/usr/lib/python2.7/dist-packages/dolfin/cpp/la.py is in python-dolfin 1.3.0+dfsg-2.
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# Version 2.0.11
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (3,0,0):
new_instancemethod = lambda func, inst, cls: _la.SWIG_PyInstanceMethod_New(func)
else:
from new import instancemethod as new_instancemethod
if version_info >= (2,6,0):
def swig_import_helper():
from os.path import dirname
import imp
fp = None
try:
fp, pathname, description = imp.find_module('_la', [dirname(__file__)])
except ImportError:
import _la
return _la
if fp is not None:
try:
_mod = imp.load_module('_la', fp, pathname, description)
finally:
fp.close()
return _mod
_la = swig_import_helper()
del swig_import_helper
else:
import _la
del version_info
try:
_swig_property = property
except NameError:
pass # Python < 2.2 doesn't have 'property'.
def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
if (name == "thisown"): return self.this.own(value)
if (name == "this"):
if type(value).__name__ == 'SwigPyObject':
self.__dict__[name] = value
return
method = class_type.__swig_setmethods__.get(name,None)
if method: return method(self,value)
if (not static):
self.__dict__[name] = value
else:
raise AttributeError("You cannot add attributes to %s" % self)
def _swig_setattr(self,class_type,name,value):
return _swig_setattr_nondynamic(self,class_type,name,value,0)
def _swig_getattr(self,class_type,name):
if (name == "thisown"): return self.this.own()
method = class_type.__swig_getmethods__.get(name,None)
if method: return method(self)
raise AttributeError(name)
def _swig_repr(self):
try: strthis = "proxy of " + self.this.__repr__()
except: strthis = ""
return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
try:
_object = object
_newclass = 1
except AttributeError:
class _object : pass
_newclass = 0
def _swig_setattr_nondynamic_method(set):
def set_attr(self,name,value):
if (name == "thisown"): return self.this.own(value)
if hasattr(self,name) or (name == "this"):
set(self,name,value)
else:
raise AttributeError("You cannot add attributes to %s" % self)
return set_attr
try:
import weakref
weakref_proxy = weakref.proxy
except:
weakref_proxy = lambda x: x
SHARED_PTR_DISOWN = _la.SHARED_PTR_DISOWN
import ufc
def _attach_base_to_numpy_array(*args):
return _la._attach_base_to_numpy_array(*args)
_attach_base_to_numpy_array = _la._attach_base_to_numpy_array
def dolfin_swigversion(*args):
return _la.dolfin_swigversion(*args)
dolfin_swigversion = _la.dolfin_swigversion
tmp = hex(dolfin_swigversion())
__swigversion__ = "%d.%d.%d"%(tuple(map(int, [tmp[-5], tmp[-3], tmp[-2:]])))
del tmp, dolfin_swigversion
def has_petsc4py(*args):
return _la.has_petsc4py(*args)
has_petsc4py = _la.has_petsc4py
import common
import mesh
class LinearAlgebraObject(common.Variable):
"""
This is a common base class for all DOLFIN linear algebra
objects. In particular, it provides casting mechanisms between
different types.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def shared_instance(self, *args):
"""
**Overloaded versions**
* shared_instance\ ()
Return concrete shared ptr instance / unwrap (const version)
* shared_instance\ ()
Return concrete shared ptr instance / unwrap (non-const version)
"""
return _la.LinearAlgebraObject_shared_instance(self, *args)
def __init__(self, *args):
_la.LinearAlgebraObject_swiginit(self,_la.new_LinearAlgebraObject(*args))
__swig_destroy__ = _la.delete_LinearAlgebraObject
LinearAlgebraObject.shared_instance = new_instancemethod(_la.LinearAlgebraObject_shared_instance,None,LinearAlgebraObject)
LinearAlgebraObject_swigregister = _la.LinearAlgebraObject_swigregister
LinearAlgebraObject_swigregister(LinearAlgebraObject)
class GenericLinearOperator(LinearAlgebraObject):
"""
This class defines a common interface for linear operators,
including actual matrices (class :py:class:`GenericMatrix`) and linear
operators only defined in terms of their action on vectors.
This class is used internally by DOLFIN to define a class
hierarchy of backend independent linear operators and solvers.
Users should not interface to this class directly but instead
use the :py:class:`LinearOperator` class.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_GenericLinearOperator
def size(self, *args):
"""
Return size of given dimension
"""
return _la.GenericLinearOperator_size(self, *args)
def mult(self, *args):
"""
Compute matrix-vector product y = Ax
"""
return _la.GenericLinearOperator_mult(self, *args)
GenericLinearOperator.size = new_instancemethod(_la.GenericLinearOperator_size,None,GenericLinearOperator)
GenericLinearOperator.mult = new_instancemethod(_la.GenericLinearOperator_mult,None,GenericLinearOperator)
GenericLinearOperator_swigregister = _la.GenericLinearOperator_swigregister
GenericLinearOperator_swigregister(GenericLinearOperator)
class GenericTensor(LinearAlgebraObject):
"""
This class defines a common interface for arbitrary rank tensors.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_GenericTensor
def init(self, *args):
"""
Initialize zero tensor using tensor layout
"""
return _la.GenericTensor_init(self, *args)
def rank(self, *args):
"""
Return tensor rank (number of dimensions)
"""
return _la.GenericTensor_rank(self, *args)
def size(self, *args):
"""
Return size of given dimension
"""
return _la.GenericTensor_size(self, *args)
def local_range(self, *args):
"""
Return local ownership range
"""
return _la.GenericTensor_local_range(self, *args)
def add(self, *args):
"""
**Overloaded versions**
* add\ (block, rows)
Add block of values
* add\ (block, rows)
Add block of values
* add\ (block, num_rows, rows)
Add block of values
"""
return _la.GenericTensor_add(self, *args)
def zero(self, *args):
"""
Set all entries to zero and keep any sparse structure
"""
return _la.GenericTensor_zero(self, *args)
def apply(self, *args):
"""
Finalize assembly of tensor
"""
return _la.GenericTensor_apply(self, *args)
def factory(self, *args):
"""
Return linear algebra backend factory
"""
return _la.GenericTensor_factory(self, *args)
GenericTensor.init = new_instancemethod(_la.GenericTensor_init,None,GenericTensor)
GenericTensor.rank = new_instancemethod(_la.GenericTensor_rank,None,GenericTensor)
GenericTensor.size = new_instancemethod(_la.GenericTensor_size,None,GenericTensor)
GenericTensor.local_range = new_instancemethod(_la.GenericTensor_local_range,None,GenericTensor)
GenericTensor.add = new_instancemethod(_la.GenericTensor_add,None,GenericTensor)
GenericTensor.zero = new_instancemethod(_la.GenericTensor_zero,None,GenericTensor)
GenericTensor.apply = new_instancemethod(_la.GenericTensor_apply,None,GenericTensor)
GenericTensor.factory = new_instancemethod(_la.GenericTensor_factory,None,GenericTensor)
GenericTensor_swigregister = _la.GenericTensor_swigregister
GenericTensor_swigregister(GenericTensor)
class GenericMatrix(GenericTensor,GenericLinearOperator):
"""
This class defines a common interface for matrices.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_GenericMatrix
def copy(self, *args):
"""
Return copy of matrix
"""
return _la.GenericMatrix_copy(self, *args)
def resize(self, *args):
"""
Resize vector z to be compatible with the matrix-vector
product y = Ax. In the parallel case, both size and layout are
important.
*Arguments*
dim (int)
The dimension (axis): dim = 0 --> z = y, dim = 1 --> z = x
"""
return _la.GenericMatrix_resize(self, *args)
def get(self, *args):
"""
**Overloaded versions**
* get\ (block, num_rows, rows)
Get block of values
* get\ (block, m, rows, n, cols)
Get block of values
"""
return _la.GenericMatrix_get(self, *args)
def set(self, *args):
"""
**Overloaded versions**
* set\ (block, num_rows, rows)
Set block of values
* set\ (block, m, rows, n, cols)
Set block of values
"""
return _la.GenericMatrix_set(self, *args)
def add(self, *args):
"""
**Overloaded versions**
* add\ (block, num_rows, rows)
Add block of values
* add\ (block, rows)
Add block of values
* add\ (block, rows)
Add block of values
* add\ (block, m, rows, n, cols)
Add block of values
"""
return _la.GenericMatrix_add(self, *args)
def axpy(self, *args):
"""
Add multiple of given matrix (AXPY operation)
"""
return _la.GenericMatrix_axpy(self, *args)
def norm(self, *args):
"""
Return norm of matrix
"""
return _la.GenericMatrix_norm(self, *args)
def getrow(self, *args):
"""
Get non-zero values of given row on local process
"""
return _la.GenericMatrix_getrow(self, *args)
def setrow(self, *args):
"""
Set values for given row on local process
"""
return _la.GenericMatrix_setrow(self, *args)
def zero(self, *args):
"""
**Overloaded versions**
* zero\ ()
Set all entries to zero and keep any sparse structure
* zero\ (m, rows)
Set given rows to zero
"""
return _la.GenericMatrix_zero(self, *args)
def ident(self, *args):
"""
Set given rows to identity matrix
"""
return _la.GenericMatrix_ident(self, *args)
def transpmult(self, *args):
"""
Matrix-vector product, y = A^T x. The y vector must either be
zero-sized or have correct size and parallel layout.
"""
return _la.GenericMatrix_transpmult(self, *args)
def is_symmetric(self, *args):
"""
Test if matrix is symmetric
"""
return _la.GenericMatrix_is_symmetric(self, *args)
def assign(self, *args):
"""
Assignment operator
"""
return _la.GenericMatrix_assign(self, *args)
def ident_zeros(self, *args):
"""
Insert one on the diagonal for all zero rows
"""
return _la.GenericMatrix_ident_zeros(self, *args)
def compress(self, *args):
"""
Compress matrix
"""
return _la.GenericMatrix_compress(self, *args)
def _scale(self, *args):
"""Missing docstring"""
return _la.GenericMatrix__scale(self, *args)
def _data(self, *args):
"""Missing docstring"""
return _la.GenericMatrix__data(self, *args)
def __is_compatible(self,other):
"Returns True if self, and other are compatible Vectors"
if not isinstance(other,GenericMatrix):
return False
self_type = get_tensor_type(self)
return self_type == get_tensor_type(other)
def array(self):
"Return a numpy array representation of Matrix"
from numpy import zeros
m_range = self.local_range(0);
A = zeros((m_range[1] - m_range[0], self.size(1)))
for i, row in enumerate(xrange(*m_range)):
column, values = self.getrow(row)
A[i, column] = values
return A
def sparray(self):
"Return a scipy.sparse representation of Matrix"
from scipy.sparse import csr_matrix
data = self.data(deepcopy=True)
C = csr_matrix((data[2], data[1], data[0]))
return C
def data(self, deepcopy=True):
"""
Return arrays to underlaying compresssed row/column storage data
This method is only available for the uBLAS linear algebra
backend.
*Arguments*
deepcopy
Return a copy of the data. If set to False a reference
to the Matrix need to be kept, otherwise the data will be
destroyed together with the destruction of the Matrix
"""
rows, cols, values = self._data()
if deepcopy:
rows, cols, values = rows.astype(int), cols.astype(int), values.copy()
else:
_attach_base_to_numpy_array(rows, self)
_attach_base_to_numpy_array(cols, self)
_attach_base_to_numpy_array(values, self)
return rows, cols, values
# FIXME: Getting matrix entries need to be carefully examined, especially for
# parallel objects.
"""
def __getitem__(self,indices):
from numpy import ndarray
from types import SliceType
if not (isinstance(indices, tuple) and len(indices) == 2):
raise TypeError, "expected two indices"
if not all(isinstance(ind, (int, SliceType, list, ndarray)) for ind in indices):
raise TypeError, "an int, slice, list or numpy array as indices"
if isinstance(indices[0], int):
if isinstance(indices[1], int):
return _get_matrix_single_item(self,indices[0],indices[1])
return as_backend_type(_get_matrix_sub_vector(self,indices[0], indices[1], True))
elif isinstance(indices[1],int):
return as_backend_type(_get_matrix_sub_vector(self,indices[1], indices[0], False))
else:
same_indices = id(indices[0]) == id(indices[1])
if not same_indices and ( type(indices[0]) == type(indices[1]) ):
if isinstance(indices[0],(list,SliceType)):
same_indices = indices[0] == indices[1]
else:
same_indices = (indices[0] == indices[1]).all()
if same_indices:
return as_backend_type(_get_matrix_sub_matrix(self, indices[0], None))
else:
return as_backend_type(_get_matrix_sub_matrix(self, indices[0], indices[1]))
def __setitem__(self, indices, values):
from numpy import ndarray, isscalar
from types import SliceType
if not (isinstance(indices, tuple) and len(indices) == 2):
raise TypeError, "expected two indices"
if not all(isinstance(ind, (int, SliceType, list, ndarray)) for ind in indices):
raise TypeError, "an int, slice, list or numpy array as indices"
if isinstance(indices[0], int):
if isinstance(indices[1], int):
if not isscalar(values):
raise TypeError, "expected scalar for single value assigment"
_set_matrix_single_item(self, indices[0], indices[1], values)
else:
raise NotImplementedError
if isinstance(values,GenericVector):
_set_matrix_items_vector(self, indices[0], indices[1], values, True)
elif isinstance(values,ndarray):
_set_matrix_items_array_of_float(self, indices[0], indices[1], values, True)
else:
raise TypeError, "expected a GenericVector or numpy array of float"
elif isinstance(indices[1], int):
raise NotImplementedError
if isinstance(values, GenericVector):
_set_matrix_items_vector(self, indices[1], indices[0], values, False)
elif isinstance(values, ndarray):
_set_matrix_items_array_of_float(self, indices[1], indices[0], values, False)
else:
raise TypeError, "expected a GenericVector or numpy array of float"
else:
raise NotImplementedError
same_indices = id(indices[0]) == id(indices[1])
if not same_indices and ( type(indices[0]) == type(indices[1]) ):
if isinstance(indices[0], (list, SliceType)):
same_indices = indices[0] == indices[1]
else:
same_indices = (indices[0] == indices[1]).all()
if same_indices:
if isinstance(values,GenericMatrix):
_set_matrix_items_matrix(self, indices[0], None, values)
elif isinstance(values, ndarray) and len(values.shape)==2:
_set_matrix_items_array_of_float(self, indices[0], None, values)
else:
raise TypeError, "expected a GenericMatrix or 2D numpy array of float"
else:
if isinstance(values,GenericMatrix):
_set_matrix_items_matrix(self, indices[0], indices[1], values)
elif isinstance(values,ndarray) and len(values.shape) == 2:
_set_matrix_items_array_of_float(self, indices[0], indices[1], values)
else:
raise TypeError, "expected a GenericMatrix or 2D numpy array of float"
"""
def __add__(self,other):
"""x.__add__(y) <==> x+y"""
if self.__is_compatible(other):
ret = self.copy()
ret.axpy(1.0, other, False)
return ret
return NotImplemented
def __sub__(self,other):
"""x.__sub__(y) <==> x-y"""
if self.__is_compatible(other):
ret = self.copy()
ret.axpy(-1.0, other, False)
return ret
return NotImplemented
def __mul__(self,other):
"""x.__mul__(y) <==> x*y"""
from numpy import ndarray, isscalar
if isscalar(other):
ret = self.copy()
ret._scale(other)
return ret
elif isinstance(other,GenericVector):
matrix_type = get_tensor_type(self)
vector_type = get_tensor_type(other)
if vector_type not in _matrix_vector_mul_map[matrix_type]:
raise TypeError("Provide a Vector which can be as_backend_typeed to ''"%vector_type.__name__)
if type(other) == Vector:
ret = Vector()
else:
ret = vector_type()
self.mult(other, ret)
return ret
elif isinstance(other, ndarray):
if len(other.shape) != 1:
raise ValueError("Provide an 1D NumPy array")
vec_size = other.shape[0]
if vec_size != self.size(1):
raise ValueError("Provide a NumPy array with length %d"%self.size(1))
vec_type = _matrix_vector_mul_map[get_tensor_type(self)][0]
vec = vec_type(vec_size)
vec.set_local(other)
result_vec = vec.copy()
self.mult(vec, result_vec)
#ret = other.copy()
#result_vec.get_local(ret)
#return ret
return result_vec.get_local()
return NotImplemented
def __div__(self,other):
"""x.__div__(y) <==> x/y"""
from numpy import isscalar
if isscalar(other):
ret = self.copy()
ret._scale(1.0/other)
return ret
return NotImplemented
def __radd__(self,other):
"""x.__radd__(y) <==> y+x"""
return self.__add__(other)
def __rsub__(self,other):
"""x.__rsub__(y) <==> y-x"""
return self.__sub__(other)
def __rmul__(self,other):
"""x.__rmul__(y) <==> y*x"""
from numpy import isscalar
if isscalar(other):
ret = self.copy()
ret._scale(other)
return ret
return NotImplemented
def __rdiv__(self,other):
"""x.__rdiv__(y) <==> y/x"""
return NotImplemented
def __iadd__(self,other):
"""x.__iadd__(y) <==> x+y"""
if self.__is_compatible(other):
self.axpy(1.0, other, False)
return self
return NotImplemented
def __isub__(self,other):
"""x.__isub__(y) <==> x-y"""
if self.__is_compatible(other):
self.axpy(-1.0, other, False)
return self
return NotImplemented
def __imul__(self,other):
"""x.__imul__(y) <==> x*y"""
from numpy import isscalar
if isscalar(other):
self._scale(other)
return self
return NotImplemented
def __idiv__(self,other):
"""x.__idiv__(y) <==> x/y"""
from numpy import isscalar
if isscalar(other):
self._scale(1.0 / other)
return self
return NotImplemented
GenericMatrix.copy = new_instancemethod(_la.GenericMatrix_copy,None,GenericMatrix)
GenericMatrix.resize = new_instancemethod(_la.GenericMatrix_resize,None,GenericMatrix)
GenericMatrix.get = new_instancemethod(_la.GenericMatrix_get,None,GenericMatrix)
GenericMatrix.set = new_instancemethod(_la.GenericMatrix_set,None,GenericMatrix)
GenericMatrix.add = new_instancemethod(_la.GenericMatrix_add,None,GenericMatrix)
GenericMatrix.axpy = new_instancemethod(_la.GenericMatrix_axpy,None,GenericMatrix)
GenericMatrix.norm = new_instancemethod(_la.GenericMatrix_norm,None,GenericMatrix)
GenericMatrix.getrow = new_instancemethod(_la.GenericMatrix_getrow,None,GenericMatrix)
GenericMatrix.setrow = new_instancemethod(_la.GenericMatrix_setrow,None,GenericMatrix)
GenericMatrix.zero = new_instancemethod(_la.GenericMatrix_zero,None,GenericMatrix)
GenericMatrix.ident = new_instancemethod(_la.GenericMatrix_ident,None,GenericMatrix)
GenericMatrix.transpmult = new_instancemethod(_la.GenericMatrix_transpmult,None,GenericMatrix)
GenericMatrix.is_symmetric = new_instancemethod(_la.GenericMatrix_is_symmetric,None,GenericMatrix)
GenericMatrix.assign = new_instancemethod(_la.GenericMatrix_assign,None,GenericMatrix)
GenericMatrix.ident_zeros = new_instancemethod(_la.GenericMatrix_ident_zeros,None,GenericMatrix)
GenericMatrix.compress = new_instancemethod(_la.GenericMatrix_compress,None,GenericMatrix)
GenericMatrix._scale = new_instancemethod(_la.GenericMatrix__scale,None,GenericMatrix)
GenericMatrix._data = new_instancemethod(_la.GenericMatrix__data,None,GenericMatrix)
GenericMatrix_swigregister = _la.GenericMatrix_swigregister
GenericMatrix_swigregister(GenericMatrix)
class GenericSparsityPattern(common.Variable):
"""
Base class (interface) for generic tensor sparsity patterns.
