Resampling

class odl.discr.discr_ops.Resampling(*args, **kwargs)

Bases: odl.operator.operator.Operator

An operator that resamples on a different grid in the same set.

Attributes

adjoint

Return an (approximate) adjoint.

domain

Set of objects on which this operator can be evaluated.

interp

Interpolation scheme or tuple of per-axis interpolation schemes.

interp_byaxis

Tuple of per-axis interpolation schemes.

inverse

An (approximate) inverse of this resampling operator.

is_functional

True if this operator’s range is a Field.

is_linear

True if this operator is linear.

range

Set in which the result of an evaluation of this operator lies.

Methods

_call(self, x[, out])	Apply resampling operator.
derivative(self, point)	Return the operator derivative at point.
norm(self[, estimate])	Return the operator norm of this operator.

__init__(self, domain, range, interp)

Initialize a new instance.

Parameters

domainDiscretizedSpace

Set of elements that are to be resampled.

rangeDiscretizedSpace

Set in which the resampled elements lie. Must have the same DiscretizedSpace.domain as domain.

interpstr or sequence of str

Interpolation type that should be used to resample. A single value applies to all axes, and a sequence gives the interpolation scheme per axis.

Supported values: 'nearest', 'linear'

Examples

Create two spaces with different number of points and a resampling operator using nearest-neighbor interpolation:

>>> coarse_discr = odl.uniform_discr(0, 1, 3)
>>> fine_discr = odl.uniform_discr(0, 1, 6)
>>> resampling = odl.Resampling(coarse_discr, fine_discr, 'nearest')
>>> resampling.domain
uniform_discr(0.0, 1.0, 3)
>>> resampling.range
uniform_discr(0.0, 1.0, 6)
>>> resampling.interp
'nearest'

Apply the corresponding resampling operator to an element:

>>> print(resampling([0, 1, 0]))
[ 0.,  0.,  1.,  1.,  0.,  0.]

With linear interpolation:

>>> resampling = odl.Resampling(coarse_discr, fine_discr, 'linear')
>>> print(resampling([0, 1, 0]))
[ 0.  ,  0.25,  0.75,  0.75,  0.25,  0.  ]