylov-Schur method.
Parameters
lock: bool
True if the locking variant must be selected.
Notes
The default is to lock converged eigenpairs when the method restarts.
This behaviour can be changed so that all directions are kept in the
working subspace even if already converged to working accuracy (the
non-locking variant).
setKrylovSchurPartitions (self ,
npart )
Sets the number of partitions for the case of doing spectrum
slicing for a computational interval with the communicator split
in several sub-communicators.
Parameters
npart: int
The number of partitions.
Notes
By default, npart=1 so all processes in the communicator participate in
the processing of the whole interval. If npart>1 then the interval is
divided into npart subintervals, each of them being processed by a
subset of processes.
setKrylovSchurRestart (self ,
keep )
Sets the restart parameter for the Krylov-Schur method, in
particular the proportion of basis vectors that must be kept
after restart.
Parameters
keep: float
The number of vectors to be kept at restart.
Notes
Allowed values are in the range [0.1,0.9]. The default is 0.5.
setKrylovSchurSubintervals (self ,
subint )
Sets the subinterval boundaries for spectrum slicing with a computational interval.
Parameters
subint: list of real values specifying subintervals
Notes
Logically Collective on EPS
This function must be called after setKrylovSchurPartitions().
For npart partitions, the argument subint must contain npart+1
real values sorted in ascending order:
subint_0, subint_1, ..., subint_npart,
where the first and last values must coincide with the interval
endpoints set with EPSSetInterval().
The subintervals are then defined by two consecutive points:
[subint_0,subint_1], [subint_1,subint_2], and so on.
setLanczosReorthogType (self ,
reorthog )
Sets the type of reorthogonalization used during the Lanczos
iteration.
setOperators (self ,
Mat A ,
Mat B =None )
Sets the matrices associated with the eigenvalue problem.
Parameters
A: Mat
The matrix associated with the eigensystem.
B: Mat, optional
The second matrix in the case of generalized eigenproblems;
if not provided, a standard eigenproblem is assumed.
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all EPS options in the
database.
Parameters
prefix: string
The prefix string to prepend to all EPS option
requests.
Notes
A hyphen (-) must NOT be given at the beginning of the prefix
name. The first character of all runtime options is
AUTOMATICALLY the hyphen.
For example, to distinguish between the runtime options for
two different EPS contexts, one could call:
E1.setOptionsPrefix("eig1_")
E2.setOptionsPrefix("eig2_")
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
setPowerShiftType (self ,
shift )
Sets the type of shifts used during the power iteration. This
can be used to emulate the Rayleigh Quotient Iteration (RQI)
method.
setProblemType (self ,
problem_type )
Specifies the type of the eigenvalue problem.
Parameters
problem_type: EPS.ProblemType enumerate
The problem type to be set.
Notes
Allowed values are: Hermitian (HEP), non-Hermitian (NHEP),
generalized Hermitian (GHEP), generalized non-Hermitian
(GNHEP), and generalized non-Hermitian with positive
semi-definite B (PGNHEP).
This function must be used to instruct SLEPc to exploit
symmetry. If no problem type is specified, by default a
non-Hermitian problem is assumed (either standard or
generalized). If the user knows that the problem is Hermitian
(i.e. A=A^H ) or generalized Hermitian (i.e. A=A^H ,
B=B^H , and B positive definite) then it is recommended
to set the problem type so that eigensolver can exploit these
properties.
Associates a region object to the eigensolver.
Parameters
rg: RG
The region context.
setRQCGReset (self ,
nrest )
Sets the reset parameter of the RQCG iteration. Every nrest iterations,
the solver performs a Rayleigh-Ritz projection step.
Parameters
nrest: integer
The number of iterations between resets.
Associates a spectral transformation object to the
eigensolver.
Parameters
st: ST
The spectral transformation.
Sets the value of the target.
Parameters
target: float (real or complex)
The value of the target.
Notes
The target is a scalar value used to determine the portion of
the spectrum of interest. It is used in combination with
setWhichEigenpairs() .
setTolerances (self ,
tol =None ,
max_it =None )
Sets the tolerance and maximum iteration count used by the
default EPS convergence tests.
Parameters
tol: float, optional
The convergence tolerance.
max_it: int, optional
The maximum number of iterations
Notes
Use DECIDE for maxits to assign a reasonably good value,
which is dependent on the solution method.
setTrackAll (self ,
trackall )
Specifies if the solver must compute the residual of all
approximate eigenpairs or not.
Parameters
trackall: bool
Whether compute all residuals or not.
setTrueResidual (self ,
trueres )
Specifies if the solver must compute the true residual
explicitly or not.
Parameters
trueres: bool
Whether compute the true residual or not.
Selects the particular solver to be used in the EPS object.
Parameters
eps_type: EPS.Type enumerate
The solver to be used.
Notes
See EPS.Type for available methods. The default is
EPS.Type.KRYLOVSCHUR . Normally, it is best to use
setFromOptions() and then set the EPS type from the options
database rather than by using this routine. Using the options
database provides the user with maximum flexibility in
evaluating the different available methods.
Sets up all the internal data structures necessary for the
execution of the eigensolver.
Notes
This function need not be called explicitly in most cases,
since solve() calls it. It can be useful when one wants to
measure the set-up time separately from the solve time.
setWhichEigenpairs (self ,
which )
Specifies which portion of the spectrum is to be sought.
Parameters
which: EPS.Which enumerate
The portion of the spectrum to be sought by the solver.
Notes
Not all eigensolvers implemented in EPS account for all the
possible values. Also, some values make sense only for certain
types of problems. If SLEPc is compiled for real numbers
EPS.Which.LARGEST_IMAGINARY and
EPS.Which.SMALLEST_IMAGINARY use the absolute value of the
imaginary part for eigenvalue selection.
updateKrylovSchurSubcommMats (self ,
s =1.0 ,
a =1.0 ,
Mat Au =None ,
t =1.0 ,
b =1.0 ,
Mat Bu =None ,
structure =None ,
globalup =False )
Update the eigenproblem matrices stored internally in the subcommunicator
to which the calling process belongs.
Parameters
s: float (real or complex)
Scalar that multiplies the existing A matrix.
a: float (real or complex)
Scalar used in the axpy operation on A.
Au: Mat, optional
The matrix used in the axpy operation on A.
t: float (real or complex)
Scalar that multiplies the existing B matrix.
b: float (real or complex)
Scalar used in the axpy operation on B.
Bu: Mat, optional
The matrix used in the axpy operation on B.
structure: PETSc.Mat.Structure enumerate
Either same, different, or a subset of the non-zero sparsity pattern.
globalup: bool
Whether global matrices must be updated or not.
Notes
This function modifies the eigenproblem matrices at subcommunicator level,
and optionally updates the global matrices in the parent communicator.
The updates are expressed as A <-- s*A + a*Au , B <-- t*B + b*Bu .
It is possible to update one of the matrices, or both.
The matrices Au and Bu must be equal in all subcommunicators.
The str flag is passed to the Mat.axpy() operations to perform the updates.
If globalup is True, communication is carried out to reconstruct the updated
matrices in the parent communicator.
view (self ,
Viewer viewer =None )
Prints the EPS data structure.
Parameters
viewer: Viewer, optional.
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
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slepc4py.SLEPc.FN
Class FN
a new object with type S, a subtype of T
create (self ,
comm =None )
destroy (self )
evaluateFunction (self ,
x )
getOptionsPrefix (self )
getScale (self )
getType (self )
setOptionsPrefix (self ,
prefix )
setScale (self ,
alpha =None ,
beta =None )
setType (self ,
fn_type )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
Creates the FN object.
Parameters
comm: Comm, optional
MPI communicator; if not provided, it defaults to all
processes.
Destroys the FN object.
Overrides:
petsc4py.PETSc.Object.destroy
evaluateDerivative (self ,
x )
Computes the value of the derivative f'(x) for a given x.
Parameters
x: scalar
Value where the derivative must be evaluated.
Returns
y: scalar
The result of f'(x).
evaluateFunction (self ,
x )
Computes the value of the function f(x) for a given x.
Parameters
x: scalar
Value where the function must be evaluated.
Returns
y: scalar
The result of f(x).
Gets the prefix used for searching for all FN options in the
database.
Returns
prefix: string
The prefix string set for this FN object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Gets the scaling parameters that define the matematical function.