Currently, this interface is mostly limited to matrices.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
sorted = _la.GenericSparsityPattern_sorted
unsorted = _la.GenericSparsityPattern_unsorted
__swig_destroy__ = _la.delete_GenericSparsityPattern
def init(self, *args):
"""
Initialize sparsity pattern for a generic tensor
"""
return _la.GenericSparsityPattern_init(self, *args)
def insert(self, *args):
"""
Insert non-zero entries
"""
return _la.GenericSparsityPattern_insert(self, *args)
def add_edges(self, *args):
"""
Add edges (vertex = [index, owning process])
"""
return _la.GenericSparsityPattern_add_edges(self, *args)
def rank(self, *args):
"""
Return rank
"""
return _la.GenericSparsityPattern_rank(self, *args)
def primary_dim(self, *args):
"""
Return primary dimension (e.g., 0=row partition, 1=column partition)
"""
return _la.GenericSparsityPattern_primary_dim(self, *args)
def local_range(self, *args):
"""
Return local range for dimension dim
"""
return _la.GenericSparsityPattern_local_range(self, *args)
def num_nonzeros(self, *args):
"""
Return total number of nonzeros in local_range
"""
return _la.GenericSparsityPattern_num_nonzeros(self, *args)
def num_nonzeros_diagonal(self, *args):
"""
Fill vector with number of nonzeros for diagonal block in
local_range for primary dimemsion
"""
return _la.GenericSparsityPattern_num_nonzeros_diagonal(self, *args)
def num_nonzeros_off_diagonal(self, *args):
"""
Fill vector with number of nonzeros for off-diagonal block in
local_range for primary dimemsion
"""
return _la.GenericSparsityPattern_num_nonzeros_off_diagonal(self, *args)
def num_local_nonzeros(self, *args):
"""
Fill vector with number of nonzeros in local_range for
primary dimemsion
"""
return _la.GenericSparsityPattern_num_local_nonzeros(self, *args)
def diagonal_pattern(self, *args):
"""
Return underlying sparsity pattern (diagonal). Options are
'sorted' and 'unsorted'.
"""
return _la.GenericSparsityPattern_diagonal_pattern(self, *args)
def off_diagonal_pattern(self, *args):
"""
Return underlying sparsity pattern (off-diagional). Options are
'sorted' and 'unsorted'.
"""
return _la.GenericSparsityPattern_off_diagonal_pattern(self, *args)
def get_edges(self, *args):
"""
Fill vector with edges for given vertex
"""
return _la.GenericSparsityPattern_get_edges(self, *args)
def apply(self, *args):
"""
Finalize sparsity pattern
"""
return _la.GenericSparsityPattern_apply(self, *args)
GenericSparsityPattern.init = new_instancemethod(_la.GenericSparsityPattern_init,None,GenericSparsityPattern)
GenericSparsityPattern.insert = new_instancemethod(_la.GenericSparsityPattern_insert,None,GenericSparsityPattern)
GenericSparsityPattern.add_edges = new_instancemethod(_la.GenericSparsityPattern_add_edges,None,GenericSparsityPattern)
GenericSparsityPattern.rank = new_instancemethod(_la.GenericSparsityPattern_rank,None,GenericSparsityPattern)
GenericSparsityPattern.primary_dim = new_instancemethod(_la.GenericSparsityPattern_primary_dim,None,GenericSparsityPattern)
GenericSparsityPattern.local_range = new_instancemethod(_la.GenericSparsityPattern_local_range,None,GenericSparsityPattern)
GenericSparsityPattern.num_nonzeros = new_instancemethod(_la.GenericSparsityPattern_num_nonzeros,None,GenericSparsityPattern)
GenericSparsityPattern.num_nonzeros_diagonal = new_instancemethod(_la.GenericSparsityPattern_num_nonzeros_diagonal,None,GenericSparsityPattern)
GenericSparsityPattern.num_nonzeros_off_diagonal = new_instancemethod(_la.GenericSparsityPattern_num_nonzeros_off_diagonal,None,GenericSparsityPattern)
GenericSparsityPattern.num_local_nonzeros = new_instancemethod(_la.GenericSparsityPattern_num_local_nonzeros,None,GenericSparsityPattern)
GenericSparsityPattern.diagonal_pattern = new_instancemethod(_la.GenericSparsityPattern_diagonal_pattern,None,GenericSparsityPattern)
GenericSparsityPattern.off_diagonal_pattern = new_instancemethod(_la.GenericSparsityPattern_off_diagonal_pattern,None,GenericSparsityPattern)
GenericSparsityPattern.get_edges = new_instancemethod(_la.GenericSparsityPattern_get_edges,None,GenericSparsityPattern)
GenericSparsityPattern.apply = new_instancemethod(_la.GenericSparsityPattern_apply,None,GenericSparsityPattern)
GenericSparsityPattern_swigregister = _la.GenericSparsityPattern_swigregister
GenericSparsityPattern_swigregister(GenericSparsityPattern)
class GenericVector(GenericTensor):
"""
This class defines a common interface for vectors.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_GenericVector
def copy(self, *args):
"""
Return copy of vector
"""
return _la.GenericVector_copy(self, *args)
def resize(self, *args):
"""
**Overloaded versions**
* resize\ (rank, dims)
Resize tensor with given dimensions
* resize\ (N)
Resize vector to global size N
* resize\ (range)
Resize vector with given ownership range
* resize\ (range, ghost_indices)
Resize vector with given ownership range and with ghost values
"""
return _la.GenericVector_resize(self, *args)
def empty(self, *args):
"""
Return true if empty
"""
return _la.GenericVector_empty(self, *args)
def size(self, *args):
"""
**Overloaded versions**
* size\ (dim)
Return size of given dimension
* size\ ()
Return global size of vector
"""
return _la.GenericVector_size(self, *args)
def local_size(self, *args):
"""
Return local size of vector
"""
return _la.GenericVector_local_size(self, *args)
def local_range(self, *args):
"""
**Overloaded versions**
* local_range\ (dim)
Return local ownership range
* local_range\ ()
Return local ownership range of a vector
"""
return _la.GenericVector_local_range(self, *args)
def owns_index(self, *args):
"""
Determine whether global vector index is owned by this process
"""
return _la.GenericVector_owns_index(self, *args)
def add(self, *args):
"""
**Overloaded versions**
* add\ (block, num_rows, rows)
Add block of values
* add\ (block, rows)
Add block of values
* add\ (block, rows)
Add block of values
* add\ (block, m, rows)
Add block of values
"""
return _la.GenericVector_add(self, *args)
def get_local(self, *args):
"""
**Overloaded versions**
* get_local\ (block, m, rows)
Get block of values (values must all live on the local process)
* get_local\ (values)
Get all values on local process
"""
return _la.GenericVector_get_local(self, *args)
def set_local(self, *args):
"""
Set all values on local process
"""
return _la.GenericVector_set_local(self, *args)
def add_local(self, *args):
"""
Add values to each entry on local process
"""
return _la.GenericVector_add_local(self, *args)
def gather(self, *args):
"""
**Overloaded versions**
* gather\ (x, indices)
Gather entries into local vector x
* gather\ (x, indices)
Gather entries into x
"""
return _la.GenericVector_gather(self, *args)
def gather_on_zero(self, *args):
"""
Gather all entries into x on process 0
"""
return _la.GenericVector_gather_on_zero(self, *args)
def axpy(self, *args):
"""
Add multiple of given vector (AXPY operation)
"""
return _la.GenericVector_axpy(self, *args)
def abs(self, *args):
"""
Replace all entries in the vector by their absolute values
"""
return _la.GenericVector_abs(self, *args)
def inner(self, *args):
"""
Return inner product with given vector
"""
return _la.GenericVector_inner(self, *args)
def norm(self, *args):
"""
Return norm of vector
"""
return _la.GenericVector_norm(self, *args)
def min(self, *args):
"""
Return minimum value of vector
"""
return _la.GenericVector_min(self, *args)
def max(self, *args):
"""
Return maximum value of vector
"""
return _la.GenericVector_max(self, *args)
def sum(self, *args):
"""
**Overloaded versions**
* sum\ ()
Return sum of vector
* sum\ (rows)
Return sum of selected rows in vector. Repeated entries are
only summed once.
"""
return _la.GenericVector_sum(self, *args)
def _assign(self, *args):
"""
**Overloaded versions**
* operator=\ (x)
Assignment operator
* operator=\ (a)
Assignment operator
"""
return _la.GenericVector__assign(self, *args)
def update_ghost_values(self, *args):
"""
Update ghost values
"""
return _la.GenericVector_update_ghost_values(self, *args)
def _scale(self, *args):
"""Missing docstring"""
return _la.GenericVector__scale(self, *args)
def _vec_mul(self, *args):
"""Missing docstring"""
return _la.GenericVector__vec_mul(self, *args)
def __in_parallel(self):
first, last = self.local_range()
return first > 0 or len(self) > last
def __is_compatible(self, other):
"Returns True if self, and other are compatible Vectors"
if not isinstance(other, GenericVector):
return False
self_type = get_tensor_type(self)
return self_type == get_tensor_type(other)
def array(self):
"Return a numpy array representation of the local part of a Vector"
#from numpy import zeros, arange, uint0
#v = zeros(self.local_size())
#self.get_local(v)
#return v
return self.get_local()
def __contains__(self, value):
from numpy import isscalar
if not isscalar(value):
raise TypeError("expected scalar")
return _contains(self,value)
def __gt__(self, value):
from numpy import isscalar
if isscalar(value):
return _compare_vector_with_value(self, value, dolfin_gt)
if isinstance(value, GenericVector):
return _compare_vector_with_vector(self, value, dolfin_gt)
return NotImplemented
def __ge__(self,value):
from numpy import isscalar
if isscalar(value):
return _compare_vector_with_value(self, value, dolfin_ge)
if isinstance(value, GenericVector):
return _compare_vector_with_vector(self, value, dolfin_ge)
return NotImplemented
def __lt__(self,value):
from numpy import isscalar
if isscalar(value):
return _compare_vector_with_value(self, value, dolfin_lt)
if isinstance(value, GenericVector):
return _compare_vector_with_vector(self, value, dolfin_lt)
return NotImplemented
def __le__(self,value):
from numpy import isscalar
if isscalar(value):
return _compare_vector_with_value(self, value, dolfin_le)
if isinstance(value, GenericVector):
return _compare_vector_with_vector(self, value, dolfin_le)
return NotImplemented
def __eq__(self,value):
from numpy import isscalar
if isscalar(value):
return _compare_vector_with_value(self, value, dolfin_eq)
if isinstance(value, GenericVector):
return _compare_vector_with_vector(self, value, dolfin_eq)
return NotImplemented
def __neq__(self,value):
from numpy import isscalar
if isscalar(value):
return _compare_vector_with_value(self, value, dolfin_neq)
if isinstance(value, GenericVector):
return _compare_vector_with_vector(self, value, dolfin_neq)
return NotImplemented
def __neg__(self):
ret = self.copy()
ret *= -1
return ret
def __delitem__(self,i):
raise ValueError("cannot delete Vector elements")
def __delslice__(self,i,j):
raise ValueError("cannot delete Vector elements")
def __setslice__(self, i, j, values):
if i == 0 and (j >= len(self) or j == -1): # slice == whole
from numpy import isscalar
# No test for equal lengths because this is checked by DOLFIN in _assign
if isinstance(values, GenericVector) or isscalar(values):
self._assign(values)
return
self.__setitem__(slice(i, j, 1), values)
def __getslice__(self, i, j):
if i == 0 and (j >= len(self) or j == -1):
return self.copy()
return self.__getitem__(slice(i, j, 1))
def __getitem__(self, indices):
from numpy import ndarray, integer
from types import SliceType
if isinstance(indices, (int, integer, long)):
return _get_vector_single_item(self, indices)
elif isinstance(indices, (SliceType, ndarray, list) ):
return as_backend_type(_get_vector_sub_vector(self, indices))
else:
raise TypeError("expected an int, slice, list or numpy array of integers")
def __setitem__(self, indices, values):
from numpy import ndarray, integer, isscalar
from types import SliceType
if isinstance(indices, (int, integer, long)):
if isscalar(values):
return _set_vector_items_value(self, indices, values)
else:
raise TypeError("provide a scalar to set single item")
elif isinstance(indices, (SliceType, ndarray, list)):
if isscalar(values):
_set_vector_items_value(self, indices, values)
elif isinstance(values, GenericVector):
_set_vector_items_vector(self, indices, values)
elif isinstance(values, ndarray):
_set_vector_items_array_of_float(self, indices, values)
else:
raise TypeError("provide a scalar, GenericVector or numpy array of float to set items in Vector")
else:
raise TypeError("index must be an int, slice or a list or numpy array of integers")
def __len__(self):
return self.size()
def __iter__(self):
for i in xrange(self.size()):
yield _get_vector_single_item(self, i)
def __add__(self, other):
"""x.__add__(y) <==> x+y"""
from numpy import isscalar
if isscalar(other):
ret = self.copy()
ret._iadd_scalar(other)
return ret
elif self.__is_compatible(other):
ret = self.copy()
ret.axpy(1.0, other)
return ret
return NotImplemented
def __sub__(self, other):
"""x.__sub__(y) <==> x-y"""
from numpy import isscalar
if isscalar(other):
ret = self.copy()
ret._iadd_scalar(-other)
return ret
elif self.__is_compatible(other):
ret = self.copy()
ret.axpy(-1.0, other)
return ret
return NotImplemented
def __mul__(self, other):
"""x.__mul__(y) <==> x*y"""
from numpy import isscalar
if isscalar(other):
ret = self.copy()
ret._scale(other)
return ret
if isinstance(other,GenericVector):
ret = self.copy()
ret._vec_mul(other)
return ret
return NotImplemented
def __div__(self,other):
"""x.__div__(y) <==> x/y"""
from numpy import isscalar
if isscalar(other):
ret = self.copy()
ret._scale(1.0 / other)
return ret
return NotImplemented
def __radd__(self,other):
"""x.__radd__(y) <==> y+x"""
return self.__add__(other)
def __rsub__(self, other):
"""x.__rsub__(y) <==> y-x"""
return self.__sub__(other)
def __rmul__(self, other):
"""x.__rmul__(y) <==> y*x"""
from numpy import isscalar
if isscalar(other):
ret = self.copy()
ret._scale(other)
return ret
return NotImplemented
def __rdiv__(self, other):
"""x.__rdiv__(y) <==> y/x"""
return NotImplemented
def __iadd__(self, other):
"""x.__iadd__(y) <==> x+y"""
from numpy import isscalar
if isscalar(other):
self._iadd_scalar(other)
return self
elif self.__is_compatible(other):
self.axpy(1.0, other)
return self
return NotImplemented
def __isub__(self, other):
"""x.__isub__(y) <==> x-y"""
from numpy import isscalar
if isscalar(other):
self._iadd_scalar(-other)
return self
elif self.__is_compatible(other):
