Release Notes

Upcoming release

ODL 0.7.0 Release Notes (2018-09-09)

This release is a big one as it includes the cumulative work over a period of 1 1/2 years. It is planned to be the last release before version 1.0.0 where we expect to land a number of exciting new features.

Highlights

Native multi-indexing of ODL space elements

The DiscreteLpElement and Tensor (renamed from FnBaseVector) data structures now natively support almost all kinds of Numpy “fancy” indexing. Likewise, the spaces DiscreteLp and Tensorspace (renamed from FnBase) have more advanced indexing capabilities as well. Up to few exceptions, elem[indices] in space[indices] is always fulfilled. Alongside, ProductSpace and its elements also gained more advanced indexing capabilities, in particular in the case of power spaces.

Furthermore, integration with Numpy has been further improved with the implementation of the __array_ufunc__ interface. This allows to transparently use ODL objects in calls to Numpy UFuncs, e.g., np.cos(odl_obj, out=odl_obj) or np.add.reduce(odl_in, axis=0, out=odl_out) — both these examples were not possible with the __array__ and __array_wrap__ interfaces.

Unfortunately, this changeset makes the odlcuda plugin unusable since it only supports linear indexing. A much more powerful replacement based on CuPy will be added in version 1.0.0.

Integration with deep learning frameworks

ODL is now integrated with three major deep learning frameworks: TensorFlow, PyTorch and Theano. In particular, ODL Operator and Functional objects can be used as layers in neural networks, with support for automatic differentiation and backpropagation. This makes a lot of (inverse) problems that ODL can handle well, e.g., tomography, accessible to the computation engines of the deep learning field, and opens up a wide range of possibilities to combine the two.

The implementation of this functionality and examples of its usage can be found in the packages tensorflow, torch and theano in the odl.contrib sub-package (see below).

New contrib sub-package

The core ODL library is intended to stay focused on general-purpose classes and data structures, and good code quality is a major goal. This implies that contributions need to undergo scrutiny in a review process, and that some contributions might not be a good fit if they are too specific for certain applications.

For this reason, we have created a new contrib sub-package that is intended for exactly this kind of code. As of writing this, contrib already contains a number of highly useful modules:

  • datasets: Loaders and utility code for publicly available datasets (currently FIPS CT, Mayo clinic human CT, Tu Graz MRI and some image data)

  • fom: Implementations of Figures-of-Merit for image quality assessment

  • mrc: Reader and writer for the MRC 2014 data format in electron microscopy

  • param_opt: Optimization strategies for method hyperparameters

  • pyshearlab: Integration of the pyshearlab Python library for shearlet decomposition and analysis

  • shearlab: Integration of the Shearlab.jl Julia shearlet library

  • solvers: More exotic functionals and optimization methods than in the core ODL library

  • tomo: Vendor- or application-specific geometries (currently Elekta ICON and XIV)

  • tensorflow: Integration of ODL with TensorFlow

  • theano: Integration of ODL with Theano

  • torch: Integration of ODL with

Overhaul of tomographic geometries

The classes for representing tomographic geometries in odl.tomo have undergone a major update, resulting in a consistent definition of coordinate systems across all cases, proper documentation, vectorization and broadcasting semantics in all methods that compute vectors, and significant speed-up of backprojection due to better axis handling. Additionally, factory functions cone_beam_geometry and helical_geometry have been added as a simpler and more accessible way to create cone beam geometries.

New features

  • Function pkg_supports for tracking package features (PR 976).

  • Class CallbackShowConvergence for tracking values of functionals in a plot (PR 832).

  • Context manager NumpyRandomSeed for setting and resetting the random seed, to get reproducible randomness (PR 1003).

  • Parameter seed in noise phantoms for reproducible results (PR 1003).

  • Function as_scipy_functional that allows using Functional instances and their gradients in SciPy’s optimization methods (PR 1004).

  • New text phantom to create images from arbitrary text (PR 1009, PR 1072).