Returns
alpha: scalar (possibly complex)
inner scaling (argument).
beta: scalar (possibly complex)
outer scaling (result).
Gets the FN type of this object.
Returns
type: FN.Type enumerate
The inner product type currently being used.
Overrides:
petsc4py.PETSc.Object.getType
Sets FN options from the options database.
Notes
To see all options, run your program with the -help
Overrides:
petsc4py.PETSc.Object.setFromOptions
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all FN options in the
database.
Parameters
prefix: string
The prefix string to prepend to all FN option
requests.
Notes
A hyphen (- ) must NOT be given at the beginning of the
prefix name. The first character of all runtime options is
AUTOMATICALLY the hyphen.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
setRationalDenominator (self ,
alpha )
Sets the coefficients of the denominator of the rational function.
Parameters
alpha: array of scalars
Coefficients.
setRationalNumerator (self ,
alpha )
Sets the coefficients of the numerator of the rational function.
Parameters
alpha: array of scalars
Coefficients.
setScale (self ,
alpha =None ,
beta =None )
Sets the scaling parameters that define the matematical function.
Parameters
alpha: scalar (possibly complex)
inner scaling (argument).
beta: scalar (possibly complex)
outer scaling (result).
Selects the type for the FN object.
Parameters
fn_type: FN.Type enumerate
The inner product type to be used.
view (self ,
Viewer viewer =None )
Prints the FN data structure.
Parameters
viewer: Viewer, optional
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
slepc4py.SLEPc.SVD.ErrorType
Class ErrorType
SVD error type to assess accuracy of computed solutions
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ABSOLUTE = 0
RELATIVE = 1
__qualname__ = 'SVDErrorType'
Inherited from object :
__class__
slepc4py-3.7.0/docs/apiref/slepc4py.SLEPc.ST.MatMode-class.html 0000644 0001750 0000144 00000021765 12720314524 024575 0 ustar dalcinl users 0000000 0000000
slepc4py.SLEPc.ST.MatMode
Class MatMode
ST matrix mode
COPY : A working copy of the matrix is created.INPLACE : The operation is computed in-place.SHELL : The matrix A-sigma*B
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
COPY = 0
INPLACE = 1
SHELL = 2
__qualname__ = 'STMatMode'
Inherited from object :
__class__
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slepc4py.SLEPc.EPS.Conv
Class Conv
EPS convergence test
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ABS = 0
NORM = 2
REL = 1
USER = 3
__qualname__ = 'EPSConv'
Inherited from object :
__class__
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slepc4py.SLEPc.ST
Class ST
a new object with type S, a subtype of T
apply (self ,
Vec x ,
Vec y )(A - sB)^-1 B in the case of the shift-and-invert
tranformation and generalized eigenproblem.
applyTranspose (self ,
Vec x ,
Vec y )B^T(A - sB)^-T in the case of the
shift-and-invert tranformation and generalized eigenproblem.
create (self ,
comm =None )
destroy (self )
getKSP (self )
getMatMode (self )
getOperators (self )
getOptionsPrefix (self )
getShift (self )
getTransform (self )
getType (self )
reset (self )
setCayleyAntishift (self ,
tau )
setKSP (self ,
KSP ksp )
setMatMode (self ,
mode )
setMatStructure (self ,
structure )A
and B constituting the generalized eigenvalue
problem.
setOperators (self ,
operators )
setOptionsPrefix (self ,
prefix )
setShift (self ,
shift )
setTransform (self ,
flag )
setType (self ,
st_type )
setUp (self )
view (self ,
Viewer viewer =None )
Inherited from petsc4py.PETSc.Object :
__copy__,
__deepcopy__,
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__,
__nonzero__,
compose,
decRef,
getAttr,
getClassId,
getClassName,
getComm,
getDict,
getName,
getRefCount,
getTabLevel,
incRef,
incrementTabLevel,
query,
setAttr,
setName,
setTabLevel,
stateIncrease
Inherited from object :
__delattr__,
__format__,
__getattribute__,
__hash__,
__init__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
ksp
mat_mode
shift
Inherited from petsc4py.PETSc.Object :
classid,
comm,
fortran,
handle,
klass,
name,
prefix,
refcount,
type
Inherited from object :
__class__
Returns: a new object with type S, a subtype of T
Overrides:
object.__new__
apply (self ,
Vec x ,
Vec y )
Applies the spectral transformation operator to a vector, for
instance (A - sB)^-1 B in the case of the shift-and-invert
tranformation and generalized eigenproblem.
Parameters
x: Vec
The input vector.
y: Vec
The result vector.
applyTranspose (self ,
Vec x ,
Vec y )
Applies the transpose of the operator to a vector, for
instance B^T(A - sB)^-T in the case of the
shift-and-invert tranformation and generalized eigenproblem.
Parameters
x: Vec
The input vector.
y: Vec
The result vector.
Creates the ST object.
Parameters
comm: Comm, optional
MPI communicator; if not provided, it defaults to all
processes.
Destroys the ST object.
Overrides:
petsc4py.PETSc.Object.destroy
Gets the KSP object associated with the spectral
transformation.
Returns
ksp: KSP
The linear solver object.
Notes
On output, the internal value of KSP can be NULL if the
combination of eigenproblem type and selected transformation
does not require to solve a linear system of equations.
Gets a flag that indicates how the matrix is being shifted in
the shift-and-invert and Cayley spectral transformations.
Gets the matrices associated with the eigenvalue problem.
Returns
operators: tuple of Mat
The matrices associated with the eigensystem.
Gets the prefix used for searching for all ST options in the
database.
Returns
prefix: string
The prefix string set for this ST object.
Overrides:
petsc4py.PETSc.Object.getOptionsPrefix
Gets the shift associated with the spectral transformation.
Returns
shift: scalar (possibly complex)
The value of the shift.
Gets the flag indicating whether the transformed matrices
are computed or not.
Returns
flag: boolean
This flag is intended for the case of polynomial
eigenproblems solved via linearization.
If this flag is False (default) the spectral transformation
is applied to the linearization (handled by the eigensolver),
otherwise it is applied to the original problem.
Gets the ST type of this object.
Returns
type: ST.Type enumerate
The spectral transformation currently being used.
Overrides:
petsc4py.PETSc.Object.getType
setCayleyAntishift (self ,
tau )
Sets the value of the anti-shift for the Cayley spectral
transformation.
Parameters
tau: scalar (possibly complex)
The anti-shift.
Notes
In the generalized Cayley transform, the operator can be
expressed as OP = inv(A - sigma B)*(A + tau B) . This
function sets the value of tau. Use setShift() for
setting sigma .
Sets ST options from the options database. This routine must
be called before setUp() if the user is to be allowed to set
the solver type.
Notes
To see all options, run your program with the -help option.
Overrides:
petsc4py.PETSc.Object.setFromOptions
Sets the KSP object associated with the spectral
transformation.
Parameters
ksp: KSP
The linear solver object.
Sets a flag to indicate how the matrix is being shifted in the
shift-and-invert and Cayley spectral transformations.
Parameters
mode: ST.MatMode enumerate
The mode flag.
Notes
By default (ST.MatMode.COPY ), a copy of matrix A is made
and then this copy is shifted explicitly, e.g. A <- (A - s
B) .
With ST.MatMode.INPLACE , the original matrix A is
shifted at setUp() and unshifted at the end of the
computations. With respect to the previous one, this mode
avoids a copy of matrix A . However, a backdraw is that the
recovered matrix might be slightly different from the original
one (due to roundoff).
With ST.MatMode.SHELL , the solver works with an implicit
shell matrix that represents the shifted matrix. This mode is
the most efficient in creating the shifted matrix but it
places serious limitations to the linear solves performed in
each iteration of the eigensolver (typically, only interative
solvers with Jacobi preconditioning can be used).
In the case of generalized problems, in the two first modes
the matrix A - s B has to be computed explicitly. The
efficiency of this computation can be controlled with
setMatStructure() .
setMatStructure (self ,
structure )
Sets an internal Mat.Structure attribute to indicate which is
the relation of the sparsity pattern of the two matrices A
and B constituting the generalized eigenvalue
problem. This function has no effect in the case of standard
eigenproblems.
Parameters
structure: PETSc.Mat.Structure enumerate
Either same, different, or a subset of the non-zero
sparsity pattern.