self.axpy(-1.0, other)
return self
return NotImplemented
def __imul__(self, other):
"""x.__imul__(y) <==> x*y"""
from numpy import isscalar
if isscalar(other):
self._scale(other)
return self
if isinstance(other, GenericVector):
self._vec_mul(other)
return self
return NotImplemented
def __idiv__(self, other):
"""x.__idiv__(y) <==> x/y"""
from numpy import isscalar
if isscalar(other):
self._scale(1.0 / other)
return self
return NotImplemented
GenericVector.copy = new_instancemethod(_la.GenericVector_copy,None,GenericVector)
GenericVector.resize = new_instancemethod(_la.GenericVector_resize,None,GenericVector)
GenericVector.empty = new_instancemethod(_la.GenericVector_empty,None,GenericVector)
GenericVector.size = new_instancemethod(_la.GenericVector_size,None,GenericVector)
GenericVector.local_size = new_instancemethod(_la.GenericVector_local_size,None,GenericVector)
GenericVector.local_range = new_instancemethod(_la.GenericVector_local_range,None,GenericVector)
GenericVector.owns_index = new_instancemethod(_la.GenericVector_owns_index,None,GenericVector)
GenericVector.add = new_instancemethod(_la.GenericVector_add,None,GenericVector)
GenericVector.get_local = new_instancemethod(_la.GenericVector_get_local,None,GenericVector)
GenericVector.set_local = new_instancemethod(_la.GenericVector_set_local,None,GenericVector)
GenericVector.add_local = new_instancemethod(_la.GenericVector_add_local,None,GenericVector)
GenericVector.gather = new_instancemethod(_la.GenericVector_gather,None,GenericVector)
GenericVector.gather_on_zero = new_instancemethod(_la.GenericVector_gather_on_zero,None,GenericVector)
GenericVector.axpy = new_instancemethod(_la.GenericVector_axpy,None,GenericVector)
GenericVector.abs = new_instancemethod(_la.GenericVector_abs,None,GenericVector)
GenericVector.inner = new_instancemethod(_la.GenericVector_inner,None,GenericVector)
GenericVector.norm = new_instancemethod(_la.GenericVector_norm,None,GenericVector)
GenericVector.min = new_instancemethod(_la.GenericVector_min,None,GenericVector)
GenericVector.max = new_instancemethod(_la.GenericVector_max,None,GenericVector)
GenericVector.sum = new_instancemethod(_la.GenericVector_sum,None,GenericVector)
GenericVector._assign = new_instancemethod(_la.GenericVector__assign,None,GenericVector)
GenericVector.update_ghost_values = new_instancemethod(_la.GenericVector_update_ghost_values,None,GenericVector)
GenericVector._iadd_scalar = new_instancemethod(_la.GenericVector__iadd_scalar,None,GenericVector)
GenericVector._scale = new_instancemethod(_la.GenericVector__scale,None,GenericVector)
GenericVector._vec_mul = new_instancemethod(_la.GenericVector__vec_mul,None,GenericVector)
GenericVector_swigregister = _la.GenericVector_swigregister
GenericVector_swigregister(GenericVector)
class VectorSpaceBasis(object):
"""
This class defines a basis for vector spaces,
typically used for expressing nullspaces, transpose nullspaces
and near nullspaces of singular operators
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
_la.VectorSpaceBasis_swiginit(self,_la.new_VectorSpaceBasis(*args))
__swig_destroy__ = _la.delete_VectorSpaceBasis
def is_orthonormal(self, *args):
"""
Test if basis is orthonormal
"""
return _la.VectorSpaceBasis_is_orthonormal(self, *args)
def is_orthogonal(self, *args):
"""
Test if basis is orthogonal
"""
return _la.VectorSpaceBasis_is_orthogonal(self, *args)
def orthogonalize(self, *args):
"""
Orthogonalize x with respect to basis
"""
return _la.VectorSpaceBasis_orthogonalize(self, *args)
def dim(self, *args):
"""
Dimension of the basis
"""
return _la.VectorSpaceBasis_dim(self, *args)
def _sub(self, *args):
"""
Get a particular basis vector
"""
return _la.VectorSpaceBasis__sub(self, *args)
VectorSpaceBasis.is_orthonormal = new_instancemethod(_la.VectorSpaceBasis_is_orthonormal,None,VectorSpaceBasis)
VectorSpaceBasis.is_orthogonal = new_instancemethod(_la.VectorSpaceBasis_is_orthogonal,None,VectorSpaceBasis)
VectorSpaceBasis.orthogonalize = new_instancemethod(_la.VectorSpaceBasis_orthogonalize,None,VectorSpaceBasis)
VectorSpaceBasis.dim = new_instancemethod(_la.VectorSpaceBasis_dim,None,VectorSpaceBasis)
VectorSpaceBasis._sub = new_instancemethod(_la.VectorSpaceBasis__sub,None,VectorSpaceBasis)
VectorSpaceBasis_swigregister = _la.VectorSpaceBasis_swigregister
VectorSpaceBasis_swigregister(VectorSpaceBasis)
class GenericLinearSolver(common.Variable):
"""
This class provides a general solver for linear systems Ax = b.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def set_operator(self, *args):
"""
Set operator (matrix)
"""
return _la.GenericLinearSolver_set_operator(self, *args)
def set_operators(self, *args):
"""
Set operator (matrix) and preconditioner matrix
"""
return _la.GenericLinearSolver_set_operators(self, *args)
def set_nullspace(self, *args):
"""
Set null space of the operator (matrix). This is used to solve
singular systems
"""
return _la.GenericLinearSolver_set_nullspace(self, *args)
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (A, x, b)
Solve linear system Ax = b
* solve\ (x, b)
Solve linear system Ax = b
"""
return _la.GenericLinearSolver_solve(self, *args)
def solve_transpose(self, *args):
"""
**Overloaded versions**
* solve_transpose\ (A, x, b)
Solve linear system A^Tx = b
* solve_transpose\ (x, b)
Solve linear system A^Tx = b
"""
return _la.GenericLinearSolver_solve_transpose(self, *args)
def parameter_type(self, *args):
"""
Return parameter type: "krylov_solver" or "lu_solver"
"""
return _la.GenericLinearSolver_parameter_type(self, *args)
def update_parameters(self, *args):
"""
Update solver parameters (useful for LinearSolver wrapper)
"""
return _la.GenericLinearSolver_update_parameters(self, *args)
__swig_destroy__ = _la.delete_GenericLinearSolver
GenericLinearSolver.set_operator = new_instancemethod(_la.GenericLinearSolver_set_operator,None,GenericLinearSolver)
GenericLinearSolver.set_operators = new_instancemethod(_la.GenericLinearSolver_set_operators,None,GenericLinearSolver)
GenericLinearSolver.set_nullspace = new_instancemethod(_la.GenericLinearSolver_set_nullspace,None,GenericLinearSolver)
GenericLinearSolver.solve = new_instancemethod(_la.GenericLinearSolver_solve,None,GenericLinearSolver)
GenericLinearSolver.solve_transpose = new_instancemethod(_la.GenericLinearSolver_solve_transpose,None,GenericLinearSolver)
GenericLinearSolver.parameter_type = new_instancemethod(_la.GenericLinearSolver_parameter_type,None,GenericLinearSolver)
GenericLinearSolver.update_parameters = new_instancemethod(_la.GenericLinearSolver_update_parameters,None,GenericLinearSolver)
GenericLinearSolver_swigregister = _la.GenericLinearSolver_swigregister
GenericLinearSolver_swigregister(GenericLinearSolver)
class GenericLUSolver(GenericLinearSolver):
"""
This a base class for LU solvers
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (x, b)
Solve linear system Ax = b
* solve\ (A, x, b)
Solve linear system Ax = b
"""
return _la.GenericLUSolver_solve(self, *args)
def solve_transpose(self, *args):
"""
**Overloaded versions**
* solve_transpose\ (x, b)
Solve linear system A^Tx = b
* solve_transpose\ (A, x, b)
Solve linear system A^Tx = b
"""
return _la.GenericLUSolver_solve_transpose(self, *args)
__swig_destroy__ = _la.delete_GenericLUSolver
GenericLUSolver.solve = new_instancemethod(_la.GenericLUSolver_solve,None,GenericLUSolver)
GenericLUSolver.solve_transpose = new_instancemethod(_la.GenericLUSolver_solve_transpose,None,GenericLUSolver)
GenericLUSolver_swigregister = _la.GenericLUSolver_swigregister
GenericLUSolver_swigregister(GenericLUSolver)
class GenericPreconditioner(object):
"""
This class provides a common base preconditioners.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_GenericPreconditioner
def set_nullspace(self, *args):
"""
Set the (approximate) null space of the preconditioner operator
(matrix). This is required for certain preconditioner types,
e.g. smoothed aggregation multigrid
"""
return _la.GenericPreconditioner_set_nullspace(self, *args)
def set_coordinates(self, *args):
"""
Set the coordinates of the operator (matrix) rows and geometric
dimension d. This is can be used by required for certain
preconditioners, e.g. ML. The input for this function can be
generated using GenericDofMap::tabulate_all_dofs.
"""
return _la.GenericPreconditioner_set_coordinates(self, *args)
def __init__(self, *args):
_la.GenericPreconditioner_swiginit(self,_la.new_GenericPreconditioner(*args))
GenericPreconditioner.set_nullspace = new_instancemethod(_la.GenericPreconditioner_set_nullspace,None,GenericPreconditioner)
GenericPreconditioner.set_coordinates = new_instancemethod(_la.GenericPreconditioner_set_coordinates,None,GenericPreconditioner)
GenericPreconditioner_swigregister = _la.GenericPreconditioner_swigregister
GenericPreconditioner_swigregister(GenericPreconditioner)
class PETScOptions(object):
"""
These class provides static functions that permit users to set
and retreive PETSc options via the PETSc option/parameter
system. The option should be prefixed by '-', e.g.
PETScOptions::set("mat_mumps_icntl_14", 40.0);
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def set(*args):
"""
**Overloaded versions**
* set\ (option)
Set PETSc option that takes no value
* set\ (option, value)
Set PETSc boolean option
* set\ (option, value)
Set PETSc integer option
* set\ (option, value)
Set PETSc double option
* set\ (option, value)
Set PETSc string option
* set\ (option, value)
Genetic function for setting PETSc option
"""
return _la.PETScOptions_set(*args)
set = staticmethod(set)
def clear(*args):
"""
Clear PETSc option
"""
return _la.PETScOptions_clear(*args)
clear = staticmethod(clear)
def __init__(self, *args):
_la.PETScOptions_swiginit(self,_la.new_PETScOptions(*args))
__swig_destroy__ = _la.delete_PETScOptions
PETScOptions_swigregister = _la.PETScOptions_swigregister
PETScOptions_swigregister(PETScOptions)
def PETScOptions_set(*args):
"""
**Overloaded versions**
* set\ (option)
Set PETSc option that takes no value
* set\ (option, value)
Set PETSc boolean option
* set\ (option, value)
Set PETSc integer option
* set\ (option, value)
Set PETSc double option
* set\ (option, value)
Set PETSc string option
* set\ (option, value)
Genetic function for setting PETSc option
"""
return _la.PETScOptions_set(*args)
def PETScOptions_clear(*args):
"""
Clear PETSc option
"""
return _la.PETScOptions_clear(*args)
class PETScObject(object):
"""
This class calls SubSystemsManager to initialise PETSc.
All PETSc objects must be derived from this class.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor. Ensures that PETSc has been initialised.
"""
_la.PETScObject_swiginit(self,_la.new_PETScObject(*args))
__swig_destroy__ = _la.delete_PETScObject
def petsc_error(*args):
"""
Print error message for PETSc calls that return an error
"""
return _la.PETScObject_petsc_error(*args)
petsc_error = staticmethod(petsc_error)
PETScObject_swigregister = _la.PETScObject_swigregister
PETScObject_swigregister(PETScObject)
def PETScObject_petsc_error(*args):
"""
Print error message for PETSc calls that return an error
"""
return _la.PETScObject_petsc_error(*args)
class PETScMatrixDeleter(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
_la.PETScMatrixDeleter_swiginit(self,_la.new_PETScMatrixDeleter(*args))
__swig_destroy__ = _la.delete_PETScMatrixDeleter
PETScMatrixDeleter.__call__ = new_instancemethod(_la.PETScMatrixDeleter___call__,None,PETScMatrixDeleter)
PETScMatrixDeleter_swigregister = _la.PETScMatrixDeleter_swigregister
PETScMatrixDeleter_swigregister(PETScMatrixDeleter)
class PETScBaseMatrix(PETScObject,common.Variable):
"""
This class is a base class for matrices that can be used in
PETScKrylovSolver.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def size(self, *args):
"""
Return number of rows (dim = 0) or columns (dim = 1)
"""
return _la.PETScBaseMatrix_size(self, *args)
def local_range(self, *args):
"""
Return local range along dimension dim
"""
return _la.PETScBaseMatrix_local_range(self, *args)
def resize(self, *args):
"""
Resize matrix to be compatible with the matrix-vector product
y = Ax. In the parallel case, both size and layout are
important.
*Arguments*
dim (int)
The dimension (axis): dim = 0 --> z = y, dim = 1 --> z = x
"""
return _la.PETScBaseMatrix_resize(self, *args)
def mat(self):
common.dolfin_error("dolfin/swig/la/post.i",
"access PETScMatrix objects in python",
"dolfin must be configured with petsc4py enabled")
return None
__swig_destroy__ = _la.delete_PETScBaseMatrix
PETScBaseMatrix.size = new_instancemethod(_la.PETScBaseMatrix_size,None,PETScBaseMatrix)
PETScBaseMatrix.local_range = new_instancemethod(_la.PETScBaseMatrix_local_range,None,PETScBaseMatrix)
PETScBaseMatrix.resize = new_instancemethod(_la.PETScBaseMatrix_resize,None,PETScBaseMatrix)
PETScBaseMatrix_swigregister = _la.PETScBaseMatrix_swigregister
PETScBaseMatrix_swigregister(PETScBaseMatrix)
class uBLASLinearOperator(GenericLinearOperator):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
if self.__class__ == uBLASLinearOperator:
_self = None
else:
_self = self
_la.uBLASLinearOperator_swiginit(self,_la.new_uBLASLinearOperator(_self, *args))
def size(self, *args):
"""
Return size of given dimension
"""
return _la.uBLASLinearOperator_size(self, *args)
def mult(self, *args):
"""
Compute matrix-vector product y = Ax
"""
return _la.uBLASLinearOperator_mult(self, *args)
__swig_destroy__ = _la.delete_uBLASLinearOperator
def __disown__(self):
self.this.disown()
_la.disown_uBLASLinearOperator(self)
return weakref_proxy(self)
uBLASLinearOperator.size = new_instancemethod(_la.uBLASLinearOperator_size,None,uBLASLinearOperator)
uBLASLinearOperator.mult = new_instancemethod(_la.uBLASLinearOperator_mult,None,uBLASLinearOperator)
uBLASLinearOperator.init_layout = new_instancemethod(_la.uBLASLinearOperator_init_layout,None,uBLASLinearOperator)
uBLASLinearOperator_swigregister = _la.uBLASLinearOperator_swigregister
uBLASLinearOperator_swigregister(uBLASLinearOperator)
class PETScMatrix(GenericMatrix,PETScBaseMatrix):
"""
This class provides a simple matrix class based on PETSc.