  • Class CallbackPrintHardwareUsage for monitoring of OS resources during an optimization loop (PR 1024).

  • New odl.contrib sub-package as a place for user-contributed code that lives outside the ODL core, but is still bundled with it (PR 1020).

  • Class FiniteSet with some simple set logic (PR 865).

  • Alternative constructor frommatrix for tomographic geometries which takes a matrix that rotates (and scales) the default coordinate system. This is an advanced interface that gives full control over the initialization (PR 968).

  • Factory function cone_beam_geometry as a simple interface to cone beam geometries (PR 968).

  • Class FunctionalQuadraticPerturb that supersedes FunctionalLinearPerturb, with an additional quadratic terms and the usual rules for gradient and proximal (PR 1066).

  • Method Operator.norm that allows to implement exact (constant) values for operator norms, as well as estimating them with a power iteration (PR 1067).

  • Two phantoms smooth_cuboid and tgv_phantom (PR 1081, PR 1082, PR 1041).

  • Operator ComplexModulus, often used in MRI and phase contrast imaging (PR 1041).

  • Optimization method adam that is popular in the machine learning community (PR 972).

  • Class CallbackProgressBar for prettier progress display in solvers (PR 1097).

  • Additional axis parameter in the squeeze methods on RectGrid and RectPartition for axis-specific squeezing (PR 1110).

  • Tomographic Geometry classes now support indexing geom[indices] for extraction of sub-geometries. This is particularly useful for reconstruction methods that split up the forward operator, e.g., Kaczmarz (PR 1110).

  • Additional gamma_dual parameter in the pdhg solver (renamed from chambolle_pock_solver) for doing acceleration in the dual variable instead of the primal (PR 1092).

  • Function linear_deform now exposed (PR 1140).

  • Phantom uniform_noise (PR 1148).

  • Optimization method admm_linearized implementing the linearized version of the ADMM (Alternating Direction Method of Multipliers) (PR 1198).

  • Functional Huber, a smoothed version of the L1 Norm (PR 1191).

  • Functional BregmanDistance and a method Functional.bregman as helpers to implement “Bregmanized” versions of regularization methods (PR 1267, PR 1340).

  • Optimization method adupdates, an implementation of the Alternating Dual method of McGaffin and Fessler for nonsmooth optimization (PR 1243).

  • Helper function helical_geometry to quickly create helical cone beam geometries (PR 1157).

  • Helper functions douglas_rachford_pd_stepsize and pdhg_stepsize for automatically computing step-size-like parameters for solvers that ensure theoretical convergence (PR 1286, PR 1360).

  • Optimization methods dca, prox_dca and doubleprox_dca for difference-of-convex type problems (PR 1307).

  • Functionals IndicatorSimplex and IndicatorSumConstraint with proximals, for restraining solutions of optimization problems to simplices (PR 1347).

Updates/additions to contrib

  • New datasets sub-package for code to programatically load publicly available datasets from the web; initially containing two FIPS datasets for X-ray CT, Mayo clinic real human CT data, three MRI datasets from TU Graz, as well as some images for image processing applications (PR 992, PR 1041, PR 1193, PR 1211, PR 1352, PR 1321, PR 1367, PR 1383, PR 1421).

  • New tomo sub-package for application- or device-specific geometries and projection operators; initially populated with implementations for the Elekta ICON and XVI CT systems (PR 1035, PR 1125, PR 1138).

  • New fom sub-package for figures-of-merit (FOMs) that measure image quality (PR 1018, PR 972, PR 1116, PR 1128, PR 1108, PR 1126, PR 1144, PR 1163, PR 1280, PR 1419).

  • New solvers sub-package for application-specific solvers and experimental optimization code; initally contains a nonlocal means functional (PR 1052).

  • New tensorflow sub-package featuring seamless two-way integration of ODL and Tensorflow. This allows ODL operators and functionals to be used as layers in neural networks, which opens up a big range of (inverse problems) applications to the world of deep learning. Conversely, Tensorflow computation graphs can be treated as ODL vector space elements and, e.g., be fed to ODL solvers, resulting in an abstract representation of the result as a new computation graph (PR 972, PR 1271, PR 1366).