Notes
By default, the sparsity patterns are assumed to be
different. If the patterns are equal or a subset then it is
recommended to set this attribute for efficiency reasons (in
particular, for internal AXPY() matrix operations).
setOperators (self ,
operators )
Sets the matrices associated with the eigenvalue problem.
Parameters
operators: sequence of Mat
The matrices associated with the eigensystem.
setOptionsPrefix (self ,
prefix )
Sets the prefix used for searching for all ST options in the
database.
Parameters
prefix: string
The prefix string to prepend to all ST option
requests.
Notes
A hyphen (- ) must NOT be given at the beginning of the
prefix name. The first character of all runtime options is
AUTOMATICALLY the hyphen.
Overrides:
petsc4py.PETSc.Object.setOptionsPrefix
Sets the shift associated with the spectral transformation.
Parameters
shift: scalar (possibly complex)
The value of the shift.
Notes
In some spectral transformations, changing the shift may have
associated a lot of work, for example recomputing a
factorization.
Sets a flag to indicate whether the transformed matrices
are computed or not.
Parameters
flag: boolean
This flag is intended for the case of polynomial
eigenproblems solved via linearization.
If this flag is False (default) the spectral transformation
is applied to the linearization (handled by the eigensolver),
otherwise it is applied to the original problem.
Builds ST for a particular spectral transformation.
Parameters
st_type: ST.Type enumerate
The spectral transformation to be used.
Notes
See ST.Type for available methods. The default is
ST.Type.SHIFT with a zero shift. Normally, it is best to
use setFromOptions() and then set the ST type from the
options database rather than by using this routine. Using the
options database provides the user with maximum flexibility in
evaluating the different available methods.
view (self ,
Viewer viewer =None )
Prints the ST data structure.
Parameters
viewer: Viewer, optional
Visualization context; if not provided, the standard
output is used.
Overrides:
petsc4py.PETSc.Object.view
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,`4P),S a a('11qdN&q2O-0(Xh` V? IENDB` slepc4py-3.7.0/docs/slepc4py.info 0000644 0001750 0000144 00000065536 12720314530 017435 0 ustar dalcinl users 0000000 0000000 This is slepc4py.info, produced by makeinfo version 6.0 from
slepc4py.texi.
SLEPc for Python 3.7.0, May 22, 2016
Lisandro Dalcin
Copyright © 2016, Lisandro Dalcin
INFO-DIR-SECTION Miscellaneous
START-INFO-DIR-ENTRY
* slepc4py: (slepc4py.info). SLEPc for Python.
END-INFO-DIR-ENTRY
Generated by Sphinx 1.2.3.
File: slepc4py.info, Node: Top, Next: Contents, Up: (dir)
SLEPc for Python
****************
SLEPc for Python 3.7.0, May 22, 2016
Lisandro Dalcin
Copyright © 2016, Lisandro Dalcin
Authors: Lisandro Dalcin, Jose E. Roman
Contact: ,
Web Site: ‘https://bitbucket.org/slepc/slepc4py’
Date: May 22, 2016
Abstract
========
This document describes slepc4py(1), a Python(2) port to the SLEPc(3)
libraries.
SLEPc(4) is a software package for the parallel solution of large-scale
eigenvalue problems. It can be used for computing eigenvalues and
eigenvectors of large, sparse matrices, or matrix pairs, and also for
computing singular values and vectors of a rectangular matrix.
SLEPc(5) relies on PETSc(6) for basic functionality such as the
representation of matrices and vectors, and the solution of linear
systems of equations. Thus, slepc4py(7) must be used together with its
companion petsc4py(8).
* Menu:
* Contents::
* Index::
— The Detailed Node Listing —
Contents
* Overview::
* Tutorial::
* Installation::
* Citations::
Overview
* Features::
* Components::
Tutorial
* Commented source of a simple example::
* Example of command-line usage::
Installation
* Using pip or easy_install::
* Using distutils::
Using distutils
* Requirements::
* Downloading::
* Building::
* Installing::
---------- Footnotes ----------
(1) http://bitbucket.org/slepc/slepc4py
(2) http://www.python.org
(3) http://slepc.upv.es
(4) http://slepc.upv.es
(5) http://slepc.upv.es
(6) http://www.mcs.anl.gov/petsc/
(7) http://bitbucket.org/slepc/slepc4py
(8) http://bitbucket.org/petsc/petsc4py
File: slepc4py.info, Node: Contents, Next: Index, Prev: Top, Up: Top
1 Contents
**********
* Menu:
* Overview::
* Tutorial::
* Installation::
* Citations::
File: slepc4py.info, Node: Overview, Next: Tutorial, Up: Contents
1.1 Overview
============
`SLEPc for Python' (slepc4py) is a Python package that provides
convenient access to the functionality of SLEPc.
SLEPc (1), (2) implements algorithms and tools for the numerical
solution of large, sparse eigenvalue problems on parallel computers. It
can be used for linear eigenvalue problems in either standard or
generalized form, with real or complex arithmetic. It can also be used
for computing a partial SVD of a large, sparse, rectangular matrix, and
to solve nonlinear eigenvalue problems (polynomial or general).
Additionally, SLEPc provides solvers for the computation of the action
of a matrix function on a vector.
SLEPc is intended for computing a subset of the spectrum of a matrix (or
matrix pair). One can for instance approximate the largest magnitude
eigenvalues, or the smallest ones, or even those eigenvalues located
near a given region of the complex plane. Interior eigenvalues are
harder to compute, so SLEPc provides different methodologies. One such
method is to use a spectral transformation. Cheaper alternatives are
also available.
* Menu:
* Features::
* Components::
---------- Footnotes ----------
(1) J. E. Roman, C. Campos, E. Romero, A. Tomas. SLEPc Users Manual.
DSIC-II/24/02 - Revision 3.5 D. Sistemas Informáticos y Computación,
Universitat Politècnica de València. 2014.
(2) Vicente Hernandez, Jose E. Roman and Vicente Vidal. SLEPc: A
Scalable and Flexible Toolkit for the Solution of Eigenvalue Problems,
ACM Trans. Math. Softw. 31(3), pp. 351-362, 2005.
File: slepc4py.info, Node: Features, Next: Components, Up: Overview
1.1.1 Features
--------------
Currently, the following types of eigenproblems can be addressed:
* Standard eigenvalue problem, `Ax=kx', either for Hermitian or
non-Hermitian matrices.
* Generalized eigenvalue problem, `Ax=kBx', either Hermitian
positive-definite or not.
* Partial singular value decomposition of a rectangular matrix,
`Au=sv'.
* Polynomial eigenvalue problem, `P(k)x=0'.
* Nonlinear eigenvalue problem, `T(k)x=0'.
* Computing the action of a matrix function on a vector, `w=f(alpha
A)v'.
For the linear eigenvalue problem, the following methods are available:
* Krylov eigensolvers, particularly Krylov-Schur, Arnoldi, and
Lanczos.
* Davidson-type eigensolvers, including Generalized Davidson and
Jacobi-Davidson.
* Subspace iteration and single vector iterations (inverse iteration,
RQI).
* Conjugate gradient for the minimization of the Rayleigh quotient.
* A contour integral solver.
For singular value computations, the following alternatives can be used:
* Use an eigensolver via the cross-product matrix `A’A' or the cyclic
matrix `[0 A; A’ 0]'.
* Explicitly restarted Lanczos bidiagonalization.
* Implicitly restarted Lanczos bidiagonalization (thick-restart
Lanczos).
For polynomial eigenvalue problems, the following methods are available:
* Use an eigensolver to solve the generalized eigenvalue problem
obtained after linearization.
* TOAR and Q-Arnoldi, memory efficient variants of Arnoldi for
polynomial eigenproblems.
Computation of interior eigenvalues is supported by means of the
following methodologies:
* Spectral transformations, such as shift-and-invert. This technique
implicitly uses the inverse of the shifted matrix `(A-tI)' in order
to compute eigenvalues closest to a given target value, `t'.
* Harmonic extraction, a cheap alternative to shift-and-invert that
also tries to approximate eigenvalues closest to a target, `t', but
without requiring a matrix inversion.
Other remarkable features include:
* High computational efficiency, by using NumPy and SLEPc under the
hood.
