Release Notes¶

Upcoming release¶

ODL 0.6.0 Release Notes (2017-04-20)¶

Besides many small improvements and additions, this release is the first one under the new Mozilla Public License 2.0 (MPL-2.0).

New features¶

The Kaczmarz method has been added to the solvers (PR 840).
Most immutable types now have a __hash__ method (PR 840).
A variant of the Conjugate Gradient solver for non-linear problems has been added (PR 554).
There is now an example for tomographic reconstruction using Total Generalized Variation (TGV). (PR 883).
Power spaces can now be created using the ** operator, e.g., odl.rn(3) ** 4. Likewise, product spaces can be created using multiplication *, i.e., odl.rn(3) * odl.rn(4) (PR 882).
A SamplingOperator for the extraction of values at given indices from arrays has been added, along with its adjoint WeightedSumSamplingOperator (PR 940).
Callbacks can now be composed with operators, which can be useful, e.g., for transforming the current iterate before displaying it (PR 954).
RayTransform (and thus also fbp_op) can now be directly used on spaces of complex functions (PR 970).

Improvements¶

In CallbackPrintIteration, a step number between displays can now be specified (PR 871).
OperatorPointwiseProduct got its missing derivative (PR 877).
SeparableSum functionals can now be indexed to retrieve the constituents (PR 898).
Better self-printing of callbacks (PR 881).
ProductSpaceOperator and subclasses now have size and __len__, and the parent also has shape. Also self-printing of these operators is now better (PR 901).
Arithmetic methods of LinearSpace have become more permissive in the sense that operations like space_element + raw_array now works if the array can be cast to an element of the same space (PR 902).
There is now a (work-in-progress) document on the release process with the aim to avoid errors (PR 872).
The MRC extended header implementation is now much simpler (PR 917).
The show_discrete_data workhorse is now more robust towards arrays with inf and nan entries regarding colorbar settings (PR 921).
The title in CallbackShow are now interpreted as format string with iteration number inserted, which enables updating the figure title in real time (PR 923).
Installation instructions have been arranged in a better way, grouped after different ways of installing (PR 884).
A performance comparison example pure ASTRA vs. ODL with ASTRA for 3d cone beam has been added (PR 912).
OperatorComp avoids an operator evaluation in derivative in the case when the left operator is linear (PR 957).
FunctionalComp now has a default implementation of gradient.derivative if the operator in the composition is linear (PR 956).
The saveto parameter of CallbackShow can now be a callable that returns the file name to save to when called on the current iteration number (PR 955).

Changes¶

The sphinxext submodule has been from upstream (PR 846).
The renames TensorGrid -> RectGrid and uniform_sampling -> uniform_grid have been made, and separate class RegularGrid has been removed in favor of treating regular grids as a special case of RectGrid. Instances of RectGrid have a new property is_uniform for this purpose. Furthermore, uniformity of RectPartition and RectGrid is exposed as property per axis using is_uniform_byaxis (PR 841).
extent of grids and partitions is now a property instead of a method (PR 889).
The number of iterations in solvers is no longer optional since the old default 1 didn’t make much sense (PR 888).
The nlevels argument of WaveletTransform is now optional, and the default is the maximum number of levels as determined by the new function pywt_max_nlevels (PR 880).
MatVecOperator is now called MatrixOperator and has been moved to the tensor_ops module. This solves a circular dependency issue with ODL subpackages (PR 911).
All step parameters of callbacks are now called just step (PR 929).
The impl name for the scikit-image back-end in RayTransform has been changed from scikit to skimage (PR 970).
ODL is now licensed under the Mozilla Public License 2.0 (PR 977).

Bugfixes¶

Fix an argument order error in the gradient of QuadraticForm (PR 868).
Lots of small documentation fixes where ”, optional” was forgotten in the Parameters section (PR 554).
Fix an indexing bug in the indicate_proj_axis phantom (PR 878).
Fix wrong inheritance order in FileReaderRawBinaryWithHeader that lead to wrong header_size (PR 893).
Comparison of arbitrary objects in Python 2 is now disabled for a some ODL classes where it doesn’t make sense (PR 933).
Fix a bug in the angle calculation of the scikit-image back-end for Ray transforms (PR 947).
Fix issue with wrong integer type in as_scipy_operator (PR 960).
Fix wrong scaling in RayTransform and adjoint with unweighted spaces (PR 958).
Fix normalization bug of min_pt and max_pt parameters in RectPartition (PR 971).
Fix an issue with *args in CallbackShow that lead to the title argument provided twice (PR 981).
Fix an unconditional pytest import that lead to an ImportError if pytest was not installed (PR 982).

