Warning: Permanently added '34.229.212.245' (ED25519) to the list of known hosts.

You can reproduce this build on your computer by running:

  sudo dnf install copr-rpmbuild
  /usr/bin/copr-rpmbuild --verbose --drop-resultdir --task-url https://copr.fedorainfracloud.org/backend/get-build-task/11283289-fedora-43-x86_64 --chroot fedora-43-x86_64


Version: 1.11
PID: 11827
Logging PID: 11829
Task:
{'allow_user_ssh': False,
 'appstream': False,
 'background': False,
 'build_id': 11283289,
 'buildroot_pkgs': [],
 'chroot': 'fedora-43-x86_64',
 'enable_net': False,
 'fedora_review': False,
 'git_hash': '8d50c187fa9c88e50d7f946c5f309e0fa9526e08',
 'git_repo': 'https://copr-dist-git.fedorainfracloud.org/git/mhough/neurofedora/python-pyradplan',
 'isolation': 'default',
 'memory_reqs': 2048,
 'package_name': 'python-pyradplan',
 'package_version': '0.8.0-1',
 'project_dirname': 'neurofedora',
 'project_name': 'neurofedora',
 'project_owner': 'mhough',
 'repo_priority': None,
 'repos': [{'baseurl': 'https://download.copr.fedorainfracloud.org/results/mhough/neurofedora/fedora-43-x86_64/',
            'id': 'copr_base',
            'name': 'Copr repository',
            'priority': None,
            'repo_gpgcheck': False}],
 'sandbox': 'mhough/neurofedora--mhough',
 'source_json': {},
 'source_type': None,
 'ssh_public_keys': None,
 'storage': 1,
 'submitter': 'mhough',
 'tags': [],
 'task_id': '11283289-fedora-43-x86_64',
 'timeout': 86400,
 'uses_devel_repo': False,
 'with_opts': [],
 'without_opts': []}

Running: git clone https://copr-dist-git.fedorainfracloud.org/git/mhough/neurofedora/python-pyradplan /var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan --depth 500 --no-single-branch --recursive

cmd: ['git', 'clone', 'https://copr-dist-git.fedorainfracloud.org/git/mhough/neurofedora/python-pyradplan', '/var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan', '--depth', '500', '--no-single-branch', '--recursive']
cwd: .
rc: 0
stdout: 
stderr: Cloning into '/var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan'...

Running: git checkout 8d50c187fa9c88e50d7f946c5f309e0fa9526e08 --

cmd: ['git', 'checkout', '8d50c187fa9c88e50d7f946c5f309e0fa9526e08', '--']
cwd: /var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan
rc: 0
stdout: 
stderr: Note: switching to '8d50c187fa9c88e50d7f946c5f309e0fa9526e08'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 8d50c18 automatic import of python-pyradplan

Running: dist-git-client sources

cmd: ['dist-git-client', 'sources']
cwd: /var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan
rc: 0
stdout: 
stderr: INFO: Reading stdout from command: git rev-parse --abbrev-ref HEAD
INFO: Reading stdout from command: git rev-parse HEAD
INFO: Reading sources specification file: sources
INFO: Downloading pyradplan-0.8.0.tar.gz
INFO: Reading stdout from command: curl --help all
INFO: Calling: curl -H Pragma: -H 'Accept-Encoding: identity' -o pyradplan-0.8.0.tar.gz --location --connect-timeout 60 --retry 3 --retry-delay 10 --remote-time --show-error --fail --retry-all-errors https://copr-dist-git.fedorainfracloud.org/repo/pkgs/mhough/neurofedora/python-pyradplan/pyradplan-0.8.0.tar.gz/md5/20981edf7484cea480762e9af971343e/pyradplan-0.8.0.tar.gz
  % Total    % Received % Xferd  Average Speed  Time    Time    Time   Current
                                 Dload  Upload  Total   Spent   Left   Speed
100 33.80M 100 33.80M   0      0 500.5M      0                              0
INFO: Reading stdout from command: md5sum pyradplan-0.8.0.tar.gz