  • New theano sub-package featuring support for ODL operators and functionals as theano.Op. Unfortunately, this has limited usefulness since the Theano project has been stopped (PR 1098).

  • New pytorch sub-package integrating ODL with PyTorch, such that operators and functionals can be used in PyTorch neural nets, with similar implications as for the tensorflow integration, although only one-way (PR 1109, PR 1160, PR 1393).

  • New pyshearlab sub-package implementing bindings for the pyshearlab library for shearlet decomposition and analysis in 2D (PR 1115).

  • New solvers.spdhg sub-package containing a stochastic version of the PDHG optimizer (PR 1194, PR 1326).

  • New shearlab sub-package with a wrapper for the Julia package Shearlab.jl that implements shearlet decomposition and analysis (PR 1322, PR 1372).

  • New param_opt sub-package for parameter optimization strategies, e.g. regularization parameters in inverse problems (PR 1280).

  • Bugfix: MRC headers with invalid axis order entries are now handled properly (PR 990).

Improvements

  • Anisotropic voxels are now supported in 3D tomographic projections with the ASTRA toolbox (PR 976).

  • Zero-dimensional grids, partitions and DiscreteLp instances are now supported. They come up once in a while, e.g., during splitting or when building up something axis by axis (PR 995).

  • DiscreteLp can now have a mixture of uniform and non-uniform axes, and (most) operators that take an axis argument work with this. A major use case is ranges of tomographic projections with non-uniform angles (PR 996, PR 1000).

  • An annoying ComplexWarning in ProductSpace.inner was silenced by correct code (PR 1005).

  • Operator now disallows returning a different out than was passed in. This catches erroneous code that would allocate a new element regardless and return that, instead of using the provided out element (PR 1007).

  • FFTs now use the fastest available backend by default, instead of defaulting to Numpy’s FFT (PR 1006).

  • Many classes now make more use of caching of their computed properties to save the computational cost. Some of those properties are on hot code paths and make a big difference for the final runtime of typical code. Furthermore, heavily used functions with only a small number of possible inputs make use of an LRU input cache (PR 1012).

  • The performance of the douglas_rachford_pd solver was improved by the use of a temporary and in-place arithmetic (PR 1012).

  • Linear combination in R^n like spaces uses BLAS only for arrays of more than 50000 entries; below that threshold, a naive implementation tends to be faster (PR 1012).

  • All Callback classes now support the step parameter (PR 1021).

  • The pdhg solver (then chambolle_pock_solver) precomputes proximals for a 25 % speed-up (PR 1027).

  • The indices sequence in show methods now takes None entries as slice(None), thereby mirroring the behavior of the coords parameter (PR 1029).

  • Several functions (parker_weighting, fpb_filter, the ASTRA CUDA wrappers) got performance tweaks (PR 1035).

  • A number of code paths have been made faster by removing redundant checks, getting rid of abc, caching, etc. (PR 1043).

  • The whole system of tomographic geometries was overhauled with better internal consistency, clearer definitions of coordinate systems, vectorization of methods, and, most importantly, proper documentation (PR 968, PR 1159).

  • The indicate_proj_axis phantom can now be used in 2D as well (PR 968).

  • The ODL to ASTRA geometry translation tries as hard as possible to make the data layout beneficial for performance (less axis swapping). In 3D, this gives a whopping 15x speedup compared to the previous implementation (PR 968).

  • The duration of import odl was decreased with a number of optimizations, most of them consisting in lazy loading of modules or lazy evaluation of expressions that are not strictly needed at import time (PR 1090, PR 1112, PR 1402).

  • ProductSpaceElement now implements the __array__ interface if its space is a power space (PR 972).

  • A mutex was added to the ASTRA CUDA wrapper classes, to avoid race conditions between threads, e.g. when using tensorflow (PR 972).