It is a wrapper for a PETSc matrix pointer (Mat)
implementing the GenericMatrix interface.
The interface is intentionally simple. For advanced usage,
access the PETSc Mat pointer using the function mat() and
use the standard PETSc interface.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* PETScMatrix\ (use_gpu=false)
Create empty matrix
* PETScMatrix\ (A, use_gpu=false)
Create matrix from given PETSc Mat pointer
* PETScMatrix\ (A)
Copy constructor
"""
_la.PETScMatrix_swiginit(self,_la.new_PETScMatrix(*args))
__swig_destroy__ = _la.delete_PETScMatrix
def zero(self, *args):
"""
**Overloaded versions**
* zero\ ()
Set all entries to zero and keep any sparse structure
* zero\ (m, rows)
Set given rows to zero
"""
return _la.PETScMatrix_zero(self, *args)
def assign(self, *args):
"""
**Overloaded versions**
* operator=\ (A)
Assignment operator
* operator=\ (A)
Assignment operator
"""
return _la.PETScMatrix_assign(self, *args)
def binary_dump(self, *args):
"""
Dump matrix to PETSc binary format
"""
return _la.PETScMatrix_binary_dump(self, *args)
PETScMatrix.zero = new_instancemethod(_la.PETScMatrix_zero,None,PETScMatrix)
PETScMatrix.assign = new_instancemethod(_la.PETScMatrix_assign,None,PETScMatrix)
PETScMatrix.binary_dump = new_instancemethod(_la.PETScMatrix_binary_dump,None,PETScMatrix)
PETScMatrix_swigregister = _la.PETScMatrix_swigregister
PETScMatrix_swigregister(PETScMatrix)
class PETScLinearOperator(PETScBaseMatrix,GenericLinearOperator):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
if self.__class__ == PETScLinearOperator:
_self = None
else:
_self = self
_la.PETScLinearOperator_swiginit(self,_la.new_PETScLinearOperator(_self, *args))
def size(self, *args):
"""
Return size of given dimension
"""
return _la.PETScLinearOperator_size(self, *args)
def mult(self, *args):
"""
Compute matrix-vector product y = Ax
"""
return _la.PETScLinearOperator_mult(self, *args)
__swig_destroy__ = _la.delete_PETScLinearOperator
def __disown__(self):
self.this.disown()
_la.disown_PETScLinearOperator(self)
return weakref_proxy(self)
PETScLinearOperator.size = new_instancemethod(_la.PETScLinearOperator_size,None,PETScLinearOperator)
PETScLinearOperator.mult = new_instancemethod(_la.PETScLinearOperator_mult,None,PETScLinearOperator)
PETScLinearOperator.init_layout = new_instancemethod(_la.PETScLinearOperator_init_layout,None,PETScLinearOperator)
PETScLinearOperator_swigregister = _la.PETScLinearOperator_swigregister
PETScLinearOperator_swigregister(PETScLinearOperator)
class PETScPreconditioner(PETScObject,GenericPreconditioner,common.Variable):
"""
This class is a wrapper for configuring PETSc
preconditioners. It does not own a preconditioner. It can take a
PETScKrylovSolver and set the preconditioner type and
parameters.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Create a particular preconditioner object
"""
_la.PETScPreconditioner_swiginit(self,_la.new_PETScPreconditioner(*args))
__swig_destroy__ = _la.delete_PETScPreconditioner
def set(self, *args):
"""
Set the precondtioner type and parameters
"""
return _la.PETScPreconditioner_set(self, *args)
def near_nullspace(self, *args):
"""
Return the PETSc null space
"""
return _la.PETScPreconditioner_near_nullspace(self, *args)
def preconditioners(*args):
"""
Rerturn a list of available preconditioners
"""
return _la.PETScPreconditioner_preconditioners(*args)
preconditioners = staticmethod(preconditioners)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScPreconditioner_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
PETScPreconditioner.set = new_instancemethod(_la.PETScPreconditioner_set,None,PETScPreconditioner)
PETScPreconditioner.near_nullspace = new_instancemethod(_la.PETScPreconditioner_near_nullspace,None,PETScPreconditioner)
PETScPreconditioner.set_fieldsplit = new_instancemethod(_la.PETScPreconditioner_set_fieldsplit,None,PETScPreconditioner)
PETScPreconditioner_swigregister = _la.PETScPreconditioner_swigregister
PETScPreconditioner_swigregister(PETScPreconditioner)
def PETScPreconditioner_preconditioners(*args):
"""
Rerturn a list of available preconditioners
"""
return _la.PETScPreconditioner_preconditioners(*args)
def PETScPreconditioner_default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScPreconditioner_default_parameters(*args)
class PETScKrylovSolver(GenericLinearSolver,PETScObject):
"""
This class implements Krylov methods for linear systems
of the form Ax = b. It is a wrapper for the Krylov solvers
of PETSc.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* PETScKrylovSolver\ ("default", "default")
Create Krylov solver for a particular method and names
preconditioner
* PETScKrylovSolver\ (method, preconditioner)
Create Krylov solver for a particular method and
PETScPreconditioner
* PETScKrylovSolver\ (method, preconditioner)
Create Krylov solver for a particular method and
PETScPreconditioner (shared_ptr version)
* PETScKrylovSolver\ (method, preconditioner)
Create Krylov solver for a particular method and
PETScPreconditioner
* PETScKrylovSolver\ (method, preconditioner)
Create Krylov solver for a particular method and
PETScPreconditioner (shared_ptr version)
* PETScKrylovSolver\ (ksp)
Create solver from given PETSc KSP pointer
"""
_la.PETScKrylovSolver_swiginit(self,_la.new_PETScKrylovSolver(*args))
__swig_destroy__ = _la.delete_PETScKrylovSolver
def set_operator(self, *args):
"""
**Overloaded versions**
* set_operator\ (A)
Set operator (matrix)
* set_operator\ (A)
Set operator (matrix)
"""
return _la.PETScKrylovSolver_set_operator(self, *args)
def set_operators(self, *args):
"""
**Overloaded versions**
* set_operators\ (A, P)
Set operator (matrix) and preconditioner matrix
* set_operators\ (A, P)
Set operator (matrix) and preconditioner matrix
"""
return _la.PETScKrylovSolver_set_operators(self, *args)
def get_operator(self, *args):
"""
Get operator (matrix)
"""
return _la.PETScKrylovSolver_get_operator(self, *args)
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (x, b)
Solve linear system Ax = b and return number of iterations
* solve\ (x, b)
Solve linear system Ax = b and return number of iterations
* solve\ (A, x, b)
Solve linear system Ax = b and return number of iterations
* solve\ (A, x, b)
Solve linear system Ax = b and return number of iterations
"""
return _la.PETScKrylovSolver_solve(self, *args)
def methods(*args):
"""
Return a list of available solver methods
"""
return _la.PETScKrylovSolver_methods(*args)
methods = staticmethod(methods)
def preconditioners(*args):
"""
Return a list of available preconditioners
"""
return _la.PETScKrylovSolver_preconditioners(*args)
preconditioners = staticmethod(preconditioners)
def set_options_prefix(self, *args):
"""
Set options prefix
"""
return _la.PETScKrylovSolver_set_options_prefix(self, *args)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScKrylovSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
def ksp(self):
common.dolfin_error("dolfin/swig/la/post.i",
"access PETScKrylovSolver objects in python",
"dolfin must be configured with petsc4py enabled")
return None
PETScKrylovSolver.set_operator = new_instancemethod(_la.PETScKrylovSolver_set_operator,None,PETScKrylovSolver)
PETScKrylovSolver.set_operators = new_instancemethod(_la.PETScKrylovSolver_set_operators,None,PETScKrylovSolver)
PETScKrylovSolver.get_operator = new_instancemethod(_la.PETScKrylovSolver_get_operator,None,PETScKrylovSolver)
PETScKrylovSolver.solve = new_instancemethod(_la.PETScKrylovSolver_solve,None,PETScKrylovSolver)
PETScKrylovSolver.set_options_prefix = new_instancemethod(_la.PETScKrylovSolver_set_options_prefix,None,PETScKrylovSolver)
PETScKrylovSolver_swigregister = _la.PETScKrylovSolver_swigregister
PETScKrylovSolver_swigregister(PETScKrylovSolver)
def PETScKrylovSolver_methods(*args):
"""
Return a list of available solver methods
"""
return _la.PETScKrylovSolver_methods(*args)
def PETScKrylovSolver_preconditioners(*args):
"""
Return a list of available preconditioners
"""
return _la.PETScKrylovSolver_preconditioners(*args)
def PETScKrylovSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScKrylovSolver_default_parameters(*args)
class PETScLUSolver(GenericLUSolver,PETScObject):
"""
This class implements the direct solution (LU factorization) for
linear systems of the form Ax = b. It is a wrapper for the LU
solver of PETSc.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* PETScLUSolver\ (method="default")
Constructor
* PETScLUSolver\ (A, method="default")
Constructor
"""
_la.PETScLUSolver_swiginit(self,_la.new_PETScLUSolver(*args))
__swig_destroy__ = _la.delete_PETScLUSolver
def set_operator(self, *args):
"""
**Overloaded versions**
* set_operator\ (A)
Set operator (matrix)
* set_operator\ (A)
Set operator (matrix)
"""
return _la.PETScLUSolver_set_operator(self, *args)
def get_operator(self, *args):
"""
Get operator (matrix)
"""
return _la.PETScLUSolver_get_operator(self, *args)
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (x, b)
Solve linear system Ax = b
* solve\ (x, b, transpose)
Solve linear system Ax = b
* solve\ (A, x, b)
Solve linear system Ax = b
* solve\ (A, x, b)
Solve linear system Ax = b
"""
return _la.PETScLUSolver_solve(self, *args)
def solve_transpose(self, *args):
"""
**Overloaded versions**
* solve_transpose\ (x, b)
Solve linear system A^Tx = b
* solve_transpose\ (A, x, b)
Solve linear system A^Tx = b
* solve_transpose\ (A, x, b)
Solve linear system A^Tx = b
"""
return _la.PETScLUSolver_solve_transpose(self, *args)
def methods(*args):
"""
Return a list of available solver methods
"""
return _la.PETScLUSolver_methods(*args)
methods = staticmethod(methods)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScLUSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
def ksp(self):
common.dolfin_error("dolfin/swig/la/post.i",
"access PETScLUSolver objects in python",
"dolfin must be configured with petsc4py enabled")
return None
PETScLUSolver.set_operator = new_instancemethod(_la.PETScLUSolver_set_operator,None,PETScLUSolver)
PETScLUSolver.get_operator = new_instancemethod(_la.PETScLUSolver_get_operator,None,PETScLUSolver)
PETScLUSolver.solve = new_instancemethod(_la.PETScLUSolver_solve,None,PETScLUSolver)
PETScLUSolver.solve_transpose = new_instancemethod(_la.PETScLUSolver_solve_transpose,None,PETScLUSolver)
PETScLUSolver_swigregister = _la.PETScLUSolver_swigregister
PETScLUSolver_swigregister(PETScLUSolver)
def PETScLUSolver_methods(*args):
"""
Return a list of available solver methods
"""
return _la.PETScLUSolver_methods(*args)
def PETScLUSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScLUSolver_default_parameters(*args)
class CholmodCholeskySolver(GenericLinearSolver):
"""
This class implements the direct solution (Cholesky
factorization) of linear systems of the form Ax = b. Sparse
matrices are solved using CHOLMOD
http://www.cise.ufl.edu/research/sparse/cholmod/ if installed.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* CholmodCholeskySolver\ ()
Constructor
* CholmodCholeskySolver\ (A)
Constructor
"""
_la.CholmodCholeskySolver_swiginit(self,_la.new_CholmodCholeskySolver(*args))
__swig_destroy__ = _la.delete_CholmodCholeskySolver
def factorize(self, *args):
"""
Cholesky-factor sparse matrix A if CHOLMOD is installed
"""
return _la.CholmodCholeskySolver_factorize(self, *args)
def factorized_solve(self, *args):
"""
Solve factorized system (CHOLMOD).
"""
return _la.CholmodCholeskySolver_factorized_solve(self, *args)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.CholmodCholeskySolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
CholmodCholeskySolver.factorize = new_instancemethod(_la.CholmodCholeskySolver_factorize,None,CholmodCholeskySolver)
CholmodCholeskySolver.factorized_solve = new_instancemethod(_la.CholmodCholeskySolver_factorized_solve,None,CholmodCholeskySolver)
CholmodCholeskySolver_swigregister = _la.CholmodCholeskySolver_swigregister
CholmodCholeskySolver_swigregister(CholmodCholeskySolver)
def CholmodCholeskySolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.CholmodCholeskySolver_default_parameters(*args)
class UmfpackLUSolver(GenericLUSolver):
"""
This class implements the direct solution (LU factorization) of
linear systems of the form Ax = b using UMFPACK
(http://www.cise.ufl.edu/research/sparse/umfpack/) if installed.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* UmfpackLUSolver\ ()
Constructor
* UmfpackLUSolver\ (A)
Constructor
"""
_la.UmfpackLUSolver_swiginit(self,_la.new_UmfpackLUSolver(*args))
__swig_destroy__ = _la.delete_UmfpackLUSolver
def get_operator(self, *args):
"""
Return the operator (matrix)
"""
return _la.UmfpackLUSolver_get_operator(self, *args)
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (x, b)
Solve linear system Ax = b for a sparse matrix using UMFPACK
if installed
* solve\ (A, x, b)
Solve linear system
"""
return _la.UmfpackLUSolver_solve(self, *args)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.UmfpackLUSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
UmfpackLUSolver.get_operator = new_instancemethod(_la.UmfpackLUSolver_get_operator,None,UmfpackLUSolver)
UmfpackLUSolver.solve = new_instancemethod(_la.UmfpackLUSolver_solve,None,UmfpackLUSolver)
UmfpackLUSolver_swigregister = _la.UmfpackLUSolver_swigregister
UmfpackLUSolver_swigregister(UmfpackLUSolver)
def UmfpackLUSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.UmfpackLUSolver_default_parameters(*args)
class STLMatrix(GenericMatrix):
"""
Simple STL-based implementation of the GenericMatrix interface.
The sparse matrix is stored as a pair of std::vector of
std::vector, one for the columns and one for the values.
Historically, this class has undergone a number of different
incarnations, based on various combinations of std::vector,
std::set and std::map. The current implementation has proven to
be the fastest.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Create empty matrix
"""
_la.STLMatrix_swiginit(self,_la.new_STLMatrix(*args))
__swig_destroy__ = _la.delete_STLMatrix
def zero(self, *args):
"""
**Overloaded versions**
* zero\ ()
Set all entries to zero and keep any sparse structure
* zero\ (m, rows)
Set given rows to zero
"""
return _la.STLMatrix_zero(self, *args)
def block_size(self, *args):
"""
--- STLMatrix interface ---
Return matrix block size
"""
return _la.STLMatrix_block_size(self, *args)
def clear(self, *args):
"""
Clear matrix. Destroys data and sparse layout
"""
return _la.STLMatrix_clear(self, *args)
def nnz(self, *args):
"""
Return number of global non-zero entries
"""
return _la.STLMatrix_nnz(self, *args)
def local_nnz(self, *args):
"""
Return number of local non-zero entries
"""
return _la.STLMatrix_local_nnz(self, *args)
STLMatrix.zero = new_instancemethod(_la.STLMatrix_zero,None,STLMatrix)
STLMatrix.block_size = new_instancemethod(_la.STLMatrix_block_size,None,STLMatrix)
STLMatrix.clear = new_instancemethod(_la.STLMatrix_clear,None,STLMatrix)
STLMatrix.sort = new_instancemethod(_la.STLMatrix_sort,None,STLMatrix)
STLMatrix.nnz = new_instancemethod(_la.STLMatrix_nnz,None,STLMatrix)
STLMatrix.local_nnz = new_instancemethod(_la.STLMatrix_local_nnz,None,STLMatrix)
STLMatrix_swigregister = _la.STLMatrix_swigregister
STLMatrix_swigregister(STLMatrix)
class CoordinateMatrix(object):
"""
Coordinate sparse matrix.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
_la.CoordinateMatrix_swiginit(self,_la.new_CoordinateMatrix(*args))
__swig_destroy__ = _la.delete_CoordinateMatrix
def norm(self, *args):
"""
Return norm of matrix
"""
return _la.CoordinateMatrix_norm(self, *args)
CoordinateMatrix.size = new_instancemethod(_la.CoordinateMatrix_size,None,CoordinateMatrix)
CoordinateMatrix.rows = new_instancemethod(_la.CoordinateMatrix_rows,None,CoordinateMatrix)
CoordinateMatrix.columns = new_instancemethod(_la.CoordinateMatrix_columns,None,CoordinateMatrix)
CoordinateMatrix.values = new_instancemethod(_la.CoordinateMatrix_values,None,CoordinateMatrix)
CoordinateMatrix.norm = new_instancemethod(_la.CoordinateMatrix_norm,None,CoordinateMatrix)
CoordinateMatrix.base_one = new_instancemethod(_la.CoordinateMatrix_base_one,None,CoordinateMatrix)
CoordinateMatrix_swigregister = _la.CoordinateMatrix_swigregister
CoordinateMatrix_swigregister(CoordinateMatrix)
class uBLASVector(GenericVector):
"""
This class provides a simple vector class based on uBLAS.
