pymor.vectorarrays.numpy

Module Contents

class pymor.vectorarrays.numpy.NumpyVectorArray(space, impl, base=None, ind=None, _len=None)

Bases: pymor.vectorarrays.interface.VectorArray

VectorArray implementation via NumPy arrays.

This is the default VectorArray type used by all Operators in pyMOR’s discretization toolkit. Moreover, all reduced Operators are based on NumpyVectorArray.

This class is just a thin wrapper around the underlying NumPy array. Thus, while operations like axpy or inner will be quite efficient, removing or appending vectors will be costly.

Warning

This class is not intended to be instantiated directly. Use the associated VectorSpace instead.

impl_type

class pymor.vectorarrays.numpy.NumpyVectorArrayImpl(array, l=None)

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
axpy_copy
conj
copy
delete
dofs
imag
inner
lincomb
norm
norm2
pairwise_inner
real
scal
scal_copy
to_numpy

amax(ind)

append(other, remove_from_other, oind)

axpy(alpha, x, ind, xind)

axpy_copy(alpha, x, ind, xind)

conj(ind)

copy(deep, ind)

delete(ind)

dofs(dof_indices, ind)

imag(ind)

inner(other, ind, oind)

lincomb(coefficients, ind)

norm(ind)

norm2(ind)

pairwise_inner(other, ind, oind)

real(ind)

scal(alpha, ind)

scal_copy(alpha, ind)

to_numpy(ensure_copy, ind)

class pymor.vectorarrays.numpy.NumpyVectorSpace(dim)

Bases: pymor.vectorarrays.interface.VectorSpace

VectorSpace of NumpyVectorArrays.

Parameters:

dim – The dimension of the vectors contained in the space.

Methods

from_file	noindex:
from_numpy	noindex:
full	Create a VectorArray of vectors with all DOFs set to the same value.
make_array	noindex:
random	Create a VectorArray of vectors with random entries.
zeros	Create a VectorArray of null vectors.

from_file(path, key=None, single_vector=False, transpose=False)

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.

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.

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.

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.