  • Calls to super have been carefully revised and unified, either as super(<class_name>, self).<attr> for collaborative multiple inheritance, or as hard-wired OtherClass.<attr> if a very specific attribute should be used. As an aside, remnants of the slow super from the future module have been removed (PR 1161).

  • Detector subclasses can opt out of bounds checking with the new check_bounds parameter (PR 1059).

  • CallbackPrintIteration now passes through keyword args to the print function, and the CallbackPrintTiming has gotten a cumulative parameter (PR 1176).

  • Printing of ODL space elements, operators and others has been improved, and the implementation has been simplified with helper functions (PR 1203).

  • The internal representation of vector spaces and similar structures has been significantly simplified. Before, there were a number of *Set and *Space classes alongside, where the former was a more general version of the latter with less structure and fewer capabilities. This separation has been removed in favor of duck-typing: if it quacks like a space (e.g. has an inner product), it is a space (PR 1205).

  • A number of operators (differential operators like Gradient and pointwise vector field operators like PointwiseNorm) have been equipped with the capability of customizing their ranges (PR 1216).

  • Phantoms now take two additional parameters min_pt and max_pt that allow restricting their extent to a subvolume if both are given, or shift the phantom if only one of them is given (PR 1223).

  • KullbackLeiblerCrossEntropy.proximal now works with complex spaces (PR 1088).

  • The insert method of IntervalProd, RectGrid and RectPartition now takes an arbitrary number of objects to insert (PR 1088).

  • Numpy ufunc operators with 2 disparate output data types are now supported (PR 1088).

  • ProductSpace.shape now recursively determines the axes and its sizes in case of power spaces. The size and ndim properties work accordingly, i.e., len(pspace) is no longer necessarily the same as pspace.ndim, as for Numpy arrays (PR 1088).

  • ProductSpace and its elements now support indexing with integers, slices, tuples and lists (PR 1088).

  • The TensorSpace class (replacement for FnBase) and its element class Tensor (and by analogy also DiscreteLp and its elements) now fully and natively support Numpy “fancy” indexing up to very few exceptions (PR 1088).

  • Tensor and DiscreteLpElement support the Numpy 1.13 __array_ufunc__ interface which allows classes to take control over how ufuncs are evaluated. With this interface, it is possible to transparently perform in-place operations like np.cos(odl_obj, out=odl_obj), which was not possible with __array__ and __array_wrap__ before. Furthermore, other methods of Numpy ufuncs are available, e.g. np.add.reduce(odl_in, axis=0, out=odl_out) (PR 1088).

  • A non-discretized FunctionSpace can now be vector- or tensor-valued, using a Numpy dtype with shape, e.g., np.dtype((float, (2, 3))) (PR 1088).

  • The element methods of TensorSpace and DiscreteLp have a new order parameter to determine the array memory layout (PR 1088).

  • ProductSpaceElement.asarray has been added (PR 1152).

  • SeparableSum now accepts vector-valued step sizes, and several functionals (e.g. L1Norm) takes pointwise step sizes, with full support for proximal, convex conjuage etc. (PR 1166).

  • KullbackLeibler.convex_conj now works on product spaces (PR 1287).

  • Generation of the sparse matrix containing the operators in ProductSpaceOperator is now more robust and disallows malformed constructions like ProductSpaceOperator([A, B]) with matrices that are not 2D (PR 1293, PR 1295).

  • ProductSpace and ProductSpaceElement now implement real_space, complex_space, real, imag, conj, astype and __array_wrap__ where applicable (PR 1288).

  • matrix_representation now works with arbitrary tensor spaces as domain and range of an operator. The result will be a tensor with the sum of the number of axes in domain and range (PR 1308).

  • Optimizations for common cases in PointwiseNorm have been added, making the code run 1.5-2 times faster in typical conditions (PR 1318).

  • Several complex-to-real operators like ComplexModulus now have a derivative that implements the \mathbb{C} = \mathbb{R}^2 interpretation. Furthermore, linearity is interpreted in the same sense, allowing optimization of certain operations (PR 1324, PR 1331).