It is a simple wrapper for a uBLAS vector implementing the
GenericVector interface.
The interface is intentionally simple. For advanced usage,
access the underlying uBLAS vector and use the standard
uBLAS interface which is documented at
http://www.boost.org/libs/numeric/ublas/doc/index.htm.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* uBLASVector\ (type="global")
Create empty vector
* uBLASVector\ (N, type="global")
Create vector of size N
* uBLASVector\ (x)
Copy constructor
* uBLASVector\ (x)
Construct vector from a ublas_vector
"""
_la.uBLASVector_swiginit(self,_la.new_uBLASVector(*args))
__swig_destroy__ = _la.delete_uBLASVector
def resize(self, *args):
"""
**Overloaded versions**
* resize\ (N)
Resize vector to size N
* resize\ (range)
Resize vector with given ownership range
* resize\ (range, ghost_indices)
Resize vector with given ownership range and with ghost values
"""
return _la.uBLASVector_resize(self, *args)
def get_local(self, *args):
"""
**Overloaded versions**
* get_local\ (block, m, rows)
Get block of values
* get_local\ (values)
Get all values on local process
"""
return _la.uBLASVector_get_local(self, *args)
def gather(self, *args):
"""
**Overloaded versions**
* gather\ (x, indices)
Gather entries into local vector x
* gather\ (x, indices)
Gather entries into x
"""
return _la.uBLASVector_gather(self, *args)
def sum(self, *args):
"""
**Overloaded versions**
* sum\ ()
Return sum of values of vector
* sum\ (rows)
Return sum of selected rows in vector. Repeated entries are
only summed once.
"""
return _la.uBLASVector_sum(self, *args)
def vec(self, *args):
"""
**Overloaded versions**
* vec\ ()
Return reference to uBLAS vector (const version)
* vec\ ()
Return reference to uBLAS vector (non-const version)
"""
return _la.uBLASVector_vec(self, *args)
def _assign(self, *args):
"""
**Overloaded versions**
* operator=\ (x)
Assignment operator
* operator=\ (a)
Assignment operator
* operator=\ (x)
Assignment operator
"""
return _la.uBLASVector__assign(self, *args)
def _data(self, *args):
"""Missing docstring"""
return _la.uBLASVector__data(self, *args)
def data(self, deepcopy=True):
"""
Return an array to underlaying data
This method is only available for the uBLAS linear algebra
backend.
*Arguments*
deepcopy
Return a copy of the data. If set to False a reference
to the Matrix need to be kept, otherwise the data will be
destroyed together with the destruction of the Matrix
"""
ret = self._data()
if deepcopy:
ret = ret.copy()
else:
_attach_base_to_numpy_array(ret, self)
return ret
uBLASVector.resize = new_instancemethod(_la.uBLASVector_resize,None,uBLASVector)
uBLASVector.get_local = new_instancemethod(_la.uBLASVector_get_local,None,uBLASVector)
uBLASVector.gather = new_instancemethod(_la.uBLASVector_gather,None,uBLASVector)
uBLASVector.sum = new_instancemethod(_la.uBLASVector_sum,None,uBLASVector)
uBLASVector.vec = new_instancemethod(_la.uBLASVector_vec,None,uBLASVector)
uBLASVector._assign = new_instancemethod(_la.uBLASVector__assign,None,uBLASVector)
uBLASVector._data = new_instancemethod(_la.uBLASVector__data,None,uBLASVector)
uBLASVector_swigregister = _la.uBLASVector_swigregister
uBLASVector_swigregister(uBLASVector)
class PETScVectorDeleter(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
_la.PETScVectorDeleter_swiginit(self,_la.new_PETScVectorDeleter(*args))
__swig_destroy__ = _la.delete_PETScVectorDeleter
PETScVectorDeleter.__call__ = new_instancemethod(_la.PETScVectorDeleter___call__,None,PETScVectorDeleter)
PETScVectorDeleter_swigregister = _la.PETScVectorDeleter_swigregister
PETScVectorDeleter_swigregister(PETScVectorDeleter)
class PETScVector(GenericVector,PETScObject):
"""
This class provides a simple vector class based on PETSc.
It is a simple wrapper for a PETSc vector pointer (Vec)
implementing the GenericVector interface.
The interface is intentionally simple. For advanced usage,
access the PETSc Vec pointer using the function vec() and
use the standard PETSc interface.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* PETScVector\ (type="global", use_gpu=false)
Create empty vector
* PETScVector\ (N, type="global", use_gpu=false)
Create vector of size N
* PETScVector\ (sparsity_pattern)
Create vector
* PETScVector\ (x)
Copy constructor
* PETScVector\ (x)
Create vector from given PETSc Vec pointer
"""
_la.PETScVector_swiginit(self,_la.new_PETScVector(*args))
__swig_destroy__ = _la.delete_PETScVector
def resize(self, *args):
"""
**Overloaded versions**
* resize\ (N)
Resize vector to global size N
* resize\ (range)
Resize vector with given ownership range
* resize\ (range, ghost_indices)
Resize vector with given ownership range and with ghost values
"""
return _la.PETScVector_resize(self, *args)
def get_local(self, *args):
"""
**Overloaded versions**
* get_local\ (block, m, rows)
Get block of values (values must all live on the local process)
* get_local\ (values)
Get all values on local process
"""
return _la.PETScVector_get_local(self, *args)
def gather(self, *args):
"""
**Overloaded versions**
* gather\ (y, indices)
Gather vector entries into a local vector
* gather\ (x, indices)
Gather entries into x
"""
return _la.PETScVector_gather(self, *args)
def sum(self, *args):
"""
**Overloaded versions**
* sum\ ()
Return sum of values of vector
* sum\ (rows)
Return sum of selected rows in vector
"""
return _la.PETScVector_sum(self, *args)
def reset(self, *args):
"""
Reset data and PETSc vector object
"""
return _la.PETScVector_reset(self, *args)
def _assign(self, *args):
"""
**Overloaded versions**
* operator=\ (x)
Assignment operator
* operator=\ (a)
Assignment operator
* operator=\ (x)
Assignment operator
"""
return _la.PETScVector__assign(self, *args)
def vec(self):
common.dolfin_error("dolfin/swig/la/post.i",
"access PETScVector objects in python",
"dolfin must be configured with petsc4py enabled")
return None
PETScVector.resize = new_instancemethod(_la.PETScVector_resize,None,PETScVector)
PETScVector.get_local = new_instancemethod(_la.PETScVector_get_local,None,PETScVector)
PETScVector.gather = new_instancemethod(_la.PETScVector_gather,None,PETScVector)
PETScVector.sum = new_instancemethod(_la.PETScVector_sum,None,PETScVector)
PETScVector.reset = new_instancemethod(_la.PETScVector_reset,None,PETScVector)
PETScVector._assign = new_instancemethod(_la.PETScVector__assign,None,PETScVector)
PETScVector_swigregister = _la.PETScVector_swigregister
PETScVector_swigregister(PETScVector)
class SparsityPattern(GenericSparsityPattern):
"""
This class implements the GenericSparsityPattern interface.
It is used by most linear algebra backends.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* SparsityPattern\ (primary_dim)
Create empty sparsity pattern
* SparsityPattern\ (dims, ownership_range, off_process_owner, primary_dim)
Create sparsity pattern for a generic tensor
"""
_la.SparsityPattern_swiginit(self,_la.new_SparsityPattern(*args))
__swig_destroy__ = _la.delete_SparsityPattern
SparsityPattern_swigregister = _la.SparsityPattern_swigregister
SparsityPattern_swigregister(SparsityPattern)
class GenericLinearAlgebraFactory(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_GenericLinearAlgebraFactory
def create_matrix(self, *args):
"""
Create empty matrix
"""
return _la.GenericLinearAlgebraFactory_create_matrix(self, *args)
def create_vector(self, *args):
"""
Create empty vector (global)
"""
return _la.GenericLinearAlgebraFactory_create_vector(self, *args)
def create_local_vector(self, *args):
"""
Create empty vector (local)
"""
return _la.GenericLinearAlgebraFactory_create_local_vector(self, *args)
def create_layout(self, *args):
"""
Create empty tensor layout
"""
return _la.GenericLinearAlgebraFactory_create_layout(self, *args)
def create_linear_operator(self, *args):
"""
Create empty linear operator
"""
return _la.GenericLinearAlgebraFactory_create_linear_operator(self, *args)
def create_lu_solver(self, *args):
"""
Create LU solver
"""
return _la.GenericLinearAlgebraFactory_create_lu_solver(self, *args)
def create_krylov_solver(self, *args):
"""
Create Krylov solver
"""
return _la.GenericLinearAlgebraFactory_create_krylov_solver(self, *args)
def lu_solver_methods(self, *args):
"""
Return a list of available LU solver methods.
This function should be overloaded by subclass if non-empty.
"""
return _la.GenericLinearAlgebraFactory_lu_solver_methods(self, *args)
def krylov_solver_methods(self, *args):
"""
Return a list of available Krylov solver methods.
This function should be overloaded by subclass if non-empty.
"""
return _la.GenericLinearAlgebraFactory_krylov_solver_methods(self, *args)
def krylov_solver_preconditioners(self, *args):
"""
Return a list of available preconditioners.
This function should be overloaded by subclass if non-empty.
"""
return _la.GenericLinearAlgebraFactory_krylov_solver_preconditioners(self, *args)
GenericLinearAlgebraFactory.create_matrix = new_instancemethod(_la.GenericLinearAlgebraFactory_create_matrix,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.create_vector = new_instancemethod(_la.GenericLinearAlgebraFactory_create_vector,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.create_local_vector = new_instancemethod(_la.GenericLinearAlgebraFactory_create_local_vector,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.create_layout = new_instancemethod(_la.GenericLinearAlgebraFactory_create_layout,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.create_linear_operator = new_instancemethod(_la.GenericLinearAlgebraFactory_create_linear_operator,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.create_lu_solver = new_instancemethod(_la.GenericLinearAlgebraFactory_create_lu_solver,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.create_krylov_solver = new_instancemethod(_la.GenericLinearAlgebraFactory_create_krylov_solver,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.lu_solver_methods = new_instancemethod(_la.GenericLinearAlgebraFactory_lu_solver_methods,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.krylov_solver_methods = new_instancemethod(_la.GenericLinearAlgebraFactory_krylov_solver_methods,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory.krylov_solver_preconditioners = new_instancemethod(_la.GenericLinearAlgebraFactory_krylov_solver_preconditioners,None,GenericLinearAlgebraFactory)
GenericLinearAlgebraFactory_swigregister = _la.GenericLinearAlgebraFactory_swigregister
GenericLinearAlgebraFactory_swigregister(GenericLinearAlgebraFactory)
class DefaultFactory(GenericLinearAlgebraFactory):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
_la.DefaultFactory_swiginit(self,_la.new_DefaultFactory(*args))
__swig_destroy__ = _la.delete_DefaultFactory
def factory(*args):
"""
Return instance of default backend
"""
return _la.DefaultFactory_factory(*args)
factory = staticmethod(factory)
DefaultFactory_swigregister = _la.DefaultFactory_swigregister
DefaultFactory_swigregister(DefaultFactory)
def DefaultFactory_factory(*args):
"""
Return instance of default backend
"""
return _la.DefaultFactory_factory(*args)
class PETScUserPreconditioner(PETScObject):
"""
This class specifies the interface for user-defined Krylov
method PETScPreconditioners. A user wishing to implement her own
PETScPreconditioner needs only supply a function that approximately
solves the linear system given a right-hand side.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
if self.__class__ == PETScUserPreconditioner:
_self = None
else:
_self = self
_la.PETScUserPreconditioner_swiginit(self,_la.new_PETScUserPreconditioner(_self, *args))
__swig_destroy__ = _la.delete_PETScUserPreconditioner
setup = staticmethod(_la.PETScUserPreconditioner_setup)
def solve(self, *args):
"""
Solve linear system approximately for given right-hand side b
"""
return _la.PETScUserPreconditioner_solve(self, *args)
def __disown__(self):
self.this.disown()
_la.disown_PETScUserPreconditioner(self)
return weakref_proxy(self)
PETScUserPreconditioner.solve = new_instancemethod(_la.PETScUserPreconditioner_solve,None,PETScUserPreconditioner)
PETScUserPreconditioner_swigregister = _la.PETScUserPreconditioner_swigregister
PETScUserPreconditioner_swigregister(PETScUserPreconditioner)
def PETScUserPreconditioner_setup(*args):
return _la.PETScUserPreconditioner_setup(*args)
PETScUserPreconditioner_setup = _la.PETScUserPreconditioner_setup
class PETScFactory(GenericLinearAlgebraFactory):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_PETScFactory
def instance(*args):
"""
Return singleton instance
"""
return _la.PETScFactory_instance(*args)
instance = staticmethod(instance)
PETScFactory_swigregister = _la.PETScFactory_swigregister
PETScFactory_swigregister(PETScFactory)
def PETScFactory_instance(*args):
"""
Return singleton instance
"""
return _la.PETScFactory_instance(*args)
class STLFactory(GenericLinearAlgebraFactory):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_STLFactory
def instance(*args):
"""
Return singleton instance
"""
return _la.STLFactory_instance(*args)
instance = staticmethod(instance)
STLFactory_swigregister = _la.STLFactory_swigregister
STLFactory_swigregister(STLFactory)
def STLFactory_instance(*args):
"""
Return singleton instance
"""
return _la.STLFactory_instance(*args)
class SLEPcEigenSolver(common.Variable,PETScObject):
"""
This class provides an eigenvalue solver for PETSc matrices.
It is a wrapper for the SLEPc eigenvalue solver.
The following parameters may be specified to control the solver.
1. "spectrum"
This parameter controls which part of the spectrum to compute.
Possible values are
"largest magnitude" (eigenvalues with largest magnitude)
"smallest magnitude" (eigenvalues with smallest magnitude)
"largest real" (eigenvalues with largest double part)
"smallest real" (eigenvalues with smallest double part)
"largest imaginary" (eigenvalues with largest imaginary part)
"smallest imaginary" (eigenvalues with smallest imaginary part)
For SLEPc versions >= 3.1 , the following values are also possible
"target magnitude" (eigenvalues closest to target in magnitude)
"target real" (eigenvalues closest to target in real part)
"target imaginary" (eigenvalues closest to target in imaginary part)
The default is "largest magnitude"
2. "solver"
This parameter controls which algorithm is used by SLEPc.
Possible values are
"power" (power iteration)
"subspace" (subspace iteration)
"arnoldi" (Arnoldi)
"lanczos" (Lanczos)
"krylov-schur" (Krylov-Schur)
"lapack" (LAPACK, all values, direct, small systems only)
"arpack" (ARPACK)
The default is "krylov-schur"
3. "tolerance"
This parameter controls the tolerance used by SLEPc.
Possible values are positive double numbers.
The default is 1e-15;
4. "maximum_iterations"
This parameter controls the maximum number of iterations used by SLEPc.
Possible values are positive integers.
Note that both the tolerance and the number of iterations must be
specified if either one is specified.
5. "problem_type"
This parameter can be used to give extra information about the
type of the eigenvalue problem. Some solver types require this
extra piece of information. Possible values are:
"hermitian" (Hermitian)
"non_hermitian" (Non-Hermitian)
"gen_hermitian" (Generalized Hermitian)
"gen_non_hermitian" (Generalized Non-Hermitian)
"pos_gen_non_hermitian" (Generalized Non-Hermitian with positive semidefinite B)
6. "spectral_transform"
This parameter controls the application of a spectral transform. A
spectral transform can be used to enhance the convergence of the
eigensolver and in particular to only compute eigenvalues in the
interior of the spectrum. Possible values are:
"shift-and-invert" (A shift-and-invert transform)
Note that if a spectral transform is given, then also a non-zero
spectral shift parameter has to be provided.