ODL 0.5.3 Release Notes (2017-01-17)¶

Lots of small improvements and feature additions in this release. Most notable are the remarkable performance improvements to the ASTRA bindings (up to 10x), the addition of fbp_op to create filtered back-projection operators with several filter and windowing options, as well as further performance improvements to operator compositions and the show methods.

New features¶

Add the SeparableSum(func, n) syntax for n-times repetition of the same summand (PR 685).
Add the Ordered Subsets MLEM solver odl.solvers.osmlem for faster EM reconstruction (PR 647).
Add GroupL1Norm and IndicatorGroupL1UnitBall for mixed L1-Lp norm regularization (PR 620).
Add fbp_op helper to create filtered back-projection operators for a range of geometries (PR 703).
Add 2-dimensional FORBILD phantom (PR 694, PR 804, PR 820).
Add IndicatorZero functional in favor of of ConstantFunctionalConvexConj (PR 707).
Add reader for MRC data files and for custom binary formats with fixed header (PR 716).
Add NuclearNorm functional for multi-channel regularization (PR 691).
Add CallbackPrint for printing of intermediate results in iterative solvers (PR 691).
Expose Numpy ufuncs as operators in the new ufunc_ops subpackage (PR 576).
Add ScalingFunctional and IdentityFunctional (PR 576).
Add RealPart, ImagPart and ComplexEmbedding operators (PR 706).
Add PointwiseSum operator for vector fields (PR 754).
Add LineSearchFromIterNum for using a pre-defined mapping from iteration number to step size (PR 752).
Add axis_labels option to DiscreteLp for custom labels in plots (PR 770).
Add Defrise phantom for cone beam geometry testing (PR 756).
Add filter option to fbp_op and tam_danielson_window and parker_weighting helpers for helical/cone geometries (PR 756, PR 806, PR 825).
Add ISTA (proximal_gradient) and FISTA (accelerated_proximal_gradient) algorithms, among others useful for L1 regularization (PR 758).
Add salt_pepper_noise helper function (PR 758).
Expose FBP filtering as operator fbp_filter_op (PR 780).
Add parallel_beam_geometry helper for creation of simple test geometries (PR 775).
Add MoreauEnvelope functional for smoothed regularization (PR 763).
Add saveto option to CallbackShow to store plots of iterates (PR 708).
Add CallbackSaveToDisk and CallbackSleep (PR 798).
Add a utility signature_string for robust generation of strings for repr or str (PR 808).

Improvements¶

New documentation on the operator derivative notion in ODL (PR 668).
Add largescale tests for the convex conjugates of functionals (PR 744).
Add domain parameter to LinDeformFixedTempl for better extensibility (PR 748).
Add example for sparse tomography with TV regularization using the Douglas-Rachford solver (PR 746).
Add support for 1/r^2 scaling in cone beam backprojection with ASTRA 1.8 using a helper function for rescaling (PR 749).
Improve performance of operator scaling in certain cases (PR 576).
Add documentation on testing in ODL (PR 704).
Replace occurrences of numpy.matrix objects (PR 778).
Implement Numpy-style indexing for ProductSpaceElement objects (PR 774).
Greatly improve efficiency of show by updating the figure in place instead of re-creating (PR 789).
Improve efficiency of operator derivatives by short-circuiting in case of a linear operator (PR 796).
Implement simple indexing for ProducSpaceOperator (PR 815).
Add caching to ASTRA projectors, thus making algorithms run much faster (PR 802).

Changes¶

Rename vector_field_space to tangent_bundle in vector spaces (more adequate for complex spaces) (PR 702).
Rename show parameter of show methods to force_show (PR 771).
Rename elem.ufunc to elem.ufuncs where implemented (PR 809).
Remove “Base” from weighting base classes and rename weight parameter to weighting for consistency (PR 810).
Move tensor_ops module from odl.discr to odl.operator for more general application (PR 813).
Rename ellipse to ellipsoid in names intended for 3D cases (PR 816).
Pick the fastest available implementation in RayTransform by default instead of astra_cpu (PR 826).