  • The colorbar in plots from show can new be turned off with the colorbar flag (PR 1343).

  • FunctionSpace and ProductSpace now have properties is_real and is_complex (PR 1348).

  • power_method_opnorm now starts from a noise element, making it easier to use with operators that have null spaces, like Gradient (PR 1286).

  • The default of the omega relaxation parameter in the landweber solver has been changed from 1 to 1 / op.norm(estimate=True) ** 2, which theoretically guarantees convergence (PR 1286).

  • For the solvers douglas_rachford_pd and pdhg, the step-size-like parameters have been made optional, with the default values being computed automatically using some heuristics and the bound that guarantees convergence (PR 1286).

  • The LpNorm proximal now also supports exponent infinity (PR 1347).

  • Filters for FBP reconstruction can now be given as arrays to fbp_op (PR 1379).

  • ProductSpace and its element type now implement nbytes (PR 1410).

Bugfixes

  • Resolve an issue with negative indices resulting in a truncated image in ellipsoid_phantom (PR 998).

  • MultiplyOperator.adjoint now works for scalar domain and range (PR 987).

  • ReductionOperator._call now properly unwraps the result before returning it (PR 1012, PR 1010).

  • Fix the issue of 0 * log(0) producing NaN in KullbackLeibler (PR 1042).

  • Sometimes, titles of figures resulting from show would be clipped. This is now fixed (PR 1045).

  • Parallel3dEulerGeometry now actually works with ASTRA projectors (PR 968).

  • Fix a rounding error preventing colorbar ticks to show up in show (PR 1063).

  • DiscreteLp.astype now propagates its axis labels as expected (PR 1073).

  • Resolve an issue with wrong inner products on non-uniformly discretized spaces (PR 1096).

  • CallbackStore now works with objects that do have a copy method but do implement __copy__ (PR 1094).

  • RayTransform and FBP operators used the wrong projection space weighting if the reconstruction space was unweighted. This was fixed, but the patch has been superseded by PR 1088 (PR 1099, PR 1102).

  • Fix LinearSpace.zeros using the wrong order of arguments (PR 972).

  • ProductSpaceElement now has a (space pass-through) shape property (PR 972).

  • Resolve several issues with complex spaces in optimization problems (PR 1120).

  • The tick labels in show are now “NaN-proof” (PR 1092, PR 1158, PR 1088).

  • Fix a bug in nonuniform_partition that caused length-1 inputs to crash the function (PR 1141).

  • Fix DiscreteLpElement.real (and .imag) sometimes returning a copy instead of a view (PR 1155).

  • Fix ConeFlatGeometry not propagating pitch in its __getitem__ method (PR 1173).

  • Fix a bug in parker_weighting caused by the change of geometry definitions (PR 1175).

  • Resolve an issue with wrong results of the L1 convex conjugate proximal when input and output were aliased (PR 1182).

  • Correct the implementation of Operator{Left,Right}VectorMult.adjoint for complex spaces (PR 1192).

  • Add a workaround for the fact BLAS internally works with 32-bit integers as indices, which goes wrong for very large arrays (PR 1190).

  • Fix Numpy errors not recognizing builtins.int from the future library as valid dtype by disallowing that object as dtype internally (PR 1205).

  • Resolve a number of minor issues with geometry methods’ broadcasting (PR 1210).

  • Correct handling of degenerate (size 1) axes in Fourier transform range inference (PR 1208).

  • Fix a bug in OperatorSum and OperatorPointwiseProduct that resulted in wrong outputs for aliased input and output objects (PR 1225).

  • Fix the broken field determination for ProductSpace(space, 0) (PR 1088).

  • Add back the string dtypes in NumpyTensorSpace.available_dtypes (PR 1236, PR 1294).

  • Disallow bool conversion of Tensor with size > 1 (PR 1235).

  • Fix a sign flip error in 2D geometries (PR 1245).

  • Blacklisted several patch versions of NumPy 1.14 due to bugs in new-style array printing that result in failing doctests (PR 1265).