The default is no spectral transform.
7. "spectral_shift"
This parameter controls the spectral shift used by the spectral
transform and must be provided if a spectral transform is given. The
possible values are real numbers.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* SLEPcEigenSolver\ (A)
Create eigenvalue solver for Ax = \lambda x
* SLEPcEigenSolver\ (A, B)
Create eigenvalue solver Ax = \lambda Bx
* SLEPcEigenSolver\ (A)
Create eigenvalue solver for Ax = \lambda x
* SLEPcEigenSolver\ (A, B)
Create eigenvalue solver for Ax = \lambda x
"""
_la.SLEPcEigenSolver_swiginit(self,_la.new_SLEPcEigenSolver(*args))
__swig_destroy__ = _la.delete_SLEPcEigenSolver
def solve(self, *args):
"""
**Overloaded versions**
* solve\ ()
Compute all eigenpairs of the matrix A (solve Ax = \lambda x)
* solve\ (n)
Compute the n first eigenpairs of the matrix A (solve Ax = \lambda x)
"""
return _la.SLEPcEigenSolver_solve(self, *args)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.SLEPcEigenSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
def _get_eigenvalue(self, *args):
"""Missing docstring"""
return _la.SLEPcEigenSolver__get_eigenvalue(self, *args)
def _get_eigenpair(self, *args):
"""Missing docstring"""
return _la.SLEPcEigenSolver__get_eigenpair(self, *args)
def get_eigenpair(self, i = 0, r_vec = None, c_vec = None,):
"""Gets the i-th solution of the eigenproblem"""
r_vec = r_vec or PETScVector()
c_vec = c_vec or PETScVector()
lr, lc = self._get_eigenpair(r_vec, c_vec, i)
return lr, lc, r_vec, c_vec
def get_eigenvalue(self, i = 0):
"""Gets the i-th eigenvalue of the eigenproblem"""
return self._get_eigenvalue(i)
SLEPcEigenSolver.solve = new_instancemethod(_la.SLEPcEigenSolver_solve,None,SLEPcEigenSolver)
SLEPcEigenSolver.get_iteration_number = new_instancemethod(_la.SLEPcEigenSolver_get_iteration_number,None,SLEPcEigenSolver)
SLEPcEigenSolver.get_number_converged = new_instancemethod(_la.SLEPcEigenSolver_get_number_converged,None,SLEPcEigenSolver)
SLEPcEigenSolver.set_deflation_space = new_instancemethod(_la.SLEPcEigenSolver_set_deflation_space,None,SLEPcEigenSolver)
SLEPcEigenSolver._get_eigenvalue = new_instancemethod(_la.SLEPcEigenSolver__get_eigenvalue,None,SLEPcEigenSolver)
SLEPcEigenSolver._get_eigenpair = new_instancemethod(_la.SLEPcEigenSolver__get_eigenpair,None,SLEPcEigenSolver)
SLEPcEigenSolver_swigregister = _la.SLEPcEigenSolver_swigregister
SLEPcEigenSolver_swigregister(SLEPcEigenSolver)
def SLEPcEigenSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.SLEPcEigenSolver_default_parameters(*args)
class uBLASPreconditioner(object):
"""
This class specifies the interface for preconditioners for the
uBLAS Krylov solver.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_uBLASPreconditioner
def init(self, *args):
"""
**Overloaded versions**
* init\ (P)
Initialise preconditioner (sparse matrix)
* init\ (P)
Initialise preconditioner (dense matrix)
* init\ (P)
Initialise preconditioner (virtual matrix)
"""
return _la.uBLASPreconditioner_init(self, *args)
def solve(self, *args):
"""
Solve linear system (M^-1)Ax = y
"""
return _la.uBLASPreconditioner_solve(self, *args)
uBLASPreconditioner.init = new_instancemethod(_la.uBLASPreconditioner_init,None,uBLASPreconditioner)
uBLASPreconditioner.solve = new_instancemethod(_la.uBLASPreconditioner_solve,None,uBLASPreconditioner)
uBLASPreconditioner_swigregister = _la.uBLASPreconditioner_swigregister
uBLASPreconditioner_swigregister(uBLASPreconditioner)
class uBLASKrylovSolver(GenericLinearSolver):
"""
This class implements Krylov methods for linear systems
of the form Ax = b using uBLAS data types.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* uBLASKrylovSolver\ (method="default", preconditioner="default")
Create Krylov solver for a particular method and preconditioner
* uBLASKrylovSolver\ (pc)
Create Krylov solver for a particular uBLASPreconditioner
* uBLASKrylovSolver\ (method, pc)
Create Krylov solver for a particular method and uBLASPreconditioner
"""
_la.uBLASKrylovSolver_swiginit(self,_la.new_uBLASKrylovSolver(*args))
__swig_destroy__ = _la.delete_uBLASKrylovSolver
def get_operator(self, *args):
"""
Return the operator (matrix)
"""
return _la.uBLASKrylovSolver_get_operator(self, *args)
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (x, b)
Solve linear system Ax = b and return number of iterations
* solve\ (A, x, b)
Solve linear system Ax = b and return number of iterations
"""
return _la.uBLASKrylovSolver_solve(self, *args)
def methods(*args):
"""
Return a list of available solver methods
"""
return _la.uBLASKrylovSolver_methods(*args)
methods = staticmethod(methods)
def preconditioners(*args):
"""
Return a list of available preconditioners
"""
return _la.uBLASKrylovSolver_preconditioners(*args)
preconditioners = staticmethod(preconditioners)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.uBLASKrylovSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
uBLASKrylovSolver.get_operator = new_instancemethod(_la.uBLASKrylovSolver_get_operator,None,uBLASKrylovSolver)
uBLASKrylovSolver.solve = new_instancemethod(_la.uBLASKrylovSolver_solve,None,uBLASKrylovSolver)
uBLASKrylovSolver_swigregister = _la.uBLASKrylovSolver_swigregister
uBLASKrylovSolver_swigregister(uBLASKrylovSolver)
def uBLASKrylovSolver_methods(*args):
"""
Return a list of available solver methods
"""
return _la.uBLASKrylovSolver_methods(*args)
def uBLASKrylovSolver_preconditioners(*args):
"""
Return a list of available preconditioners
"""
return _la.uBLASKrylovSolver_preconditioners(*args)
def uBLASKrylovSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.uBLASKrylovSolver_default_parameters(*args)
class uBLASILUPreconditioner(uBLASPreconditioner):
"""
This class implements an incomplete LU factorization (ILU)
preconditioner for the uBLAS Krylov solver.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
_la.uBLASILUPreconditioner_swiginit(self,_la.new_uBLASILUPreconditioner(*args))
__swig_destroy__ = _la.delete_uBLASILUPreconditioner
uBLASILUPreconditioner_swigregister = _la.uBLASILUPreconditioner_swigregister
uBLASILUPreconditioner_swigregister(uBLASILUPreconditioner)
class Vector(GenericVector):
"""
This class provides the default DOLFIN vector class,
based on the default DOLFIN linear algebra backend.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* Vector\ ()
Create empty vector
* Vector\ (N)
Create vector of size N
* Vector\ (x)
Copy constructor
* Vector\ (x)
Create a Vector from a GenericVetor
"""
_la.Vector_swiginit(self,_la.new_Vector(*args))
def resize(self, *args):
"""
**Overloaded versions**
* resize\ (N)
Resize vector to size N
* resize\ (range)
Resize vector with given ownership range
* resize\ (range, ghost_indices)
Resize vector with given ownership range and with ghost values
"""
return _la.Vector_resize(self, *args)
def get_local(self, *args):
"""
**Overloaded versions**
* get_local\ (block, m, rows)
Get block of values (values must all live on the local process)
* get_local\ (values)
Get all values on local process
"""
return _la.Vector_get_local(self, *args)
def gather(self, *args):
"""
**Overloaded versions**
* gather\ (x, indices)
Gather entries into local vector x
* gather\ (x, indices)
Gather entries into x
"""
return _la.Vector_gather(self, *args)
def sum(self, *args):
"""
Return sum of values of vector
"""
return _la.Vector_sum(self, *args)
def shared_instance(self, *args):
"""
**Overloaded versions**
* shared_instance\ ()
Return concrete shared ptr instance / unwrap (const version)
* shared_instance\ ()
Return concrete shared ptr instance / unwrap (non-const version)
"""
return _la.Vector_shared_instance(self, *args)
def _assign(self, *args):
"""
**Overloaded versions**
* operator=\ (x)
Assignment operator
* operator=\ (a)
Assignment operator
* operator=\ (x)
Assignment operator
"""
return _la.Vector__assign(self, *args)
def _data(self, *args):
"""Missing docstring"""
return _la.Vector__data(self, *args)
def data(self, deepcopy=True):
"""
Return an array to underlaying data
This method is only available for the uBLAS linear algebra
backend.
*Arguments*
deepcopy
Return a copy of the data. If set to False a reference
to the Matrix need to be kept, otherwise the data will be
destroyed together with the destruction of the Matrix
"""
ret = self._data()
if deepcopy:
ret = ret.copy()
else:
_attach_base_to_numpy_array(ret, self)
return ret
__swig_destroy__ = _la.delete_Vector
Vector.resize = new_instancemethod(_la.Vector_resize,None,Vector)
Vector.get_local = new_instancemethod(_la.Vector_get_local,None,Vector)
Vector.gather = new_instancemethod(_la.Vector_gather,None,Vector)
Vector.sum = new_instancemethod(_la.Vector_sum,None,Vector)
Vector.shared_instance = new_instancemethod(_la.Vector_shared_instance,None,Vector)
Vector._assign = new_instancemethod(_la.Vector__assign,None,Vector)
Vector._data = new_instancemethod(_la.Vector__data,None,Vector)
Vector_swigregister = _la.Vector_swigregister
Vector_swigregister(Vector)
class Matrix(GenericMatrix):
"""
This class provides the default DOLFIN matrix class,
based on the default DOLFIN linear algebra backend.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* Matrix\ ()
Create empty matrix
* Matrix\ (A)
Copy constructor
* Matrix\ (A)
Create a Vector from a GenericVetor
"""
_la.Matrix_swiginit(self,_la.new_Matrix(*args))
__swig_destroy__ = _la.delete_Matrix
def zero(self, *args):
"""
**Overloaded versions**
* zero\ ()
Set all entries to zero and keep any sparse structure
* zero\ (m, rows)
Set given rows to zero
"""
return _la.Matrix_zero(self, *args)
def shared_instance(self, *args):
"""
**Overloaded versions**
* shared_instance\ ()
Return concrete shared ptr instance / unwrap (const version)
* shared_instance\ ()
Return concrete shared ptr instance / unwrap (non-const version)
"""
return _la.Matrix_shared_instance(self, *args)
def assign(self, *args):
"""
**Overloaded versions**
* operator=\ (A)
Assignment operator
* operator=\ (A)
Assignment operator
"""
return _la.Matrix_assign(self, *args)
Matrix.zero = new_instancemethod(_la.Matrix_zero,None,Matrix)
Matrix.shared_instance = new_instancemethod(_la.Matrix_shared_instance,None,Matrix)
Matrix.assign = new_instancemethod(_la.Matrix_assign,None,Matrix)
Matrix_swigregister = _la.Matrix_swigregister
Matrix_swigregister(Matrix)
class Scalar(GenericTensor):
"""
This class represents a real-valued scalar quantity and
implements the GenericTensor interface for scalars.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Create zero scalar
"""
_la.Scalar_swiginit(self,_la.new_Scalar(*args))
__swig_destroy__ = _la.delete_Scalar
def resize(self, *args):
"""
Resize tensor to given dimensions
"""
return _la.Scalar_resize(self, *args)
def add(self, *args):
"""
**Overloaded versions**
* add\ (block, num_rows, rows)
Add block of values
* add\ (block, rows)
Add block of values
* add\ (block, rows)
Add block of values
"""
return _la.Scalar_add(self, *args)
def copy(self, *args):
"""
Return copy of scalar
"""
return _la.Scalar_copy(self, *args)
def __float__(self, *args):
"""
Cast to double
"""
return _la.Scalar___float__(self, *args)
def assign(self, *args):
"""
Assignment from double
"""
return _la.Scalar_assign(self, *args)
def getval(self, *args):
"""
Get value
"""
return _la.Scalar_getval(self, *args)
Scalar.resize = new_instancemethod(_la.Scalar_resize,None,Scalar)
Scalar.add = new_instancemethod(_la.Scalar_add,None,Scalar)
Scalar.copy = new_instancemethod(_la.Scalar_copy,None,Scalar)
Scalar.__float__ = new_instancemethod(_la.Scalar___float__,None,Scalar)
Scalar.assign = new_instancemethod(_la.Scalar_assign,None,Scalar)
Scalar.getval = new_instancemethod(_la.Scalar_getval,None,Scalar)
Scalar_swigregister = _la.Scalar_swigregister
Scalar_swigregister(Scalar)
class LinearSolver(GenericLinearSolver):
"""
This class provides a general solver for linear systems Ax = b.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Create linear solver
"""
_la.LinearSolver_swiginit(self,_la.new_LinearSolver(*args))
__swig_destroy__ = _la.delete_LinearSolver
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (A, x, b)
Solve linear system Ax = b
* solve\ (x, b)
Solve linear system Ax = b
"""
return _la.LinearSolver_solve(self, *args)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.LinearSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
LinearSolver.solve = new_instancemethod(_la.LinearSolver_solve,None,LinearSolver)
LinearSolver_swigregister = _la.LinearSolver_swigregister
LinearSolver_swigregister(LinearSolver)
def LinearSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.LinearSolver_default_parameters(*args)
class KrylovSolver(GenericLinearSolver):
"""
This class defines an interface for a Krylov solver. The
approproiate solver is chosen on the basis of the matrix/vector
type.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* KrylovSolver\ (method="default", preconditioner="default")
Constructor
* KrylovSolver\ (A, method="default", preconditioner="default")
Constructor
"""
_la.KrylovSolver_swiginit(self,_la.new_KrylovSolver(*args))
__swig_destroy__ = _la.delete_KrylovSolver
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (x, b)
Solve linear system Ax = b
* solve\ (A, x, b)
Solve linear system Ax = b
"""
return _la.KrylovSolver_solve(self, *args)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.KrylovSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
KrylovSolver.solve = new_instancemethod(_la.KrylovSolver_solve,None,KrylovSolver)
KrylovSolver_swigregister = _la.KrylovSolver_swigregister
KrylovSolver_swigregister(KrylovSolver)
def KrylovSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.KrylovSolver_default_parameters(*args)
class LUSolver(GenericLUSolver):
"""
LU solver for the built-in LA backends.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* LUSolver\ ("default")
Constructor
* LUSolver\ (A, method="default")
Constructor
"""
_la.LUSolver_swiginit(self,_la.new_LUSolver(*args))
__swig_destroy__ = _la.delete_LUSolver
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (x, b)
Solve linear system Ax = b
* solve\ (A, x, b)
Solve linear system
"""
return _la.LUSolver_solve(self, *args)
def solve_transpose(self, *args):
"""
**Overloaded versions**
* solve_transpose\ (x, b)
Solve linear system A^Tx = b
* solve_transpose\ (A, x, b)
Solve linear system
"""
return _la.LUSolver_solve_transpose(self, *args)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.LUSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
LUSolver.solve = new_instancemethod(_la.LUSolver_solve,None,LUSolver)
LUSolver.solve_transpose = new_instancemethod(_la.LUSolver_solve_transpose,None,LUSolver)
LUSolver_swigregister = _la.LUSolver_swigregister
LUSolver_swigregister(LUSolver)
def LUSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.LUSolver_default_parameters(*args)
def la_solve(*args):
"""
Solve linear system Ax = b
"""
return _la.la_solve(*args)
def list_linear_algebra_backends(*args):
"""
List available linear algebra backends
"""
return _la.list_linear_algebra_backends(*args)
def list_linear_solver_methods(*args):
"""
List available solver methods for current linear algebra backend
