pymor.vectorarrays.list

Module Contents

class pymor.vectorarrays.list.ComplexifiedListVectorSpace

Bases: ListVectorSpace

VectorSpace of ListVectorArrays.

Methods

full_vector
make_vector
random_vector
real_full_vector
real_make_vector
real_random_vector
real_vector_from_numpy
real_zero_vector
vector_from_numpy
zero_vector

real_vector_type

vector_type

full_vector(value)

make_vector(obj)

random_vector(distribution, **kwargs)

real_full_vector(value)

abstract real_make_vector(obj)

real_random_vector(distribution, **kwargs)

abstract real_vector_from_numpy(data, ensure_copy=False)

abstract real_zero_vector()

vector_from_numpy(data, ensure_copy=False)

zero_vector()

class pymor.vectorarrays.list.ComplexifiedVector(real_part, imag_part)

Bases: Vector

Interface for vectors used in conjunction with ListVectorArray.

This interface must be satisfied by the individual entries of the vector list managed by ListVectorArray. All interface methods have a direct counterpart in the VectorArray interface.

Methods

amax
axpy
conj
copy
dofs
inner
norm
norm2
scal
sup_norm
to_numpy

amax()

axpy(alpha, x)

conj()

copy(deep=False)

dofs(dof_indices)

inner(other)

norm()

norm2()

scal(alpha)

sup_norm()

to_numpy(ensure_copy=False)

class pymor.vectorarrays.list.CopyOnWriteVector

Bases: Vector

Interface for vectors used in conjunction with ListVectorArray.

This interface must be satisfied by the individual entries of the vector list managed by ListVectorArray. All interface methods have a direct counterpart in the VectorArray interface.

Methods

axpy
copy
from_instance
scal

axpy(alpha, x)

copy(deep=False)

classmethod from_instance(instance)

scal(alpha)

class pymor.vectorarrays.list.ListVectorArray(space, impl, base=None, ind=None, _len=None)

Bases: pymor.vectorarrays.interface.VectorArray

VectorArray implemented as a Python list of vectors.

This VectorArray implementation is the first choice when creating pyMOR wrappers for external solvers which are based on single vector objects. In order to do so, a wrapping subclass of Vector has to be provided on which the implementation of ListVectorArray will operate. The associated VectorSpace is a subclass of ListVectorSpace.

For an example, see NumpyVector and NumpyListVectorSpace, FenicsVector and FenicsVectorSpace, NGSolveVector and NGSolveVectorSpace.

impl_type

class pymor.vectorarrays.list.ListVectorArrayImpl(vectors, space)

Bases: pymor.vectorarrays.interface.VectorArrayImpl

Implementation of a VectorArray.

The VectorArray base class defers all calls to interface methods to an internal impl object of this type. Indexing, error checking or non-standard inner products are handled by VectorArray. Possible indices are passed to the methods of VectorArrayImpl as ind, oind or xind parameters. These can either be None, in case the array has not been indexed, a slice object, or a Python list of non-negative numbers.

Methods

amax
append
axpy
conj
copy
delete
dofs
gramian
imag
inner
lincomb
norm
norm2
pairwise_inner
real
scal
to_numpy

amax(ind)

append(other, remove_from_other, oind)

axpy(alpha, x, ind, xind)

conj(ind)

copy(deep, ind)

delete(ind)

dofs(dof_indices, ind)

gramian(ind)

imag(ind)

inner(other, ind, oind)

lincomb(coefficients, ind)

norm(ind)

norm2(ind)

pairwise_inner(other, ind, oind)

real(ind)

scal(alpha, ind)

to_numpy(ensure_copy, ind)

class pymor.vectorarrays.list.ListVectorSpace

Bases: pymor.vectorarrays.interface.VectorSpace

VectorSpace of ListVectorArrays.

Methods

from_numpy	noindex:
full	Create a VectorArray of vectors with all DOFs set to the same value.
full_vector
make_array	noindex:
make_vector
ones	Create a VectorArray of vectors with all DOFs set to one.
ones_vector
random	Create a VectorArray of vectors with random entries.
random_vector
space_from_dim
space_from_vector_obj
vector_from_numpy
zero_vector
zeros	Create a VectorArray of null vectors.

dim = None

vector_type

from_numpy(data, ensure_copy=False)

Create a VectorArray from a NumPy array.