* Data-structure neutral implementation, by using efficient sparse
matrix storage provided by PETSc. Implicit matrix representation
is also available by providing basic operations such as
matrix-vector products as user-defined Python functions.
* Run-time flexibility, by specifying numerous setting at the command
line.
* Ability to do the computation in parallel.
File: slepc4py.info, Node: Components, Prev: Features, Up: Overview
1.1.2 Components
----------------
SLEPc provides the following components, which are mirrored by slepc4py
for its use from Python. The first five components are solvers for
different classes of problems, while the rest can be considered
auxiliary object.
EPS: The Eigenvalue Problem Solver is the component that provides all
the functionality necessary to define and solve an eigenproblem. It
provides mechanisms for completely specifying the problem: the problem
type (e.g. standard symmetric), number of eigenvalues to compute, part
of the spectrum of interest. Once the problem has been defined, a
collection of solvers can be used to compute the required solutions.
The behaviour of the solvers can be tuned by means of a few parameters,
such as the maximum dimension of the subspace to be used during the
computation.
SVD: This component is the analog of EPS for the case of Singular Value
Decompositions. The user provides a rectangular matrix and specifies
how many singular values and vectors are to be computed, whether the
largest or smallest ones, as well as some other parameters for fine
tuning the computation. Different solvers are available, as in the case
of EPS.
PEP: This component is the analog of EPS for the case of Polynomial
Eigenvalue Problems. The user provides the coefficient matrices of the
polynomial. Several parameters can be specified, as in the case of EPS.
It is also possible to indicate whether the problem belongs to a special
type, e.g., symmetric or gyroscopic.
NEP: This component covers the case of general nonlinear eigenproblems,
T(lambda)x=0.
MFN: This component provides the functionality for computing the action
of a matrix function on a vector. Given a matrix A and a vector b, the
call MFNSolve(mfn,b,x) computes x=f(A)b, where f is a function such as
the exponential.
ST: The Spectral Transformation is a component that provides convenient
implementations of common spectral transformations. These are simple
transformations that map eigenvalues to different positions, in such a
way that convergence to wanted eigenvalues is enhanced. The most common
spectral transformation is shift-and-invert, that allows for the
computation of eigenvalues closest to a given target value.
BV: This component encapsulates the concept of a set of Basis Vectors
spanning a vector space. This component provides convenient access to
common operations such as orthogonalization of vectors. The BV
component is usually not required by end-users.
DS: The Dense System (or Direct Solver) component, used internally to
solve dense eigenproblems of small size that appear in the course of
iterative eigensolvers.
FN: A component used to define mathematical functions. This is required
by the end-user for instance to define function T(.) when solving
nonlinear eigenproblems with NEP in split form.
File: slepc4py.info, Node: Tutorial, Next: Installation, Prev: Overview, Up: Contents
1.2 Tutorial
============
This tutorial is intended for basic use of slepc4py. For more advanced
use, the reader is referred to SLEPc tutorials as well as to slepc4py
reference documentation.
* Menu:
* Commented source of a simple example::
* Example of command-line usage::
File: slepc4py.info, Node: Commented source of a simple example, Next: Example of command-line usage, Up: Tutorial
1.2.1 Commented source of a simple example
------------------------------------------
In this section, we include the source code of example ‘demo/ex1.py’
available in the slepc4py distribution, with comments inserted inline.
The first thing to do is initialize the libraries. This is normally not
required, as it is done automatically at import time. However, if you
want to gain access to the facilities for accesing command-line options,
the following lines must be executed by the main script prior to any
petsc4py or slepc4py calls:
import sys, slepc4py
slepc4py.init(sys.argv)
Next, we have to import the relevant modules. Normally, both PETSc and
SLEPc modules have to be imported in all slepc4py programs. It may be
useful to import NumPy as well:
from petsc4py import PETSc
from slepc4py import SLEPc
import numpy
At this point, we can use any petsc4py and slepc4py operations. For
instance, the following lines allow the user to specify an integer
command-line argument ‘n’ with a default value of 30 (see the next
section for example usage of command-line options):
opts = PETSc.Options()
n = opts.getInt('n', 30)
It is necessary to build a matrix to define an eigenproblem (or two in
the case of generalized eigenproblems). The following fragment of code
creates the matrix object and then fills the non-zero elements one by
one. The matrix of this particular example is tridiagonal, with value 2
in the diagonal, and -1 in off-diagonal positions. See petsc4py
documentation for details about matrix objects:
A = PETSc.Mat().create()
A.setSizes([n, n])
A.setFromOptions()
A.setUp()
rstart, rend = A.getOwnershipRange()
# first row
if rstart == 0:
A[0, :2] = [2, -1]
rstart += 1
# last row
if rend == n:
A[n-1, -2:] = [-1, 2]
rend -= 1
# other rows
for i in range(rstart, rend):
A[i, i-1:i+2] = [-1, 2, -1]
A.assemble()
The solver object is created in a similar way as other objects in
petsc4py:
E = SLEPc.EPS(); E.create()
Once the object is created, the eigenvalue problem must be specified.
At least one matrix must be provided. The problem type must be
indicated as well, in this case it is HEP (Hermitian eigenvalue
problem). Apart from these, other settings could be provided here (for
instance, the tolerance for the computation). After all options have
been set, the user should call the ‘setFromOptions()’ operation, so that
any options specified at run time in the command line are passed to the
solver object:
E.setOperators(A)
E.setProblemType(SLEPc.EPS.ProblemType.HEP)
E.setFromOptions()
After that, the ‘solve()’ method will run the selected eigensolver,
keeping the solution stored internally:
E.solve()
Once the computation has finished, we are ready to print the results.
First, some informative data can be retrieved from the solver object:
Print = PETSc.Sys.Print
Print()
Print("******************************")
Print("*** SLEPc Solution Results ***")
Print("******************************")
Print()
its = E.getIterationNumber()
Print("Number of iterations of the method: %d" % its)
eps_type = E.getType()
Print("Solution method: %s" % eps_type)
nev, ncv, mpd = E.getDimensions()
Print("Number of requested eigenvalues: %d" % nev)
tol, maxit = E.getTolerances()
Print("Stopping condition: tol=%.4g, maxit=%d" % (tol, maxit))
For retrieving the solution, it is necessary to find out how many
eigenpairs have converged to the requested precision:
nconv = E.getConverged()
Print("Number of converged eigenpairs %d" % nconv)
For each of the ‘nconv’ eigenpairs, we can retrieve the eigenvalue ‘k’,
and the eigenvector, which is represented by means of two petsc4py
vectors ‘vr’ and ‘vi’ (the real and imaginary part of the eigenvector,
since for real matrices the eigenvalue and eigenvector may be complex).
We also compute the corresponding relative errors in order to make sure
that the computed solution is indeed correct:
if nconv > 0:
# Create the results vectors
vr, wr = A.getVecs()
vi, wi = A.getVecs()
#
Print()
Print(" k ||Ax-kx||/||kx|| ")
Print("----------------- ------------------")
for i in range(nconv):
k = E.getEigenpair(i, vr, vi)
error = E.computeError(i)
if k.imag != 0.0:
Print(" %9f%+9f j %12g" % (k.real, k.imag, error))
else:
Print(" %12f %12g" % (k.real, error))
Print()
File: slepc4py.info, Node: Example of command-line usage, Prev: Commented source of a simple example, Up: Tutorial
1.2.2 Example of command-line usage
-----------------------------------
Now we illustrate how to specify command-line options in order to
extract the full potential of slepc4py.