Bugfixes¶

Prevent ASTRA cubic voxel check from failing due to numerical rounding errors (PR 721).
Implement the missing __ne__ in RectPartition (PR 748).
Correct adjoint of WaveletTransform (PR 758).
Fix issue with creation of phantoms in a space with degenerate shape (PR 777).
Fix issue with Windows paths in collect_ignore.
Fix bad dict lookup with RayTransform.adjoint.adjoint.
Fix rounding issue in a couple of indicator functionals.
Several bugfixes in show methods.
Fixes to outdated example code.

ODL 0.5.2 Release Notes (2016-11-02)¶

Another maintenance release that fixes a number of issues with installation and testing, see issue 674, issue 679, and PR 692 and PR 696.

ODL 0.5.1 Release Notes (2016-10-24)¶

This is a maintenance release since the test suite was not bundled with PyPI and Conda packages as intended already in 0.5.0. From this version on, users can run python -c "import odl; odl.test()" with all types of installations (from PyPI, Conda or from source).

ODL 0.5.0 Release Notes (2016-10-21)¶

This release features a new important top level class Functional that is intended to be used in optimization methods. Beyond its parent Operator, it provides special methods and properties like gradient or proximal which are useful in advanced smooth or non-smooth optimization schemes. The interfaces of all solvers in odl.solvers have been updated to make use of functionals instead of their proximals, gradients etc. directly.

Further notable changes are the implementation of an as_writable_array context manager that exposes arbitrary array storage as writable Numpy arrays, and the generalization of the wavelet transform to arbitrary dimensions.

See below for a complete list of changes.

New features¶

Add Functional class to the solvers package. (PR 498) Functional is a subclass of odl Operator and intended to help in formulating and solving optimization problems. It contains optimization specific features like proximal and convex_conj, and built-in intelligence for handling things like translation, scaling of argument or scaling of functional. * Migrate all solvers to work with Functional‘s instead of raw proximals etc. (PR 587) * FunctionalProduct and FunctionalQuotient which allow evaluation of the product/quotient of functions and also provides a gradient through the Leibniz/quotient rules. (PR 586) * FunctionalDefaultConvexConjugate which acts as a default for Functional.convex_conj, providing it with a proximal property. (PR 588) * IndicatorBox and IndicatorNonnegativity which are indicator functions on a box shaped set and the set of nonnegative numbers, respectively. They return 0 if all points in a vector are inside the box, and infinity otherwise. (PR 589) * Add Functional``s for ``KullbackLeibler and KullbackLeiblerCrossEntropy, together with corresponding convex conjugates (PR 627). Also add proximal operator for the convex conjugate of cross entropy Kullback-Leibler divergence, called proximal_cconj_kl_cross_entropy (PR 561)
Add ResizingOperator for shrinking and extending (padding) of discretized functions, including a variety of padding methods. (PR 499)
Add as_writable_array that allows casting arbitrary array-likes to a numpy array and then storing the results later on. This is intended to be used with odl vectors that may not be stored in numpy format (like cuda vectors), but can be used with other types like lists. (PR 524)
Allow ASTRA backend to be used with arbitrary dtypes. (PR 524)
Add reset to SolverCallback that resets the callback to its initial state. (issue 552)
Add nonuniform_partition utility that creates a partition with non-uniformly spaced points. This is useful e.g. when the angles of a tomography problem are not exactly uniform. (PR 558)
Add Functional class to the solvers package. Functional is a subclass of odl Operator and intended to help in formulating and solving optimization problems. It contains optimization specific features like proximal and convex_conj, and built-in intelligence for handling things like translation, scaling of argument or scaling of functional. (PR 498)
Add FunctionalProduct and FunctionalQuotient which allow evaluation of the product/quotient of functions and also provides a gradient through the Leibniz/quotient rules. (PR 586)
Add FunctionalDefaultConvexConjugate which acts as a default for Functional.convex_conj, providing it with a proximal property. (PR 588)
Add IndicatorBox and IndicatorNonnegativity which are indicator functions on a box shaped set and the set of nonnegative numbers, respectively. They return 0 if all points in a vector are inside the box, and infinity otherwise. (PR 589)
Add proximal operator for the convex conjugate of cross entropy Kullback-Leibler divergence, called proximal_cconj_kl_cross_entropy (PR 561)
Add Functional‘s for KullbackLeibler and KullbackLeiblerCrossEntropy, together with corresponding convex conjugates (PR 627)
Add tutorial style example. (PR 521)
Add MLEM solver. (PR 497)
Add MatVecOperator.inverse. (PR 608)
Add the Rosenbrock standard test functional. (PR 602)
Add broadcasting of vector arithmetic involving ProductSpace vectors. (PR 555)
Add phantoms.poisson_noise. (PR 630)
Add NumericalGradient and NumericalDerivative that numerically compute gradient and derivative of Operator‘s and Functional‘s. (PR 624)