  • Correct the implementations of PointwiseNorm.derivative and GroupL1Norm.gradient to account for division-by-zero errors (PR 1070).

  • Fix issue in NumpyTensor.lincomb when one of the scalars is NaN (PR 1272).

  • Fix indexing into RectPartition.byaxis producing a wrong result with integers (PR 1284).

  • Resolve space.astype(float) failing for space.dtype == bool (PR 1285).

  • Add a missing check for scalar sigma in FunctionalQuadraticPerturb.proximal (PR 1283).

  • Fix an error in the adjoint of SamplingOperator triggered by a sampling_points argument of length 1 (PR 1351).

  • Make DiscreteLpElement.show use the correct interpolation scheme (PR 1375).

  • Fix checking of pyFFTW versions to also support Git revision versions (PR 1373).

  • Correct the implementation of MultiplyOperator.adjoint for complex spaces (PR 1390).

  • Replace the improper and potentially ambiguous indexing with tuple indexing as signalled by the Numpy deprecation warning (PR 1420).

API Changes

  • Functions and attributes related to convex conjugates now use convex_conj as name part instead of cconj (PR 1048).

  • ParallelGeometry was renamed to ParallelBeamGeometry (PR 968).

  • HelicalConeFlatGeometry was renamed to ConeFlatGeometry, and CircularConeFlatGeometry was removed as special case (PR 968).

  • pitch_offset in 3D cone beam geometries was renamed to offset_along_axis (PR 968).

  • ellipsoid_phantom now takes angles in radians instead of degrees (PR 972).

  • The L1Norm.gradient operator now implements the (ad-hoc) derivative method, returning ZeroOperator (PR 972).

  • The base class for solver callbacks was renamed from SolverCallback to Callback (PR 1097).

  • The chambolle_pock_solver has been renamed to pdhg (Primal-Dual Hybrid Gradient), along with all references to “Chambolle-Pock” (PR 1092).

  • The gamma parameter in pdhg (see one above) has been renamed to gamma_primal, since one can now alternatively specify a gamma_dual acceleration parameter (PR 1092).

  • As a result of merging internal *Set and *Space classes, a number of arguments to internal class constructors like FunctionSpaceMapping have been renamed accordingly (PR 1205)

  • Remove the (dubious) dist_using_inner optimization of vector spaces (PR 1214).

  • The class Ntuples has been merged into FnBase, but both have been superseded by PR 1088 (PR 1205, PR 1216).

  • The writable_array context manager no longer takes an arbitrary number of positional arguments as pass-through, only keyword arguments (PR 1088).

  • LinearSpaceElement and ProductSpaceElement are no longer available in the top-level odl namespace (PR 1088).

  • The NoWeighting classes have been removed due to their odd behavior. For the time being, no weighting is equivalent to weighting with constant 1.0, but this will change a bit in the future (PR 1088).

  • The classes FnBase and NumpyFn have been removed in favor of TensorSpace and NumpyTensorSpace. Likewise, the fn factory function is now called tensor_space, and any other name associated with fn has been renamed accordingly (PR 1088).

  • The uspace and dspace properties of Discretization have been renamed to fspace (“function space”) and tspace (“tensor space”), respectively (PR 1088).

  • With mandatory multi-indexing support for TensorSpace implementations, the old CudaFn class is no longer supported. The next release 1.0.0 will have a much more powerful replacement using CuPy, see PR 1401 (PR 1088).

  • The meanings of the parameters f and g has been switched in pdhg to make the interface match the rest of the solvers (PR 1286).

  • Bindings to the STIR reconstruction software have been overhauled and moved out of the core into a separate repository (PR 1403).

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)

  • Add 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

  • Move 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.

  • Add Numpy to the numy based spaces. They are now named NumpyFn and NumpyNtuples.

  • Prepend npy_ to all methods specific to ntuples such as weightings.

ODL 0.2.4 Release Notes (2016-06-28)

New features

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

ODL 0.1 Release Notes (2016-03-08)

First official release.