A simple execution of the ‘demo/ex1.py’ script will result in the
following output:
$ python demo/ex1.py
******************************
*** SLEPc Solution Results ***
******************************
Number of iterations of the method: 4
Solution method: krylovschur
Number of requested eigenvalues: 1
Stopping condition: tol=1e-07, maxit=100
Number of converged eigenpairs 4
k ||Ax-kx||/||kx||
----------------- ------------------
3.989739 5.76012e-09
3.959060 1.41957e-08
3.908279 6.74118e-08
3.837916 8.34269e-08
For specifying different setting for the solver parameters, we can use
SLEPc command-line options with the ‘-eps’ prefix. For instance, to
change the number of requested eigenvalues and the tolerance:
$ python demo/ex1.py -eps_nev 10 -eps_tol 1e-11
The method used by the solver object can also be set at run time:
$ python demo/ex1.py -eps_type lanczos
All the above settings can also be change within the source code by
making use of the appropriate slepc4py method. Since options can be set
from within the code and the command-line, it is often useful to view
the particular settings that are currently being used:
$ python demo/ex1.py -eps_view
EPS Object: 1 MPI processes
type: krylovschur
Krylov-Schur: 50% of basis vectors kept after restart
problem type: symmetric eigenvalue problem
selected portion of the spectrum: largest eigenvalues in magnitude
number of eigenvalues (nev): 1
number of column vectors (ncv): 16
maximum dimension of projected problem (mpd): 16
maximum number of iterations: 100
tolerance: 1e-08
convergence test: relative to the eigenvalue
BV Object: 1 MPI processes
type: svec
17 columns of global length 30
orthogonalization method: classical Gram-Schmidt
orthogonalization refinement: if needed (eta: 0.7071)
DS Object: 1 MPI processes
type: hep
solving the problem with: Implicit QR method (_steqr)
ST Object: 1 MPI processes
type: shift
shift: 0
number of matrices: 1
Note that for computing eigenvalues of smallest magnitude we can use the
option ‘-eps_smallest_magnitude’, but for interior eigenvalues things
are not so straightforward. One possibility is to try with harmonic
extraction, for instance to get the eigenvalues closest to 0.6:
$ python demo/ex1.py -eps_harmonic -eps_target 0.6
Depending on the problem, harmonic extraction may fail to converge. In
those cases, it is necessary to specify a spectral transformation other
than the default. In the command-line, this is indicated with the
‘-st_’ prefix. For example, shift-and-invert with a value of the shift
equal to 0.6 would be:
$ python demo/ex1.py -st_type sinvert -eps_target 0.6
File: slepc4py.info, Node: Installation, Next: Citations, Prev: Tutorial, Up: Contents
1.3 Installation
================
* Menu:
* Using pip or easy_install::
* Using distutils::
File: slepc4py.info, Node: Using pip or easy_install, Next: Using distutils, Up: Installation
1.3.1 Using `pip' or `easy_install'
-----------------------------------
You can use `pip' to install ‘slepc4py’ and its dependencies (‘mpi4py’
is optional but highly recommended):
$ pip install [--user] numpy mpi4py
$ pip install [--user] petsc petsc4py
$ pip install [--user] slepc slepc4py
Alternatively, you can use `easy_install' (deprecated):
$ easy_install [--user] slepc4py
If you already have working PETSc and SLEPc installs, set environment
variables ‘SLEPC_DIR’ and ‘PETSC_DIR’ (and perhaps ‘PETSC_ARCH’ for
non-prefix installs) to appropriate values and next use `pip':
$ export SLEPC_DIR=/path/to/slepc
$ export PETSC_DIR=/path/to/petsc
$ export PETSC_ARCH=arch-linux2-c-opt
$ pip install [--user] petsc4py slepc4py
File: slepc4py.info, Node: Using distutils, Prev: Using pip or easy_install, Up: Installation
1.3.2 Using `distutils'
-----------------------
* Menu:
* Requirements::
* Downloading::
* Building::
* Installing::
File: slepc4py.info, Node: Requirements, Next: Downloading, Up: Using distutils
1.3.2.1 Requirements
....................
You need to have the following software properly installed in order to
build `SLEPc for Python':
* Any MPI(1) implementation (2) (e.g., MPICH(3) or Open MPI(4)),
built with shared libraries.
* A matching version of PETSc(5) built with shared libraries.
* A matching version of SLEPc(6) built with shared libraries.
* NumPy(7) package.
* petsc4py(8) package.
---------- Footnotes ----------
(1) http://www.mpi-forum.org
(2) Unless you have appropriately configured and built SLEPc and
PETSc without MPI (configure option ‘--with-mpi=0’).
(3) http://www.mpich.org
(4) http://www.open-mpi.org
(5) http://www.mcs.anl.gov/petsc/
(6) http://slepc.upv.es
(7) http://www.numpy.org
(8) http://bitbucket.org/petsc/petsc4py
File: slepc4py.info, Node: Downloading, Next: Building, Prev: Requirements, Up: Using distutils
1.3.2.2 Downloading
...................
The `SLEPc for Python' package is available for download at the project
website generously hosted by Bitbucket. You can use `curl' or `wget' to
get a release tarball.
* Using `curl':
$ curl -O https://bitbucket.org/slepc/slepc4py/downloads/slepc4py-X.Y.tar.gz
* Using `wget':
$ wget https://bitbucket.org/slepc/slepc4py/downloads/slepc4py-X.Y.tar.gz
File: slepc4py.info, Node: Building, Next: Installing, Prev: Downloading, Up: Using distutils
1.3.2.3 Building
................
After unpacking the release tarball:
$ tar -zxf slepc4py-X.Y.tar.gz
$ cd slepc4py-X.Y
the distribution is ready for building.
Note: `Mac OS X' users employing a Python distribution built with
`universal binaries' may need to set the environment variables
‘MACOSX_DEPLOYMENT_TARGET’, ‘SDKROOT’, and ‘ARCHFLAGS’ to
appropriate values. As an example, assume your Mac is running
`Snow Leopard' on a `64-bit Intel' processor and you want to
override the hard-wired cross-development SDK in Python
configuration, your environment should be modified like this:
$ export MACOSX_DEPLOYMENT_TARGET=10.6
$ export SDKROOT=/
$ export ARCHFLAGS='-arch x86_64'
Some environment configuration is needed to inform the location of PETSc
and SLEPc. You can set (using `setenv', `export' or what applies to you
shell or system) the environment variables ‘SLEPC_DIR`’, ‘PETSC_DIR’,
and ‘PETSC_ARCH’ indicating where you have built/installed SLEPc and
PETSc:
$ export SLEPC_DIR=/usr/local/slepc
$ export PETSC_DIR=/usr/local/petsc
$ export PETSC_ARCH=arch-linux2-c-opt
Alternatively, you can edit the file ‘setup.cfg’ and provide the
required information below the ‘[config]’ section:
[config]
slepc_dir = /usr/local/slepc
petsc_dir = /usr/local/petsc
petsc_arch = arch-linux2-c-opt
...
Finally, you can build the distribution by typing:
$ python setup.py build
File: slepc4py.info, Node: Installing, Prev: Building, Up: Using distutils
1.3.2.4 Installing
..................
After building, the distribution is ready for installation.
If you have root privileges (either by log-in as the root user of by
using `sudo') and you want to install `SLEPc for Python' in your system
for all users, just do:
$ python setup.py install
The previous steps will install the ‘slepc4py’ package at standard
location ‘`prefix'/lib/python`X'.`X'/site-packages’.
If you do not have root privileges or you want to install `SLEPc for
Python' for your private use, just do:
$ python setup.py install --user
File: slepc4py.info, Node: Citations, Prev: Installation, Up: Contents
1.4 Citations
=============
If SLEPc for Python been significant to a project that leads to an
academic publication, please acknowledge that fact by citing the
project.
* L. Dalcin, P. Kler, R. Paz, and A. Cosimo, `Parallel Distributed
Computing using Python', Advances in Water Resources,
34(9):1124-1139, 2011.
‘http://dx.doi.org/10.1016/j.advwatres.2011.04.013’
* V. Hernandez, J.E. Roman, and V. Vidal, `SLEPc: A scalable and
flexible toolkit for the solution of eigenvalue problems', ACM
Transactions on Mathematical Software, 31(3):351-362, 2005.
‘http://dx.doi.org/10.1145/1089014.1089019’
File: slepc4py.info, Node: Index, Prev: Contents, Up: Top
Index
*****
[index ]
* Menu:
* ARCHFLAGS: Building. (line 15)
* environment variable; ARCHFLAGS: Building. (line 15)
* environment variable; MACOSX_DEPLOYMENT_TARGET: Building. (line 14)
* environment variable; PETSC_ARCH: Using pip or easy_install.
(line 18)
* environment variable; PETSC_ARCH <1>: Building. (line 28)
* environment variable; PETSC_DIR: Using pip or easy_install.
(line 18)
* environment variable; PETSC_DIR <1>: Building. (line 27)
* environment variable; SDKROOT: Building. (line 15)
* environment variable; SLEPC_DIR: Using pip or easy_install.