Improvements¶

Add intelligence to power_method_opnorm so it can terminate early by checking if consecutive iterates are close. (PR 527)
Add BroadcastOperator(op, n), ReductionOperator(op, n) and DiagonalOperator(op, n) syntax. This is equivalent to BroadcastOperator(*([op] * n)) etc, i.e. create n copies of the operator. (PR 532)
Allow showing subsets of the whole volume in DiscreteLpElement.show. Previously this allowed slices to be shown, but the new version allows subsets such as 0 < x < 3 to be shown as well. (PR 574)
Add Solvercallback.reset() which allows users to reset a callback to its initial state. Applicable if users want to reuse a callback in another solver. (PR 553)
WaveletTransform and related operators now work in arbitrary dimensions. (PR 547)
Several documentation improvements. Including:
- Move documentation from _call to __init__. (PR 549)
- Major review of minor style issues. (PR 534)
- Typeset math in proximals. (PR 580)
Improved installation docs and update of Chambolle-Pock documentation. (PR 121)

Changes¶

Change definition of LinearSpaceVector.multiply to match the definition used by Numpy. (PR 509)
Rename the parameters padding_method in diff_ops.py and mode in wavelet.py to pad_mode. The parameter padding_value is now called pad_const. (PR 511)
Expose ellipse_phantom and shepp_logan_ellipses to odl.phantom. (PR 529)
Unify the names of minimum (min_pt), maximum (max_pt) and middle (mid_pt) points as well as number of points (shape) in grids, interval products and factory functions for discretized spaces. (PR 541)
Remove simple_operator since it was never used and did not follow the ODL style. (PR 543) The parameter padding_value is now called pad_const.
Remove Interval, Rectangle and Cuboid since they were confusing (Capitalized name but not a class) and barely ever used. Users should instead use IntervalProd in all cases. (PR 537)
The following classes have been renamed (PR 560):
- LinearSpaceVector -> LinearSpaceElement
- DiscreteLpVector -> DiscreteLpElement
- ProductSpaceVector -> ProductSpaceElement
- DiscretizedSetVector -> DiscretizedSetElement
- DiscretizedSpaceVector -> DiscretizedSpaceElement
- FunctionSetVector -> FunctionSetElement
- FunctionSpaceVector -> FunctionSpaceElement
Change parameter style of differential operators from having a pad_mode and a separate edge_order argument that were mutually exclusive to a single pad_mode that covers all cases. Also added several new pad modes to the differential operators. (PR 548)
Switch from RTD documentation hosting to gh-pages and let Travis CI build and deploy the documentation. (PR 536)
Update name of proximal_zero to proximal_const_func. (PR 582)
Move unit tests from top level test/ to odl/test/ folder and distribute them with the source. (PR 638)
Update pytest dependency to [>3.0] and use new featuers. (PR 653)
Add pytest option --documentation to test all doctest examples in the online documentation.
Remove the pip install odl[all] option since it fails by default.

Bugfixes¶

Fix python -c "import odl; odl.test()" not working on Windows. (PR 508)
Fix a TypeError being raised in OperatorTest when running optest.ajoint() without specifying an operator norm. (PR 525)
Fix scaling of scikit ray transform for non full scan. (PR 523)
Fix bug causing classes to not be vectorizable. (PR 604)
Fix rounding problem in some proximals (PR 661)

ODL 0.4.0 Release Notes (2016-08-17)¶

This release marks the addition of the deform package to ODL, adding functionality for the deformation of DiscreteLp elements.

New features¶

Add deform package with linearized deformations (PR 488)
Add option to interface with ProxImaL solvers using ODL operators. (PR 494)

ODL 0.3.1 Release Notes (2016-08-15)¶

This release mainly fixes an issue that made it impossible to pip install odl with version 0.3.0. It also adds the first really advanced solvers based on forward-backward and Douglas-Rachford splitting.