"""
return _la.list_linear_solver_methods(*args)
def list_lu_solver_methods(*args):
"""
List available LU methods for current linear algebra backend
"""
return _la.list_lu_solver_methods(*args)
def list_krylov_solver_methods(*args):
"""
List available Krylov methods for current linear algebra backend
"""
return _la.list_krylov_solver_methods(*args)
def list_krylov_solver_preconditioners(*args):
"""
List available preconditioners for current linear algebra
backend
"""
return _la.list_krylov_solver_preconditioners(*args)
def has_linear_algebra_backend(*args):
return _la.has_linear_algebra_backend(*args)
has_linear_algebra_backend = _la.has_linear_algebra_backend
def has_lu_solver_method(*args):
"""
Return true if LU method for the current linear algebra backend is
available
"""
return _la.has_lu_solver_method(*args)
def has_krylov_solver_method(*args):
"""
Return true if Krylov method for the current linear algebra
backend is available
"""
return _la.has_krylov_solver_method(*args)
def has_krylov_solver_preconditioner(*args):
"""
Return true if Preconditioner for the current linear algebra
backend is available
"""
return _la.has_krylov_solver_preconditioner(*args)
def linear_algebra_backends(*args):
"""
Return available linear algebra backends
"""
return _la.linear_algebra_backends(*args)
def linear_solver_methods(*args):
"""
Return a list of available solver methods for current linear
algebra backend
"""
return _la.linear_solver_methods(*args)
def lu_solver_methods(*args):
"""
Return a list of available LU methods for current linear algebra
backend
"""
return _la.lu_solver_methods(*args)
def krylov_solver_methods(*args):
"""
Return a list of available Krylov methods for current linear
algebra backend
"""
return _la.krylov_solver_methods(*args)
def krylov_solver_preconditioners(*args):
"""
Return a list of available preconditioners for current linear
algebra backend
"""
return _la.krylov_solver_preconditioners(*args)
def residual(*args):
"""
Compute residual ||Ax - b||
"""
return _la.residual(*args)
def normalize(*args):
"""
Normalize vector according to given normalization type
"""
return _la.normalize(*args)
class BlockVector(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
_la.BlockVector_swiginit(self,_la.new_BlockVector(*args))
__swig_destroy__ = _la.delete_BlockVector
def copy(self, *args):
"""
Return copy of tensor
"""
return _la.BlockVector_copy(self, *args)
def set_block(self, *args):
"""
Set function
"""
return _la.BlockVector_set_block(self, *args)
def get_block(self, *args):
"""
**Overloaded versions**
* get_block\ (i)
Get sub-vector (const)
* get_block\ ()
Get sub-vector (non-const)
"""
return _la.BlockVector_get_block(self, *args)
def axpy(self, *args):
"""
Add multiple of given vector (AXPY operation)
"""
return _la.BlockVector_axpy(self, *args)
def inner(self, *args):
"""
Return inner product with given vector
"""
return _la.BlockVector_inner(self, *args)
def norm(self, *args):
"""
Return norm of vector
"""
return _la.BlockVector_norm(self, *args)
def min(self, *args):
"""
Return minimum value of vector
"""
return _la.BlockVector_min(self, *args)
def max(self, *args):
"""
Return maximum value of vector
"""
return _la.BlockVector_max(self, *args)
def __imul__(self, *args):
"""
Multiply vector by given number
"""
return _la.BlockVector___imul__(self, *args)
def __idiv__(self, *args):
"""
Divide vector by given number
"""
return _la.BlockVector___idiv__(self, *args)
def __iadd__(self, *args):
"""
Add given vector
"""
return _la.BlockVector___iadd__(self, *args)
def __isub__(self, *args):
"""
Subtract given vector
"""
return _la.BlockVector___isub__(self, *args)
def empty(self, *args):
"""
Return true if empty
"""
return _la.BlockVector_empty(self, *args)
def size(self, *args):
"""
Number of vectors
"""
return _la.BlockVector_size(self, *args)
def str(self, *args):
"""
Return informal string representation (pretty-print)
"""
return _la.BlockVector_str(self, *args)
def __getitem__(self, i):
return self.get_block(i)
def __setitem__(self, i, m):
if not hasattr(self, "_items"):
self._items = {}
self._items[i] = m
self.set_block(i, m)
def __add__(self, v):
a = self.copy()
a += v
return a
def __sub__(self, v):
a = self.copy()
a -= v
return a
def __mul__(self, v):
a = self.copy()
a *= v
return a
def __rmul__(self, v):
return self.__mul__(v)
BlockVector.copy = new_instancemethod(_la.BlockVector_copy,None,BlockVector)
BlockVector.set_block = new_instancemethod(_la.BlockVector_set_block,None,BlockVector)
BlockVector.get_block = new_instancemethod(_la.BlockVector_get_block,None,BlockVector)
BlockVector.axpy = new_instancemethod(_la.BlockVector_axpy,None,BlockVector)
BlockVector.inner = new_instancemethod(_la.BlockVector_inner,None,BlockVector)
BlockVector.norm = new_instancemethod(_la.BlockVector_norm,None,BlockVector)
BlockVector.min = new_instancemethod(_la.BlockVector_min,None,BlockVector)
BlockVector.max = new_instancemethod(_la.BlockVector_max,None,BlockVector)
BlockVector.__imul__ = new_instancemethod(_la.BlockVector___imul__,None,BlockVector)
BlockVector.__idiv__ = new_instancemethod(_la.BlockVector___idiv__,None,BlockVector)
BlockVector.__iadd__ = new_instancemethod(_la.BlockVector___iadd__,None,BlockVector)
BlockVector.__isub__ = new_instancemethod(_la.BlockVector___isub__,None,BlockVector)
BlockVector.empty = new_instancemethod(_la.BlockVector_empty,None,BlockVector)
BlockVector.size = new_instancemethod(_la.BlockVector_size,None,BlockVector)
BlockVector.str = new_instancemethod(_la.BlockVector_str,None,BlockVector)
BlockVector_swigregister = _la.BlockVector_swigregister
BlockVector_swigregister(BlockVector)
class BlockMatrix(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
_la.BlockMatrix_swiginit(self,_la.new_BlockMatrix(*args))
__swig_destroy__ = _la.delete_BlockMatrix
def set_block(self, *args):
"""
Set block
"""
return _la.BlockMatrix_set_block(self, *args)
def get_block(self, *args):
"""
**Overloaded versions**
* get_block\ (i, j)
Get block (const version)
* get_block\ (i, j)
Get block
"""
return _la.BlockMatrix_get_block(self, *args)
def size(self, *args):
"""
Return size of given dimension
"""
return _la.BlockMatrix_size(self, *args)
def zero(self, *args):
"""
Set all entries to zero and keep any sparse structure
"""
return _la.BlockMatrix_zero(self, *args)
def apply(self, *args):
"""
Finalize assembly of tensor
"""
return _la.BlockMatrix_apply(self, *args)
def str(self, *args):
"""
Return informal string representation (pretty-print)
"""
return _la.BlockMatrix_str(self, *args)
def mult(self, *args):
"""
Matrix-vector product, y = Ax
"""
return _la.BlockMatrix_mult(self, *args)
def schur_approximation(self, *args):
"""
Create a crude explicit Schur approximation of S = D - C A^-1 B of (A B; C D)
If symmetry != 0, then the caller promises that B = symmetry * transpose(C).
"""
return _la.BlockMatrix_schur_approximation(self, *args)
def __mul__(self, other):
v = BlockVector(self.size(0))
self.mult(other, v)
return v
def __getitem__(self, t):
i,j = t
return self.get_block(i, j)
def __setitem__(self, t, m):
if not hasattr(self, "_items"):
self._items = {}
self._items[t] = m
i,j = t
self.set_block(i, j, m)
BlockMatrix.set_block = new_instancemethod(_la.BlockMatrix_set_block,None,BlockMatrix)
BlockMatrix.get_block = new_instancemethod(_la.BlockMatrix_get_block,None,BlockMatrix)
BlockMatrix.size = new_instancemethod(_la.BlockMatrix_size,None,BlockMatrix)
BlockMatrix.zero = new_instancemethod(_la.BlockMatrix_zero,None,BlockMatrix)
BlockMatrix.apply = new_instancemethod(_la.BlockMatrix_apply,None,BlockMatrix)
BlockMatrix.str = new_instancemethod(_la.BlockMatrix_str,None,BlockMatrix)
BlockMatrix.mult = new_instancemethod(_la.BlockMatrix_mult,None,BlockMatrix)
BlockMatrix.schur_approximation = new_instancemethod(_la.BlockMatrix_schur_approximation,None,BlockMatrix)
BlockMatrix_swigregister = _la.BlockMatrix_swigregister
BlockMatrix_swigregister(BlockMatrix)
class TensorProductVector(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Create tensor product vector with given dimensions
"""
_la.TensorProductVector_swiginit(self,_la.new_TensorProductVector(*args))
__swig_destroy__ = _la.delete_TensorProductVector
def str(self, *args):
"""
Return informal string representation (pretty-print)
"""
return _la.TensorProductVector_str(self, *args)
TensorProductVector.str = new_instancemethod(_la.TensorProductVector_str,None,TensorProductVector)
TensorProductVector_swigregister = _la.TensorProductVector_swigregister
TensorProductVector_swigregister(TensorProductVector)
class LinearOperator(GenericLinearOperator):
"""
This class defines an interface for linear operators defined
only in terms of their action (matrix-vector product) and can be
used for matrix-free solution of linear systems. The linear
algebra backend is decided at run-time based on the present
value of the "linear_algebra_backend" parameter.
To define a linear operator, users need to inherit from this
class and overload the function mult(x, y) which defines the
action of the matrix on the vector x as y = Ax.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Create linear operator
"""
if self.__class__ == LinearOperator:
_self = None
else:
_self = self
_la.LinearOperator_swiginit(self,_la.new_LinearOperator(_self, *args))
__swig_destroy__ = _la.delete_LinearOperator
def size(self, *args):
"""
Return size of given dimension
"""
return _la.LinearOperator_size(self, *args)
def mult(self, *args):
"""
Compute matrix-vector product y = Ax
"""
return _la.LinearOperator_mult(self, *args)
def str(self, *args):
"""
Return informal string representation (pretty-print)
"""
return _la.LinearOperator_str(self, *args)
def shared_instance(self, *args):
"""
**Overloaded versions**
* shared_instance\ ()
Return concrete instance / unwrap (const shared pointer version)
* shared_instance\ ()
Return concrete instance / unwrap (shared pointer version)
"""
return _la.LinearOperator_shared_instance(self, *args)
def __disown__(self):
self.this.disown()
_la.disown_LinearOperator(self)
return weakref_proxy(self)
LinearOperator.size = new_instancemethod(_la.LinearOperator_size,None,LinearOperator)
LinearOperator.mult = new_instancemethod(_la.LinearOperator_mult,None,LinearOperator)
LinearOperator.str = new_instancemethod(_la.LinearOperator_str,None,LinearOperator)
LinearOperator.shared_instance = new_instancemethod(_la.LinearOperator_shared_instance,None,LinearOperator)
LinearOperator.init_layout = new_instancemethod(_la.LinearOperator_init_layout,None,LinearOperator)
LinearOperator_swigregister = _la.LinearOperator_swigregister
LinearOperator_swigregister(LinearOperator)
class uBLASSparseMatrix(GenericMatrix):
"""
This class provides a simple matrix class based on uBLAS.
It is a simple wrapper for a uBLAS matrix implementing the
GenericMatrix interface.
The interface is intentionally simple. For advanced usage,
access the underlying uBLAS matrix and use the standard
uBLAS interface which is documented at
http://www.boost.org/libs/numeric/ublas/doc/index.htm.
Developer note: specialised member functions must be
inlined to avoid link errors.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* uBLASMatrix\ ()
Create empty matrix
* uBLASMatrix\ (M, N)
Create M x N matrix
* uBLASMatrix\ (A)
Copy constructor
* uBLASMatrix\ (A)
Create matrix from given uBLAS matrix expression
"""
_la.uBLASSparseMatrix_swiginit(self,_la.new_uBLASSparseMatrix(*args))
__swig_destroy__ = _la.delete_uBLASSparseMatrix
def resize(self, *args):
"""
**Overloaded versions**
* resize\ (M, N)
Resize matrix to M x N
* resize\ (z, dim)
Resize vector z to be compatible with the matrix-vector product
y = Ax. In the parallel case, both size and layout are
important.
*Arguments*
dim (int)
The dimension (axis): dim = 0 --> z = y, dim = 1 --> z = x
"""
return _la.uBLASSparseMatrix_resize(self, *args)
def zero(self, *args):
"""
**Overloaded versions**
* zero\ ()
Set all entries to zero and keep any sparse structure
* zero\ (m, rows)
Set given rows to zero
"""
return _la.uBLASSparseMatrix_zero(self, *args)
def mat(self, *args):
"""
**Overloaded versions**
* mat\ ()
Return reference to uBLAS matrix (const version)
* mat\ ()
Return reference to uBLAS matrix (non-const version)
"""
return _la.uBLASSparseMatrix_mat(self, *args)
def solve(self, *args):
"""
Solve Ax = b out-of-place using uBLAS (A is not destroyed)
"""
return _la.uBLASSparseMatrix_solve(self, *args)
def solve_in_place(self, *args):
"""
**Overloaded versions**
* solve_in_place\ (x, b)
Solve Ax = b in-place using uBLAS(A is destroyed)
* solve_in_place\ (X)
General uBLAS LU solver which accepts both vector and matrix right-hand sides
"""
return _la.uBLASSparseMatrix_solve_in_place(self, *args)
def invert(self, *args):
"""
Compute inverse of matrix
"""
return _la.uBLASSparseMatrix_invert(self, *args)
def lump(self, *args):
"""
Lump matrix into vector m
"""
return _la.uBLASSparseMatrix_lump(self, *args)
def assign(self, *args):
"""
**Overloaded versions**
* operator=\ (A)
Assignment operator
* operator=\ (A)
Assignment operator
"""
return _la.uBLASSparseMatrix_assign(self, *args)
uBLASSparseMatrix.resize = new_instancemethod(_la.uBLASSparseMatrix_resize,None,uBLASSparseMatrix)
uBLASSparseMatrix.zero = new_instancemethod(_la.uBLASSparseMatrix_zero,None,uBLASSparseMatrix)
uBLASSparseMatrix.mat = new_instancemethod(_la.uBLASSparseMatrix_mat,None,uBLASSparseMatrix)
uBLASSparseMatrix.solve = new_instancemethod(_la.uBLASSparseMatrix_solve,None,uBLASSparseMatrix)
uBLASSparseMatrix.solve_in_place = new_instancemethod(_la.uBLASSparseMatrix_solve_in_place,None,uBLASSparseMatrix)
uBLASSparseMatrix.invert = new_instancemethod(_la.uBLASSparseMatrix_invert,None,uBLASSparseMatrix)
uBLASSparseMatrix.lump = new_instancemethod(_la.uBLASSparseMatrix_lump,None,uBLASSparseMatrix)
uBLASSparseMatrix.assign = new_instancemethod(_la.uBLASSparseMatrix_assign,None,uBLASSparseMatrix)
uBLASSparseMatrix_swigregister = _la.uBLASSparseMatrix_swigregister
uBLASSparseMatrix_swigregister(uBLASSparseMatrix)
class uBLASDenseMatrix(GenericMatrix):
"""
This class provides a simple matrix class based on uBLAS.
It is a simple wrapper for a uBLAS matrix implementing the
GenericMatrix interface.
The interface is intentionally simple. For advanced usage,
access the underlying uBLAS matrix and use the standard
uBLAS interface which is documented at
http://www.boost.org/libs/numeric/ublas/doc/index.htm.
Developer note: specialised member functions must be
inlined to avoid link errors.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* uBLASMatrix\ ()
Create empty matrix
* uBLASMatrix\ (M, N)
Create M x N matrix
* uBLASMatrix\ (A)
Copy constructor
* uBLASMatrix\ (A)
Create matrix from given uBLAS matrix expression
"""
_la.uBLASDenseMatrix_swiginit(self,_la.new_uBLASDenseMatrix(*args))
__swig_destroy__ = _la.delete_uBLASDenseMatrix
def resize(self, *args):
"""
**Overloaded versions**
* resize\ (M, N)
Resize matrix to M x N
* resize\ (z, dim)
Resize vector z to be compatible with the matrix-vector product
y = Ax. In the parallel case, both size and layout are
important.