(line 18)
* environment variable; SLEPC_DIR‘: Building. (line 27)
* MACOSX_DEPLOYMENT_TARGET: Building. (line 14)
* PETSC_ARCH: Using pip or easy_install.
(line 18)
* PETSC_ARCH <1>: Building. (line 28)
* PETSC_DIR: Using pip or easy_install.
(line 18)
* PETSC_DIR <1>: Building. (line 27)
* SDKROOT: Building. (line 15)
* SLEPC_DIR: Using pip or easy_install.
(line 18)
* SLEPC_DIR‘: Building. (line 27)
Tag Table:
Node: Top334
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Node: Contents2054
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slepc4py-3.7.0/docs/source/manual.rst 0000644 0001750 0000144 00000000200 12705464414 020307 0 ustar dalcinl users 0000000 0000000 ================
SLEPc for Python
================
.. include:: abstract.txt
.. include:: toctree.txt
.. include:: links.txt
slepc4py-3.7.0/docs/source/tutorial.rst 0000644 0001750 0000144 00000017336 12705464414 020717 0 ustar dalcinl users 0000000 0000000 Tutorial
========
This tutorial is intended for basic use of slepc4py. For more advanced
use, the reader is referred to SLEPc tutorials as well as to slepc4py
reference documentation.
Commented source of a simple example
------------------------------------
In this section, we include the source code of example ``demo/ex1.py``
available in the slepc4py distribution, with comments inserted inline.
The first thing to do is initialize the libraries. This is normally
not required, as it is done automatically at import time. However, if
you want to gain access to the facilities for accesing command-line
options, the following lines must be executed by the main script prior
to any petsc4py or slepc4py calls::
import sys, slepc4py
slepc4py.init(sys.argv)
Next, we have to import the relevant modules. Normally, both PETSc and
SLEPc modules have to be imported in all slepc4py programs. It may be
useful to import NumPy as well::
from petsc4py import PETSc
from slepc4py import SLEPc
import numpy
At this point, we can use any petsc4py and slepc4py operations. For
instance, the following lines allow the user to specify an integer
command-line argument ``n`` with a default value of 30 (see the next
section for example usage of command-line options)::
opts = PETSc.Options()
n = opts.getInt('n', 30)
It is necessary to build a matrix to define an eigenproblem (or two in
the case of generalized eigenproblems). The following fragment of code
creates the matrix object and then fills the non-zero elements one by
one. The matrix of this particular example is tridiagonal, with value
2 in the diagonal, and -1 in off-diagonal positions. See petsc4py
documentation for details about matrix objects::
A = PETSc.Mat().create()
A.setSizes([n, n])
A.setFromOptions()
A.setUp()
rstart, rend = A.getOwnershipRange()
# first row
if rstart == 0:
A[0, :2] = [2, -1]
rstart += 1
# last row
if rend == n:
A[n-1, -2:] = [-1, 2]
rend -= 1
# other rows
for i in range(rstart, rend):
A[i, i-1:i+2] = [-1, 2, -1]
A.assemble()
The solver object is created in a similar way as other objects in
petsc4py::
E = SLEPc.EPS(); E.create()
Once the object is created, the eigenvalue problem must be
specified. At least one matrix must be provided. The problem type must
be indicated as well, in this case it is HEP (Hermitian eigenvalue
problem). Apart from these, other settings could be provided here (for
instance, the tolerance for the computation). After all options have
been set, the user should call the ``setFromOptions()`` operation, so
that any options specified at run time in the command line are passed
to the solver object::
E.setOperators(A)
E.setProblemType(SLEPc.EPS.ProblemType.HEP)
E.setFromOptions()
After that, the ``solve()`` method will run the selected eigensolver,
keeping the solution stored internally::
E.solve()
Once the computation has finished, we are ready to print the results.
First, some informative data can be retrieved from the solver object::
Print = PETSc.Sys.Print
Print()
Print("******************************")
Print("*** SLEPc Solution Results ***")
Print("******************************")
Print()
its = E.getIterationNumber()
Print("Number of iterations of the method: %d" % its)
eps_type = E.getType()
Print("Solution method: %s" % eps_type)
nev, ncv, mpd = E.getDimensions()
Print("Number of requested eigenvalues: %d" % nev)
tol, maxit = E.getTolerances()
Print("Stopping condition: tol=%.4g, maxit=%d" % (tol, maxit))
For retrieving the solution, it is necessary to find out how many
eigenpairs have converged to the requested precision::
nconv = E.getConverged()
Print("Number of converged eigenpairs %d" % nconv)
For each of the ``nconv`` eigenpairs, we can retrieve the eigenvalue
``k``, and the eigenvector, which is represented by means of two
petsc4py vectors ``vr`` and ``vi`` (the real and imaginary part of the
eigenvector, since for real matrices the eigenvalue and eigenvector
may be complex). We also compute the corresponding relative errors in
order to make sure that the computed solution is indeed correct::
if nconv > 0:
# Create the results vectors
vr, wr = A.getVecs()
vi, wi = A.getVecs()
#
Print()
Print(" k ||Ax-kx||/||kx|| ")
Print("----------------- ------------------")
for i in range(nconv):
k = E.getEigenpair(i, vr, vi)
error = E.computeError(i)
if k.imag != 0.0:
Print(" %9f%+9f j %12g" % (k.real, k.imag, error))
else:
Print(" %12f %12g" % (k.real, error))
Print()
Example of command-line usage
-----------------------------
Now we illustrate how to specify command-line options in order to
extract the full potential of slepc4py.
A simple execution of the ``demo/ex1.py`` script will result in the
following output::
$ python demo/ex1.py
******************************
*** SLEPc Solution Results ***
******************************
Number of iterations of the method: 4
Solution method: krylovschur
Number of requested eigenvalues: 1
Stopping condition: tol=1e-07, maxit=100
Number of converged eigenpairs 4
k ||Ax-kx||/||kx||
----------------- ------------------
3.989739 5.76012e-09
3.959060 1.41957e-08
3.908279 6.74118e-08
3.837916 8.34269e-08
For specifying different setting for the solver parameters, we can use
SLEPc command-line options with the ``-eps`` prefix. For instance, to
change the number of requested eigenvalues and the tolerance::
$ python demo/ex1.py -eps_nev 10 -eps_tol 1e-11
The method used by the solver object can also be set at run time::
$ python demo/ex1.py -eps_type lanczos
All the above settings can also be change within the source code by
making use of the appropriate slepc4py method. Since options can be
set from within the code and the command-line, it is often useful to
view the particular settings that are currently being used::
$ python demo/ex1.py -eps_view
EPS Object: 1 MPI processes
type: krylovschur
Krylov-Schur: 50% of basis vectors kept after restart
problem type: symmetric eigenvalue problem
selected portion of the spectrum: largest eigenvalues in magnitude
number of eigenvalues (nev): 1
number of column vectors (ncv): 16
maximum dimension of projected problem (mpd): 16
maximum number of iterations: 100
tolerance: 1e-08
convergence test: relative to the eigenvalue
BV Object: 1 MPI processes
type: svec
17 columns of global length 30
orthogonalization method: classical Gram-Schmidt
orthogonalization refinement: if needed (eta: 0.7071)
DS Object: 1 MPI processes
type: hep
solving the problem with: Implicit QR method (_steqr)
ST Object: 1 MPI processes
type: shift
shift: 0
number of matrices: 1
Note that for computing eigenvalues of smallest magnitude we can use
the option ``-eps_smallest_magnitude``, but for interior eigenvalues
things are not so straightforward. One possibility is to try with
harmonic extraction, for instance to get the eigenvalues closest to
0.6::
$ python demo/ex1.py -eps_harmonic -eps_target 0.6
Depending on the problem, harmonic extraction may fail to converge. In
those cases, it is necessary to specify a spectral transformation
other than the default. In the command-line, this is indicated with
the ``-st_`` prefix. For example, shift-and-invert with a value of the
shift equal to 0.6 would be::
$ python demo/ex1.py -st_type sinvert -eps_target 0.6
slepc4py-3.7.0/docs/source/Makefile 0000644 0001750 0000144 00000014252 12705464414 017754 0 ustar dalcinl users 0000000 0000000 # Makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = _build