New features¶

New solvers based on the Douglas-Rachford and forward-backward splitting schemes. (PR 478, PR 480)
NormOperator and DistOperator added. (PR 487)
Single-element NtuplesBase vectors can now be converted to float, complex etc. (PR 493)

Improvements¶

DiscreteLp.element() now allows non-vectorized and 1D scalar functions as input. (PR 476)
Speed improvements in the unit tests. (PR 479)
Uniformization of __init__() docstrings and many further documentation and naming improvements. (PR 489, PR 482, PR 491)
Clearer separation between attributes that are intended as part of the subclassing API and those that are not. (PR 471)
Chambolle-Pock solver accepts also non-linear operators and has better documentation now. (PR 490)
Clean-up of imports. (PR 492)
All solvers now check that the given start value x is in op.domain. (PR 502)
Added test for in-place evaluation of the ray transform. (PR 500)

Bugfixes¶

Axes in show() methods of several classes now use the correct corner coordinates, the old ones were off by half a grid cell in some situations. (PR 477).
Catch case in power_method_opnorm() when iteration goes to zero. (PR 495)

ODL 0.3.0 Release Notes (2016-06-29)¶

This release marks the removal of odlpp from the core library. It has instead been moved to a separate library, odlcuda.

New features¶

To enable cuda backends for the odl spaces, an entry point 'odl.space' has been added where external libraries can hook in to add FnBase and NtuplesBase type spaces.
Add pytest fixtures 'fn_impl' and 'ntuple_impl' to the test config conf.py. These can now be accessed from any test.
Allow creation of general spaces using the fn, cn and rn factories. These functions now take an impl parameter which defaults to 'numpy' but with odlcuda installed it may also be set to 'cuda'. The old numpy specific Fn, Cn and Rn functions have been removed.

Changes¶

Moved all CUDA specfic code out of the library into odlcuda. This means that cu_ntuples.py and related files have been removed.
Rename ntuples.py to npy_ntuples.py.
Added Numpy to the numy based spaces. They are now named NumpyFn and NumpyNtuples.
Prepended npy_ to all methods specific to ntuples such as weightings.

ODL 0.2.4 Release Notes (2016-06-28)¶

New features¶

Add uniform_discr_fromdiscr (PR 467).
Add conda build files (commit 86ff166).

Bugfixes¶

Fix bug in submarine phantom with non-centered space (PR 469).
Fix crash when plotting in 1d (commit 3255fa3).

Changes¶

Move phantoms to new module odl.phantom (PR 469).
Rename RectPartition.is_uniform to RectPartition.is_uniform (PR 468).

ODL 0.2.3 Release Notes (2016-06-12)¶

New features¶

uniform_sampling now supports the nodes_on_bdry option introduced in RectPartition (PR 308).
DiscreteLpVector.show has a new coords option that allows to slice by coordinate instead of by index (PR 309).
New uniform_discr_fromintv to discretize an existing IntervalProd instance (PR 318).
The operator.oputils module has a new function as_scipy_operator which exposes a linear ODL operator as a scipy.sparse.linalg.LinearOperator. This way, an ODL operator can be used seamlessly in SciPy’s sparse solvers (PR 324).
New Resampling operator to resample data between different discretizations (PR 328).
New PowerOperator taking the power of an input function (PR 338).
First pointwise operators acting on vector fields: PointwiseInner and PointwiseNorm (PR 346).
Examples for FBP reconstruction (PR 364) and TV regularization using the Chambolle-Pock method (PR 352).
New scikit-image based implementation of RayTransform for 2D parallel beam tomography (PR 352).
RectPartition has a new method append for simple extension (PR 370).
The ODL unit tests can now be run with odl.test() (PR 373).
Proximal of the Kullback-Leibler data discrepancy functional (PR 289).
Support for SPECT using ParallelHoleCollimatorGeometry (PR 304).
A range of new proximal operators (PR 401) and some calculus rules (PR 422) have been added, e.g. the proximal of the convex conjugate or of a translated functional.
Functions with parameters can now be sampled by passing the parameter values to the sampling operator. The same is true for the element method of a discrete function space (PR 406).
ProducSpaceOperator can now be indexed directly, returning the operator component(s) corresponding to the index (PR 407).
RectPartition now supports “almost-fancy” indexing, i.e. indexing via integer, slice, tuple or list in the style of NumPy (PR 386).
When evaluating a FunctionSetVector, the result is tried to be broadcast if necessary (PR 438).
uniform_partition now has a more flexible way of initialization using begin, end, num_nodes and cell_sides (3 of 4 required) (PR 444).

