Laplacian¶
- class odl.discr.diff_ops.Laplacian(*args, **kwargs)[source]¶
Bases:
PointwiseTensorFieldOperator
Spatial Laplacian operator for
DiscretizedSpace
spaces.Calls helper function
finite_diff
to calculate each component of the resulting product space vector.Outside the domain zero padding is assumed.
- Attributes:
adjoint
Return the adjoint operator.
base_space
Base space
X
of this operator's domain and range.domain
Set of objects on which this operator can be evaluated.
inverse
Return the operator inverse.
is_functional
True
if this operator's range is aField
.is_linear
True
if this operator is linear.range
Set in which the result of an evaluation of this operator lies.
Methods
__call__
(x[, out])Return
self(x[, out, **kwargs])
.derivative
([point])Return the derivative operator.
norm
([estimate])Return the operator norm of this operator.
- __init__(domain, range=None, pad_mode='constant', pad_const=0)[source]¶
Initialize a new instance.
- Parameters:
- domain
DiscretizedSpace
Space of elements which the operator is acting on.
- pad_modestring, optional
The padding mode to use outside the domain.
'constant'
: Fill withpad_const
.'symmetric'
: Reflect at the boundaries, not doubling the outmost values.'periodic'
: Fill in values from the other side, keeping the order.'order0'
: Extend constantly with the outmost values (ensures continuity).'order1'
: Extend with constant slope (ensures continuity of the first derivative). This requires at least 2 values along each axis.'order2'
: Extend with second order accuracy (ensures continuity of the second derivative). This requires at least 3 values along each axis where padding is applied.- pad_constfloat, optional
For
pad_mode == 'constant'
,f
assumespad_const
for indices outside the domain off
- domain
Examples
>>> data = np.array([[ 0., 0., 0.], ... [ 0., 1., 0.], ... [ 0., 0., 0.]]) >>> space = odl.uniform_discr([0, 0], [3, 3], [3, 3]) >>> f = space.element(data) >>> lap = Laplacian(space) >>> lap(f) uniform_discr([ 0., 0.], [ 3., 3.], (3, 3)).element( [[ 0., 1., 0.], [ 1., -4., 1.], [ 0., 1., 0.]] )