# Internal variables.
PAPEROPT_a4 = -D latex_paper_size=a4
PAPEROPT_letter = -D latex_paper_size=letter
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
# the i18n builder cannot share the environment and doctrees with the others
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
help:
@echo "Please use \`make ' where is one of"
@echo " html to make standalone HTML files"
@echo " dirhtml to make HTML files named index.html in directories"
@echo " singlehtml to make a single large HTML file"
@echo " pickle to make pickle files"
@echo " json to make JSON files"
@echo " htmlhelp to make HTML files and a HTML help project"
@echo " qthelp to make HTML files and a qthelp project"
@echo " devhelp to make HTML files and a Devhelp project"
@echo " epub to make an epub"
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
@echo " latexpdf to make LaTeX files and run them through pdflatex"
@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
@echo " text to make text files"
@echo " man to make manual pages"
@echo " texinfo to make Texinfo files"
@echo " info to make Texinfo files and run them through makeinfo"
@echo " gettext to make PO message catalogs"
@echo " changes to make an overview of all changed/added/deprecated items"
@echo " xml to make Docutils-native XML files"
@echo " pseudoxml to make pseudoxml-XML files for display purposes"
@echo " linkcheck to check all external links for integrity"
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
clean:
rm -rf $(BUILDDIR)/*
html:
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
@echo
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
pickle:
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@echo
@echo "Build finished; now you can process the pickle files."
json:
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
@echo
@echo "Build finished; now you can process the JSON files."
htmlhelp:
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
@echo
@echo "Build finished; now you can run HTML Help Workshop with the" \
".hhp project file in $(BUILDDIR)/htmlhelp."
qthelp:
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
@echo
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/slepc4py.qhcp"
@echo "To view the help file:"
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/slepc4py.qhc"
devhelp:
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
@echo
@echo "Build finished."
@echo "To view the help file:"
@echo "# mkdir -p $$HOME/.local/share/devhelp/slepc4py"
@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/slepc4py"
@echo "# devhelp"
epub:
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
@echo
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
latex:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
@echo "Run \`make' in that directory to run these through (pdf)latex" \
"(use \`make latexpdf' here to do that automatically)."
latexpdf:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through pdflatex..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
latexpdfja:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through platex and dvipdfmx..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
text:
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
@echo
@echo "Build finished. The text files are in $(BUILDDIR)/text."
man:
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
@echo
@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
texinfo:
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
@echo
@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
@echo "Run \`make' in that directory to run these through makeinfo" \
"(use \`make info' here to do that automatically)."
info:
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
@echo "Running Texinfo files through makeinfo..."
make -C $(BUILDDIR)/texinfo info
@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
gettext:
$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
@echo
@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
changes:
$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
@echo
@echo "The overview file is in $(BUILDDIR)/changes."
linkcheck:
$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
@echo
@echo "Link check complete; look for any errors in the above output " \
"or in $(BUILDDIR)/linkcheck/output.txt."
doctest:
$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
@echo "Testing of doctests in the sources finished, look at the " \
"results in $(BUILDDIR)/doctest/output.txt."
xml:
$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
@echo
@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
pseudoxml:
$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
@echo
@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
slepc4py-3.7.0/docs/source/install.rst 0000644 0001750 0000144 00000007736 12705464414 020525 0 ustar dalcinl users 0000000 0000000 Installation
============
Using **pip** or **easy_install**
---------------------------------
You can use :program:`pip` to install :mod:`slepc4py` and its
dependencies (:mod:`mpi4py` is optional but highly recommended)::
$ pip install [--user] numpy mpi4py
$ pip install [--user] petsc petsc4py
$ pip install [--user] slepc slepc4py
Alternatively, you can use :program:`easy_install` (deprecated)::
$ easy_install [--user] slepc4py
If you already have working PETSc and SLEPc installs, set environment
variables :envvar:`SLEPC_DIR` and :envvar:`PETSC_DIR` (and perhaps
:envvar:`PETSC_ARCH` for non-prefix installs) to appropriate values
and next use :program:`pip`::
$ export SLEPC_DIR=/path/to/slepc
$ export PETSC_DIR=/path/to/petsc
$ export PETSC_ARCH=arch-linux2-c-opt
$ pip install [--user] petsc4py slepc4py
Using **distutils**
-------------------
Requirements
^^^^^^^^^^^^
You need to have the following software properly installed in order to
build *SLEPc for Python*:
* Any MPI_ implementation [#]_ (e.g., MPICH_ or `Open MPI`_),
built with shared libraries.
* A matching version of PETSc_ built with shared libraries.
* A matching version of SLEPc_ built with shared libraries.
* NumPy_ package.
* petsc4py_ package.
.. [#] Unless you have appropriately configured and built SLEPc and
PETSc without MPI (configure option ``--with-mpi=0``).
.. [#] You may need to use a parallelized version of the Python
interpreter with some MPI-1 implementations (e.g. MPICH1).
.. include:: links.txt
Downloading
^^^^^^^^^^^
The *SLEPc for Python* package is available for download at the
project website generously hosted by Bitbucket. You can use
:program:`curl` or :program:`wget` to get a release tarball.
* Using :program:`curl`::
$ curl -O https://bitbucket.org/slepc/slepc4py/downloads/slepc4py-X.Y.tar.gz
* Using :program:`wget`::
$ wget https://bitbucket.org/slepc/slepc4py/downloads/slepc4py-X.Y.tar.gz
Building
^^^^^^^^
After unpacking the release tarball::
$ tar -zxf slepc4py-X.Y.tar.gz
$ cd slepc4py-X.Y
the distribution is ready for building.
.. note:: **Mac OS X** users employing a Python distribution built
with **universal binaries** may need to set the environment
variables :envvar:`MACOSX_DEPLOYMENT_TARGET`, :envvar:`SDKROOT`,
and :envvar:`ARCHFLAGS` to appropriate values. As an example,
assume your Mac is running **Snow Leopard** on a **64-bit Intel**
processor and you want to override the hard-wired cross-development
SDK in Python configuration, your environment should be modified
like this::
$ export MACOSX_DEPLOYMENT_TARGET=10.6
$ export SDKROOT=/
$ export ARCHFLAGS='-arch x86_64'
Some environment configuration is needed to inform the location of
PETSc and SLEPc. You can set (using :command:`setenv`,
:command:`export` or what applies to you shell or system) the
environment variables :envvar:`SLEPC_DIR``, :envvar:`PETSC_DIR`, and
:envvar:`PETSC_ARCH` indicating where you have built/installed SLEPc
and PETSc::
$ export SLEPC_DIR=/usr/local/slepc
$ export PETSC_DIR=/usr/local/petsc
$ export PETSC_ARCH=arch-linux2-c-opt
Alternatively, you can edit the file :file:`setup.cfg` and provide the
required information below the ``[config]`` section::
[config]
slepc_dir = /usr/local/slepc
petsc_dir = /usr/local/petsc
petsc_arch = arch-linux2-c-opt
...
Finally, you can build the distribution by typing::
$ python setup.py build
Installing
^^^^^^^^^^
After building, the distribution is ready for installation.
If you have root privileges (either by log-in as the root user of by
using :command:`sudo`) and you want to install *SLEPc for Python* in
your system for all users, just do::
$ python setup.py install
The previous steps will install the :mod:`slepc4py` package at standard
location :file:`{prefix}/lib/python{X}.{X}/site-packages`.
If you do not have root privileges or you want to install *SLEPc for
Python* for your private use, just do::
$ python setup.py install --user
slepc4py-3.7.0/docs/source/abstract.txt 0000644 0001750 0000144 00000001223 12705464414 020652 0 ustar dalcinl users 0000000 0000000 .. topic:: Abstract
This document describes slepc4py_, a Python_ port to the SLEPc_
libraries.
SLEPc_ is a software package for the parallel solution of
large-scale eigenvalue problems. It can be used for computing
eigenvalues and eigenvectors of large, sparse matrices, or matrix
pairs, and also for computing singular values and vectors of a
rectangular matrix.
SLEPc_ relies on PETSc_ for basic functionality such as the
representation of matrices and vectors, and the solution of linear
systems of equations. Thus, slepc4py_ must be used together with
its companion petsc4py_.