Improvements¶

Product spaces now utilize the same weighting class hierarchy as Rn type spaces, which makes the weight handling much more transparent and robust (PR 320).
Major refactor of the diagnostics module, with better output, improved derivative test and a simpler and more extensible way to generate example vectors in spaces (PR 338).
3D Shepp-Logan phantom sliced in the middle is now exactly the same as the 2D Shepp-Logan phantom (PR 368).
Improved usage of test parametrization, making decoration of each test function obsolete. Also the printed messages are better (PR 371).
OperatorLeftScalarMult and OperatorRightScalarMult now have proper inverses (PR 388).
Better behavior of display methods if arrays contain inf or NaN (PR 376).
Adjoints of Fourier transform operators are now correctly handled (PR 396).
Differential operators now have consistent boundary behavior (PR 405).
Repeated scalar multiplication with an operator accumulates the scalars instead of creating a new operator each time (PR 429).
Examples have undergone a major cleanup (PR 431).
Addition of __len__ at several places where it was missing (PR 425).

Bugfixes¶

The result of the evaluation of a FunctionSpaceVector is now automatically cast to the correct output data type (PR 331).
inf values are now properly treated in BacktrackingLineSearch (PR 348).
Fix for result not being written to a CUDA array in interpolation (PR 361).
Evaluation of FunctionSpaceVector now works properly in the one-dimensional case (PR 362).
Rotation by 90 degrees / wrong orientation of 2D parallel and fan beam projectors and back-projectors fixed (PR 436).

Changes¶

odl.set.pspace was moved to odl.space.pspace (PR 320)
Parameter ord in norms etc. has been renamed to exponent (PR 320)
restriction and extension operators and parameters have been renamed to sampling and interpolation, respectively (PR 337).
Differential operators like Gradient and Laplacian have been moved from odl.discr.discr_ops to odl.discr.diff_ops (PR 377)
The initialization patterns of Gradient and Divergence were unified to allow specification of domain or range or both (PR 377).
RawDiscretization and Discretization were renamed to DiscretizedSet and DiscretizedSpace, resp. (PR 406).
Diagonal “operator matrices” are now implemented with a class DiagonalOperator instead of the factory function diagonal_operator (PR 407).
The ...Partial classes have been renamed to Callback.... Parameters of solvers are now callback instead of partial (PR 430).
Occurrences of dom and ran as initialization parameters of operators have been changed to domain and range throughout (PR 433).
Assignments x = x.space.element(x) are now required to be no-ops (PR 439)

ODL 0.2.2 Release Notes (2016-03-11)¶

From this release on, ODL can be installed through pip directly from the Python package index.

ODL 0.2.1 Release Notes (2016-03-11)¶

Fix for the version number in setup.py.

ODL 0.2 Release Notes (2016-03-11)¶

This release features the Fourier transform as major addition, along with some minor improvements and fixes.

New Features¶

Add FourierTransform and DiscreteFourierTransform, where the latter is the fully discrete version not accounting for shift and scaling, and the former approximates the integral transform by taking shifted and scaled grids into account. (PR 120)
The weighting attribute in FnBase is now public and can be used to initialize a new space.
The FnBase classes now have a default_dtype static method.
A discr_sequence_space has been added as a simple implementation of finite sequences with multi-indexing.
DiscreteLp and FunctionSpace elements now have real and imag with setters as well as a conj() method.
FunctionSpace explicitly handles output data type and allows this attribute to be chosen during initialization.
FunctionSpace, FnBase and DiscreteLp spaces support creation of a copy with different data type via the astype() method.
New conj_exponent() utility to get the conjugate of a given exponent.

Improvements¶

Handle some not-so-unlikely corner cases where vectorized functions don’t behave as they should. In particular, make 1D functions work when expressions like t[t > 0] are used.
x ** 0 evaluates to the one() space element if implemented.

Changes¶

Move fast_1d_tensor_mult to the numerics.py module.

ODL 0.1 Release Notes (2016-03-08)¶

First official release.