Repository navigation
Releases: KUL-recon-lab/parallelproj
Releases · KUL-recon-lab/parallelproj
Release list
v2.0.2 (Jul 29, 2026)
Minor Bugfix
Convert all kwargs (such as sigma) of the GaussianFilterOperator to python native (e.g. list of floats for sigma).
E.g., enables passing of torch tensors for sigma.
v2.0.1 (Jul 24, 2026)
Bug fix
GaussianFilterOperatornow works with PyTorch (and array-api-strict) CPU
arrays under modern scipy. The CPU path previously passed the input array
straight toscipy.ndimage.gaussian_filter; with array-API-aware scipy
(SCIPY_ARRAY_API/ scipy ≥ 1.16) this made scipy compute natively in the
input's namespace, wheregaussian_filter1dreverses the kernel with a
negative-step slice (weights[::-1]) that PyTorch does not support. The
operator now filters on a NumPy view (zero-copy on CPU) and converts back, so
it is robust regardless of the scipy version orSCIPY_ARRAY_API. This
fixes downstream use via e.g. DeepInv.
v2.0.0 (Jul 17, 2026)
Major v2.0.0 release including many new features but also a few minor breaking changes.
Please see our changelog for details.
v1.10.2 (Aug 20, 2025)
summary of changes
- add compatibility for latest cupy version (>= 13.5) which require
from_dlpackto convert
from torch tensors - fix minor issues to be compatible with
array-api-strict~=2.0
merged PRs
- update binder postbuild by @gschramm in #110
- remove pdm by @gschramm in #111
- add gradient field projection operator by @gschramm in #112
- WIP example that shows how to implement a gradient descent layer by @gschramm in #113
- restrict cupy version in install instructions by @gschramm in #117
- fix incompatibilities with new cupy version (13.5) by @gschramm in #118
- bump version and update changelog by @gschramm in #119
Full Changelog: v1.10.1...v1.10.2
v1.10.1
v1.10.0
v1.9.1
v1.9.0
v.1.8.0
v1.8.0 (March 20, 2024)
- add function to count event multiplicity
- add more examples (e.g. DePierro and LM SPDHG)
- re-organize folder structure and pyproject.toml
- force array-api-compat<1.5 (bug in 1.5.0)
- use array-api-strict instead of numpy.array_api