Note that this method will not be supported by all vector space implementations.

Parameters:

• data – NumPy array of shape (dim, len) where len is the number of vectors and dim their dimension.

• ensure_copy – If False, modifying the returned VectorArray might alter the original NumPy array. If True always a copy of the array data is made.

Returns:

A VectorArray with data as data.

full(value, count=1, reserve=0)

Create a VectorArray of vectors with all DOFs set to the same value.

Parameters:

• value – The value each DOF should be set to.

• count – The number of vectors.

• reserve – Hint for the backend to which length the array will grow.

Returns:

A VectorArray containing count vectors with each DOF set to value.

full_vector(value)

make_array(obj)

Create a VectorArray from raw data.

This method is used in the implementation of Operators and Models to create new VectorArrays from raw data of the underlying solver backends. The ownership of the data is transferred to the newly created array.

The exact signature of this method depends on the wrapped solver backend.

abstract make_vector(obj)

ones(count=1, reserve=0)

Create a VectorArray of vectors with all DOFs set to one.

This is a shorthand for self.full(1., count, reserve).

Parameters:

• count – The number of vectors.

• reserve – Hint for the backend to which length the array will grow.

Returns:

A VectorArray containing count vectors with each DOF set to one.

ones_vector()

random(count=1, distribution='uniform', reserve=0, **kwargs)

Create a VectorArray of vectors with random entries.

Supported random distributions:

'uniform': Uniform distribution in half-open interval
           [`low`, `high`).
'normal':  Normal (Gaussian) distribution with mean
           `loc` and standard deviation `scale`.

Note that not all random distributions are necessarily implemented by all VectorSpace implementations.

Parameters:

• count – The number of vectors.

• distribution – Random distribution to use ('uniform', 'normal').

• low – Lower bound for 'uniform' distribution (defaults to 0).

• high – Upper bound for 'uniform' distribution (defaults to 1).

• loc – Mean for 'normal' distribution (defaults to 0).

• scale – Standard deviation for 'normal' distribution (defaults to 1).

• reserve – Hint for the backend to which length the array will grow.

random_vector(distribution, **kwargs)

classmethod space_from_dim(dim)

Abstractmethod:

classmethod space_from_vector_obj(vec)

Abstractmethod:

abstract vector_from_numpy(data, ensure_copy=False)

abstract zero_vector()

zeros(count=1, reserve=0)

Create a VectorArray of null vectors.

Parameters:

• count – The number of vectors.

• reserve – Hint for the backend to which length the array will grow.

Returns:

A VectorArray containing count vectors with each component zero.

class pymor.vectorarrays.list.NumpyListVectorSpace(dim)

Bases: ListVectorSpace

VectorSpace of ListVectorArrays.

Methods

full_vector
make_vector
ones_vector
space_from_dim
space_from_vector_obj
vector_from_numpy
zero_vector

vector_type

full_vector(value)

make_vector(obj)

ones_vector()

classmethod space_from_dim(dim)

classmethod space_from_vector_obj(vec)

vector_from_numpy(data, ensure_copy=False)

zero_vector()

class pymor.vectorarrays.list.NumpyVector(array)

Bases: CopyOnWriteVector

Vector stored in a NumPy 1D-array.

Methods

amax
conj
dofs
from_instance
inner
norm
norm2
to_numpy

amax()

conj()

dofs(dof_indices)

classmethod from_instance(instance)

inner(other)

norm()

norm2()

to_numpy(ensure_copy=False)

class pymor.vectorarrays.list.Vector

Bases: pymor.core.base.BasicObject

Interface for vectors used in conjunction with ListVectorArray.

This interface must be satisfied by the individual entries of the vector list managed by ListVectorArray. All interface methods have a direct counterpart in the VectorArray interface.

Methods

amax
axpy
conj
copy
dofs
inner
norm
norm2
scal
sup_norm

abstract amax()

abstract axpy(alpha, x)

conj()

abstract copy(deep=False)

abstract dofs(dof_indices)

abstract inner(other)

abstract norm()

abstract norm2()

abstract scal(alpha)

sup_norm()