*Arguments*
dim (int)
The dimension (axis): dim = 0 --> z = y, dim = 1 --> z = x
"""
return _la.uBLASDenseMatrix_resize(self, *args)
def zero(self, *args):
"""
**Overloaded versions**
* zero\ ()
Set all entries to zero and keep any sparse structure
* zero\ (m, rows)
Set given rows to zero
"""
return _la.uBLASDenseMatrix_zero(self, *args)
def mat(self, *args):
"""
**Overloaded versions**
* mat\ ()
Return reference to uBLAS matrix (const version)
* mat\ ()
Return reference to uBLAS matrix (non-const version)
"""
return _la.uBLASDenseMatrix_mat(self, *args)
def solve(self, *args):
"""
Solve Ax = b out-of-place using uBLAS (A is not destroyed)
"""
return _la.uBLASDenseMatrix_solve(self, *args)
def solve_in_place(self, *args):
"""
**Overloaded versions**
* solve_in_place\ (x, b)
Solve Ax = b in-place using uBLAS(A is destroyed)
* solve_in_place\ (X)
General uBLAS LU solver which accepts both vector and matrix right-hand sides
"""
return _la.uBLASDenseMatrix_solve_in_place(self, *args)
def invert(self, *args):
"""
Compute inverse of matrix
"""
return _la.uBLASDenseMatrix_invert(self, *args)
def lump(self, *args):
"""
Lump matrix into vector m
"""
return _la.uBLASDenseMatrix_lump(self, *args)
def assign(self, *args):
"""
**Overloaded versions**
* operator=\ (A)
Assignment operator
* operator=\ (A)
Assignment operator
"""
return _la.uBLASDenseMatrix_assign(self, *args)
uBLASDenseMatrix.resize = new_instancemethod(_la.uBLASDenseMatrix_resize,None,uBLASDenseMatrix)
uBLASDenseMatrix.zero = new_instancemethod(_la.uBLASDenseMatrix_zero,None,uBLASDenseMatrix)
uBLASDenseMatrix.mat = new_instancemethod(_la.uBLASDenseMatrix_mat,None,uBLASDenseMatrix)
uBLASDenseMatrix.solve = new_instancemethod(_la.uBLASDenseMatrix_solve,None,uBLASDenseMatrix)
uBLASDenseMatrix.solve_in_place = new_instancemethod(_la.uBLASDenseMatrix_solve_in_place,None,uBLASDenseMatrix)
uBLASDenseMatrix.invert = new_instancemethod(_la.uBLASDenseMatrix_invert,None,uBLASDenseMatrix)
uBLASDenseMatrix.lump = new_instancemethod(_la.uBLASDenseMatrix_lump,None,uBLASDenseMatrix)
uBLASDenseMatrix.assign = new_instancemethod(_la.uBLASDenseMatrix_assign,None,uBLASDenseMatrix)
uBLASDenseMatrix_swigregister = _la.uBLASDenseMatrix_swigregister
uBLASDenseMatrix_swigregister(uBLASDenseMatrix)
class uBLASSparseFactory(GenericLinearAlgebraFactory):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_uBLASSparseFactory
def instance(*args):
"""
Return singleton instance
"""
return _la.uBLASSparseFactory_instance(*args)
instance = staticmethod(instance)
uBLASSparseFactory_swigregister = _la.uBLASSparseFactory_swigregister
uBLASSparseFactory_swigregister(uBLASSparseFactory)
def uBLASSparseFactory_instance(*args):
"""
Return singleton instance
"""
return _la.uBLASSparseFactory_instance(*args)
class uBLASDenseFactory(GenericLinearAlgebraFactory):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
__repr__ = _swig_repr
__swig_destroy__ = _la.delete_uBLASDenseFactory
def instance(*args):
"""
Return singleton instance
"""
return _la.uBLASDenseFactory_instance(*args)
instance = staticmethod(instance)
uBLASDenseFactory_swigregister = _la.uBLASDenseFactory_swigregister
uBLASDenseFactory_swigregister(uBLASDenseFactory)
def uBLASDenseFactory_instance(*args):
"""
Return singleton instance
"""
return _la.uBLASDenseFactory_instance(*args)
dolfin_gt = _la.dolfin_gt
dolfin_ge = _la.dolfin_ge
dolfin_lt = _la.dolfin_lt
dolfin_le = _la.dolfin_le
dolfin_eq = _la.dolfin_eq
dolfin_neq = _la.dolfin_neq
def _get_vector_values(*args):
return _la._get_vector_values(*args)
_get_vector_values = _la._get_vector_values
def _contains(*args):
return _la._contains(*args)
_contains = _la._contains
def _compare_vector_with_value(*args):
return _la._compare_vector_with_value(*args)
_compare_vector_with_value = _la._compare_vector_with_value
def _compare_vector_with_vector(*args):
return _la._compare_vector_with_vector(*args)
_compare_vector_with_vector = _la._compare_vector_with_vector
def _get_vector_single_item(*args):
return _la._get_vector_single_item(*args)
_get_vector_single_item = _la._get_vector_single_item
def _get_vector_sub_vector(*args):
return _la._get_vector_sub_vector(*args)
_get_vector_sub_vector = _la._get_vector_sub_vector
def _set_vector_items_vector(*args):
return _la._set_vector_items_vector(*args)
_set_vector_items_vector = _la._set_vector_items_vector
def _set_vector_items_array_of_float(*args):
return _la._set_vector_items_array_of_float(*args)
_set_vector_items_array_of_float = _la._set_vector_items_array_of_float
def _set_vector_items_value(*args):
return _la._set_vector_items_value(*args)
_set_vector_items_value = _la._set_vector_items_value
def _get_matrix_single_item(*args):
return _la._get_matrix_single_item(*args)
_get_matrix_single_item = _la._get_matrix_single_item
def _get_matrix_sub_vector(*args):
return _la._get_matrix_sub_vector(*args)
_get_matrix_sub_vector = _la._get_matrix_sub_vector
def _set_matrix_single_item(*args):
return _la._set_matrix_single_item(*args)
_set_matrix_single_item = _la._set_matrix_single_item
def _set_matrix_items_array_of_float(*args):
return _la._set_matrix_items_array_of_float(*args)
_set_matrix_items_array_of_float = _la._set_matrix_items_array_of_float
def _set_matrix_items_matrix(*args):
return _la._set_matrix_items_matrix(*args)
_set_matrix_items_matrix = _la._set_matrix_items_matrix
def _set_matrix_items_vector(*args):
return _la._set_matrix_items_vector(*args)
_set_matrix_items_vector = _la._set_matrix_items_vector
_has_type_map = {}
_as_backend_type_map = {}
# A map with matrix types as keys and list of possible vector types as values
_matrix_vector_mul_map = {}
def _has_type_uBLASVector(*args):
return _la._has_type_uBLASVector(*args)
_has_type_uBLASVector = _la._has_type_uBLASVector
def _as_backend_type_uBLASVector(*args):
return _la._as_backend_type_uBLASVector(*args)
_as_backend_type_uBLASVector = _la._as_backend_type_uBLASVector
_has_type_map[uBLASVector] = _has_type_uBLASVector
_as_backend_type_map[uBLASVector] = _as_backend_type_uBLASVector
def _has_type_uBLASDenseMatrix(*args):
return _la._has_type_uBLASDenseMatrix(*args)
_has_type_uBLASDenseMatrix = _la._has_type_uBLASDenseMatrix
def _as_backend_type_uBLASDenseMatrix(*args):
return _la._as_backend_type_uBLASDenseMatrix(*args)
_as_backend_type_uBLASDenseMatrix = _la._as_backend_type_uBLASDenseMatrix
def _has_type_uBLASSparseMatrix(*args):
return _la._has_type_uBLASSparseMatrix(*args)
_has_type_uBLASSparseMatrix = _la._has_type_uBLASSparseMatrix
def _as_backend_type_uBLASSparseMatrix(*args):
return _la._as_backend_type_uBLASSparseMatrix(*args)
_as_backend_type_uBLASSparseMatrix = _la._as_backend_type_uBLASSparseMatrix
_has_type_map[uBLASDenseMatrix] = _has_type_uBLASDenseMatrix
_as_backend_type_map[uBLASDenseMatrix] = _as_backend_type_uBLASDenseMatrix
_has_type_map[uBLASSparseMatrix] = _has_type_uBLASSparseMatrix
_as_backend_type_map[uBLASSparseMatrix] = _as_backend_type_uBLASSparseMatrix
_matrix_vector_mul_map[uBLASSparseMatrix] = [uBLASVector]
_matrix_vector_mul_map[uBLASDenseMatrix] = [uBLASVector]
def _has_type_PETScVector(*args):
return _la._has_type_PETScVector(*args)
_has_type_PETScVector = _la._has_type_PETScVector
def _as_backend_type_PETScVector(*args):
return _la._as_backend_type_PETScVector(*args)
_as_backend_type_PETScVector = _la._as_backend_type_PETScVector
_has_type_map[PETScVector] = _has_type_PETScVector
_as_backend_type_map[PETScVector] = _as_backend_type_PETScVector
def _has_type_PETScMatrix(*args):
return _la._has_type_PETScMatrix(*args)
_has_type_PETScMatrix = _la._has_type_PETScMatrix
def _as_backend_type_PETScMatrix(*args):
return _la._as_backend_type_PETScMatrix(*args)
_as_backend_type_PETScMatrix = _la._as_backend_type_PETScMatrix
_has_type_map[PETScMatrix] = _has_type_PETScMatrix
_as_backend_type_map[PETScMatrix] = _as_backend_type_PETScMatrix
_matrix_vector_mul_map[PETScMatrix] = [PETScVector]
def get_tensor_type(tensor):
"Return the concrete subclass of tensor."
for k, v in _has_type_map.items():
if v(tensor):
return k
common.dolfin_error("dolfin/swig/la/post.i",
"extract backend type for %s" % type(tensor).__name__,
"This apparently doesn't work for uBLAS..")
def has_type(tensor, subclass):
"Return wether tensor is of the given subclass."
global _has_type_map
assert _has_type_map
assert subclass in _has_type_map
return bool(_has_type_map[subclass](tensor))
def as_backend_type(tensor, subclass=None):
"Cast tensor to the given subclass, passing the wrong class is an error."
global _as_backend_type_map
assert _as_backend_type_map
if subclass is None:
subclass = get_tensor_type(tensor)
assert subclass in _as_backend_type_map
ret = _as_backend_type_map[subclass](tensor)
# Store the tensor to avoid garbage collection
ret._org_upcasted_tensor = tensor
return ret
class NonlinearProblem(object):
"""
This is a base class for nonlinear problems which can return the
nonlinear function F(u) and its Jacobian J = dF(u)/du.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Constructor
"""
if self.__class__ == NonlinearProblem:
_self = None
else:
_self = self
_la.NonlinearProblem_swiginit(self,_la.new_NonlinearProblem(_self, *args))
__swig_destroy__ = _la.delete_NonlinearProblem
def form(self, *args):
"""
Function called by Newton solver before requesting F or J.
This can be used to compute F and J together
"""
return _la.NonlinearProblem_form(self, *args)
def F(self, *args):
"""
Compute F at current point x
"""
return _la.NonlinearProblem_F(self, *args)
def J(self, *args):
"""
Compute J = F' at current point x
"""
return _la.NonlinearProblem_J(self, *args)
def __disown__(self):
self.this.disown()
_la.disown_NonlinearProblem(self)
return weakref_proxy(self)
NonlinearProblem.form = new_instancemethod(_la.NonlinearProblem_form,None,NonlinearProblem)
NonlinearProblem.F = new_instancemethod(_la.NonlinearProblem_F,None,NonlinearProblem)
NonlinearProblem.J = new_instancemethod(_la.NonlinearProblem_J,None,NonlinearProblem)
NonlinearProblem_swigregister = _la.NonlinearProblem_swigregister
NonlinearProblem_swigregister(NonlinearProblem)
class NewtonSolver(common.Variable):
"""
This class defines a Newton solver for nonlinear systems of
equations of the form :math:`F(x) = 0`.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
**Overloaded versions**
* NewtonSolver\ ()
Create nonlinear solver
* NewtonSolver\ (solver, factory)
Create nonlinear solver using provided linear solver
*Arguments*
solver (:py:class:`GenericLinearSolver`)
The linear solver.
factory (:py:class:`GenericLinearAlgebraFactory`)
The factory.
"""
_la.NewtonSolver_swiginit(self,_la.new_NewtonSolver(*args))
__swig_destroy__ = _la.delete_NewtonSolver
def solve(self, *args):
"""
Solve abstract nonlinear problem :math:`F(x) = 0` for given
:math:`F` and Jacobian :math:`\dfrac{\partial F}{\partial x}`.
*Arguments*
nonlinear_function (:py:class:`NonlinearProblem`)
The nonlinear problem.
x (:py:class:`GenericVector`)
The vector.
*Returns*
(int, bool)
Pair of number of Newton iterations, and whether
iteration converged)
"""
return _la.NewtonSolver_solve(self, *args)
def iteration(self, *args):
"""
Return Newton iteration number
*Returns*
int
The iteration number.
"""
return _la.NewtonSolver_iteration(self, *args)
def residual(self, *args):
"""
Return current residual
*Returns*
float
Current residual.
"""
return _la.NewtonSolver_residual(self, *args)
def relative_residual(self, *args):
"""
Return current relative residual
*Returns*
float
Current relative residual.
"""
return _la.NewtonSolver_relative_residual(self, *args)
def linear_solver(self, *args):
"""
Return the linear solver
*Returns*
:py:class:`GenericLinearSolver`
The linear solver.
"""
return _la.NewtonSolver_linear_solver(self, *args)
def default_parameters(*args):
"""
Default parameter values
*Returns*
:py:class:`Parameters`
Parameter values.
"""
return _la.NewtonSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
NewtonSolver.solve = new_instancemethod(_la.NewtonSolver_solve,None,NewtonSolver)
NewtonSolver.iteration = new_instancemethod(_la.NewtonSolver_iteration,None,NewtonSolver)
NewtonSolver.residual = new_instancemethod(_la.NewtonSolver_residual,None,NewtonSolver)
NewtonSolver.relative_residual = new_instancemethod(_la.NewtonSolver_relative_residual,None,NewtonSolver)
NewtonSolver.linear_solver = new_instancemethod(_la.NewtonSolver_linear_solver,None,NewtonSolver)
NewtonSolver_swigregister = _la.NewtonSolver_swigregister
NewtonSolver_swigregister(NewtonSolver)
def NewtonSolver_default_parameters(*args):
"""
Default parameter values
*Returns*
:py:class:`Parameters`
Parameter values.
"""
return _la.NewtonSolver_default_parameters(*args)
class PETScSNESSolver(PETScObject):
"""
This class implements methods for solving nonlinear systems
via PETSc's SNES interface. It includes line search and trust
region techniques for globalising the convergence of the
nonlinear iteration.
"""
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def __init__(self, *args):
"""
Create SNES solver for a particular method
"""
_la.PETScSNESSolver_swiginit(self,_la.new_PETScSNESSolver(*args))
__swig_destroy__ = _la.delete_PETScSNESSolver
def solve(self, *args):
"""
**Overloaded versions**
* solve\ (nonlinear_problem, x, lb, ub)
Solve a nonlinear variational inequality with bound constraints
*Arguments*
nonlinear_function (:py:class:`NonlinearProblem`)
The nonlinear problem.
x (:py:class:`GenericVector`)
The vector.
lb (:py:class:`GenericVector`)
The lower bound.
ub (:py:class:`GenericVector`)
The upper bound.
*Returns*
(int, bool)
Pair of number of Newton iterations, and whether
iteration converged)
* solve\ (nonlinear_function, x)
Solve abstract nonlinear problem :math:`F(x) = 0` for given
:math:`F` and Jacobian :math:`\dfrac{\partial F}{\partial x}`.
*Arguments*
nonlinear_function (:py:class:`NonlinearProblem`)
The nonlinear problem.
x (:py:class:`GenericVector`)
The vector.
*Returns*
(int, bool)
Pair of number of Newton iterations, and whether
iteration converged)
"""
return _la.PETScSNESSolver_solve(self, *args)
def methods(*args):
"""
Return a list of available solver methods
"""
return _la.PETScSNESSolver_methods(*args)
methods = staticmethod(methods)
def default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScSNESSolver_default_parameters(*args)
default_parameters = staticmethod(default_parameters)
parameters = _swig_property(_la.PETScSNESSolver_parameters_get, _la.PETScSNESSolver_parameters_set)
def snes(self):
common.dolfin_error("dolfin/swig/la/post.i",
"access PETScSNESSolver objects in python",
"dolfin must be configured with petsc4py enabled")
return None
PETScSNESSolver.solve = new_instancemethod(_la.PETScSNESSolver_solve,None,PETScSNESSolver)
PETScSNESSolver_swigregister = _la.PETScSNESSolver_swigregister
PETScSNESSolver_swigregister(PETScSNESSolver)
def PETScSNESSolver_methods(*args):
"""
Return a list of available solver methods
"""
return _la.PETScSNESSolver_methods(*args)
def PETScSNESSolver_default_parameters(*args):
"""
Default parameter values
"""
return _la.PETScSNESSolver_default_parameters(*args)
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