.. Local Variables:
.. mode: rst
.. End:
slepc4py-3.7.0/docs/source/conf.py 0000644 0001750 0000144 00000024420 12716602462 017610 0 ustar dalcinl users 0000000 0000000 # -*- coding: utf-8 -*-
#
# SLEPc for Python documentation build configuration file, created by
# sphinx-quickstart on Sun Aug 17 12:57:45 2014.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
try: from slepc4py import __version__ as slepc4py_version
except: slepc4py_version = 'X.X.X'
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = []
# Add any paths that contain templates here, relative to this directory.
#templates_path = ['_templates']
templates_path = []
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'SLEPc for Python'
copyright = u'2016, Lisandro Dalcin'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = slepc4py_version[:3]
# The full version, including alpha/beta/rc tags.
release = slepc4py_version
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# " v documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
#html_static_path = ['_static']
html_static_path = []
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a ` where ^ is one of
echo. html to make standalone HTML files
echo. dirhtml to make HTML files named index.html in directories
echo. singlehtml to make a single large HTML file
echo. pickle to make pickle files
echo. json to make JSON files
echo. htmlhelp to make HTML files and a HTML help project
echo. qthelp to make HTML files and a qthelp project
echo. devhelp to make HTML files and a Devhelp project
echo. epub to make an epub
echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter
echo. text to make text files
echo. man to make manual pages
echo. texinfo to make Texinfo files
echo. gettext to make PO message catalogs
echo. changes to make an overview over all changed/added/deprecated items
echo. xml to make Docutils-native XML files
echo. pseudoxml to make pseudoxml-XML files for display purposes
echo. linkcheck to check all external links for integrity
echo. doctest to run all doctests embedded in the documentation if enabled
goto end
)
if "%1" == "clean" (
for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
del /q /s %BUILDDIR%\*
goto end
)
if "%1" == "html" (
%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/html.
goto end
)
if "%1" == "dirhtml" (
%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
goto end
)
if "%1" == "singlehtml" (
%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
goto end
)
if "%1" == "pickle" (
%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can process the pickle files.
goto end
)
if "%1" == "json" (
%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can process the JSON files.
goto end
)
if "%1" == "htmlhelp" (
%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can run HTML Help Workshop with the ^
.hhp project file in %BUILDDIR%/htmlhelp.
goto end
)
if "%1" == "qthelp" (
%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can run "qcollectiongenerator" with the ^
.qhcp project file in %BUILDDIR%/qthelp, like this:
echo.^> qcollectiongenerator %BUILDDIR%\qthelp\slepc4py.qhcp
echo.To view the help file:
echo.^> assistant -collectionFile %BUILDDIR%\qthelp\slepc4py.ghc
goto end
)
if "%1" == "devhelp" (
%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished.
goto end
)
if "%1" == "epub" (
%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The epub file is in %BUILDDIR%/epub.
goto end
)
if "%1" == "latex" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
if errorlevel 1 exit /b 1
echo.
echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "latexpdf" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
cd %BUILDDIR%/latex
make all-pdf
cd %BUILDDIR%/..
echo.
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "latexpdfja" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
cd %BUILDDIR%/latex
make all-pdf-ja
cd %BUILDDIR%/..
echo.
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "text" (
%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The text files are in %BUILDDIR%/text.
goto end
)
if "%1" == "man" (
%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The manual pages are in %BUILDDIR%/man.
goto end
)
if "%1" == "texinfo" (
%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
goto end
)
if "%1" == "gettext" (
%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
goto end
)
if "%1" == "changes" (
%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
if errorlevel 1 exit /b 1
echo.
echo.The overview file is in %BUILDDIR%/changes.
goto end
)
if "%1" == "linkcheck" (
%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
if errorlevel 1 exit /b 1
echo.
echo.Link check complete; look for any errors in the above output ^
or in %BUILDDIR%/linkcheck/output.txt.
goto end
)
if "%1" == "doctest" (
%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
if errorlevel 1 exit /b 1
echo.
echo.Testing of doctests in the sources finished, look at the ^
results in %BUILDDIR%/doctest/output.txt.
goto end
)
if "%1" == "xml" (
%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The XML files are in %BUILDDIR%/xml.
goto end
)
if "%1" == "pseudoxml" (
%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
goto end
)
:end
slepc4py-3.7.0/docs/source/links.txt 0000644 0001750 0000144 00000000765 12716603721 020177 0 ustar dalcinl users 0000000 0000000 .. _MPI: http://www.mpi-forum.org
.. _MPICH: http://www.mpich.org
.. _Open MPI: http://www.open-mpi.org
.. _PETSc: http://www.mcs.anl.gov/petsc/
.. _SLEPc: http://slepc.upv.es
.. _Python: http://www.python.org
.. _NumPy: http://www.numpy.org
.. _mpi4py: http://bitbucket.org/mpi4py/mpi4py
.. _petsc4py: http://bitbucket.org/petsc/petsc4py
.. _slepc4py: http://bitbucket.org/slepc/slepc4py
.. Local Variables:
.. mode: rst
.. End:
slepc4py-3.7.0/docs/source/index.rst 0000644 0001750 0000144 00000000502 12716603616 020147 0 ustar dalcinl users 0000000 0000000 ================
SLEPc for Python
================
:Authors: Lisandro Dalcin, Jose E. Roman
:Contact: dalcinl@gmail.com, jroman@dsic.upv.es
:Web Site: https://bitbucket.org/slepc/slepc4py
:Date: |today|
.. include:: abstract.txt
Contents
========
.. include:: toctree.txt
.. include:: links.txt
slepc4py-3.7.0/docs/source/citing.rst 0000644 0001750 0000144 00000001130 12716604206 020307 0 ustar dalcinl users 0000000 0000000 Citations
=========
If SLEPc for Python been significant to a project that leads to an
academic publication, please acknowledge that fact by citing the
project.
* L. Dalcin, P. Kler, R. Paz, and A. Cosimo,
*Parallel Distributed Computing using Python*,
Advances in Water Resources, 34(9):1124-1139, 2011.
http://dx.doi.org/10.1016/j.advwatres.2011.04.013
* V. Hernandez, J.E. Roman, and V. Vidal,
*SLEPc: A scalable and flexible toolkit for the solution of eigenvalue problems*,
ACM Transactions on Mathematical Software, 31(3):351-362, 2005.
http://dx.doi.org/10.1145/1089014.1089019
slepc4py-3.7.0/docs/usrman/ 0000755 0001750 0000144 00000000000 12720314530 016303 5 ustar dalcinl users 0000000 0000000 slepc4py-3.7.0/docs/usrman/search.html 0000644 0001750 0000144 00000006215 12720314514 020444 0 ustar dalcinl users 0000000 0000000
Search — SLEPc for Python 3.7.0 documentation
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functionality.
From here you can search these documents. Enter your search
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function will automatically search for all of the words. Pages
containing fewer words won't appear in the result list.
slepc4py-3.7.0/docs/usrman/genindex.html 0000644 0001750 0000144 00000011716 12720314514 021002 0 ustar dalcinl users 0000000 0000000
Index — SLEPc for Python 3.7.0 documentation
SLEPc for Python — SLEPc for Python 3.7.0 documentation
SLEPc for Python
Abstract
This document describes slepc4py , a Python port to the SLEPc
libraries.
SLEPc is a software package for the parallel solution of
large-scale eigenvalue problems. It can be used for computing
eigenvalues and eigenvectors of large, sparse matrices, or matrix
pairs, and also for computing singular values and vectors of a
rectangular matrix.
SLEPc relies on PETSc for basic functionality such as the
representation of matrices and vectors, and the solution of linear
systems of equations. Thus, slepc4py must be used together with
its companion petsc4py .
slepc4py-3.7.0/docs/usrman/manual.html 0000644 0001750 0000144 00000012574 12720314514 020461 0 ustar dalcinl users 0000000 0000000
SLEPc for Python — SLEPc for Python 3.7.0 documentation
SLEPc for Python
Abstract
This document describes slepc4py , a Python port to the SLEPc
libraries.
SLEPc is a software package for the parallel solution of
large-scale eigenvalue problems. It can be used for computing
eigenvalues and eigenvectors of large, sparse matrices, or matrix
pairs, and also for computing singular values and vectors of a
rectangular matrix.
SLEPc relies on PETSc for basic functionality such as the
representation of matrices and vectors, and the solution of linear
systems of equations. Thus, slepc4py must be used together with
its companion petsc4py .
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