tail: /var/lib/copr-rpmbuild/main.log: file truncated
Running (timeout=86400): unbuffer mock --spec /var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan/python-pyradplan.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1794040424.263307 -r /var/lib/copr-rpmbuild/results/configs/child.cfg
INFO: mock.py version 6.9 starting (python version = 3.14.5, NVR = mock-6.9-1.fc44), args: /usr/libexec/mock/mock --spec /var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan/python-pyradplan.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1794040424.263307 -r /var/lib/copr-rpmbuild/results/configs/child.cfg
Start(bootstrap): init plugins
INFO: tmpfs initialized
INFO: selinux enabled
INFO: chroot_scan: initialized
INFO: compress_logs: initialized
Finish(bootstrap): init plugins
Start: init plugins
INFO: tmpfs initialized
INFO: selinux enabled
INFO: chroot_scan: initialized
INFO: compress_logs: initialized
Finish: init plugins
INFO: Signal handler active
Start: run
INFO: Start(/var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan/python-pyradplan.spec)  Config(fedora-43-x86_64)
Start: clean chroot
Finish: clean chroot
Mock Version: 6.9
INFO: Mock Version: 6.9
Start(bootstrap): chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-43-x86_64-bootstrap-1794040424.263307/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start(bootstrap): cleaning package manager metadata
Finish(bootstrap): cleaning package manager metadata
INFO: Guessed host environment type: unknown
INFO: Using container image: registry.fedoraproject.org/fedora:43
INFO: Pulling image: registry.fedoraproject.org/fedora:43
INFO: Tagging container image as mock-bootstrap-e8eb2f3c-458f-4f64-9a9f-a08175a6d025
INFO: Checking that 07f45a24d0719212cd8dffda2b52f6cdc5cf22a8efd5a6dd0ff69729a80620d5 image matches host's architecture
INFO: Copy content of container 07f45a24d0719212cd8dffda2b52f6cdc5cf22a8efd5a6dd0ff69729a80620d5 to /var/lib/mock/fedora-43-x86_64-bootstrap-1794040424.263307/root
INFO: mounting 07f45a24d0719212cd8dffda2b52f6cdc5cf22a8efd5a6dd0ff69729a80620d5 with podman image mount
INFO: image 07f45a24d0719212cd8dffda2b52f6cdc5cf22a8efd5a6dd0ff69729a80620d5 as /var/lib/containers/storage/overlay/9ce6879fdc11007a0da77be81259f03144e5c4e9df98e802de5b328a0cb867c2/merged
INFO: umounting image 07f45a24d0719212cd8dffda2b52f6cdc5cf22a8efd5a6dd0ff69729a80620d5 (/var/lib/containers/storage/overlay/9ce6879fdc11007a0da77be81259f03144e5c4e9df98e802de5b328a0cb867c2/merged) with podman image umount
INFO: Removing image mock-bootstrap-e8eb2f3c-458f-4f64-9a9f-a08175a6d025
INFO: Package manager dnf5 detected and used (fallback)
INFO: Not updating bootstrap chroot, bootstrap_image_ready=True
Start(bootstrap): creating root cache
Finish(bootstrap): creating root cache
Finish(bootstrap): chroot init
Start: chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-43-x86_64-1794040424.263307/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start: cleaning package manager metadata
Finish: cleaning package manager metadata
INFO: enabled HW Info plugin
INFO: Package manager dnf5 detected and used (direct choice)
INFO: Buildroot is handled by package management downloaded with a bootstrap image:
  rpm-6.0.2-1.fc43.x86_64
  rpm-sequoia-1.10.3-1.fc43.x86_64
  dnf5-5.2.18.0-5.fc43.x86_64
  dnf5-plugins-5.2.18.0-5.fc43.x86_64
Start: installing minimal buildroot with dnf5
Updating and loading repositories:
 fedora                                 100% |  27.7 MiB/s |  35.4 MiB |  00m01s
 Copr repository                        100% | 676.5 KiB/s | 672.4 KiB |  00m01s
 updates                                100% |  30.6 MiB/s |  34.9 MiB |  00m01s
Repositories loaded.
Package                            Arch   Version                    Repository      Size
Installing group/module packages:
 bash                              x86_64 5.3.0-2.fc43               fedora       8.4 MiB
 bzip2                             x86_64 1.0.8-21.fc43              fedora      95.3 KiB
 coreutils                         x86_64 9.7-10.fc43                updates      5.4 MiB
 cpio                              x86_64 2.15-6.fc43                fedora       1.1 MiB
 diffutils                         x86_64 3.12-3.fc43                fedora       1.6 MiB
 fedora-release-common             noarch 43-27                      updates     20.6 KiB
 findutils                         x86_64 1:4.10.0-6.fc43            fedora       1.8 MiB
 gawk                              x86_64 5.3.2-2.fc43               fedora       1.8 MiB
 glibc-minimal-langpack            x86_64 2.42-17.fc43               updates      0.0   B
 grep                              x86_64 3.12-2.fc43                fedora       1.0 MiB
 gzip                              x86_64 1.13-4.fc43                fedora     388.8 KiB
 info                              x86_64 7.2-7.fc43                 updates    353.9 KiB
 patch                             x86_64 2.8-2.fc43                 fedora     222.8 KiB
 redhat-rpm-config                 noarch 343-14.fc43                updates    182.9 KiB
 rpm-build                         x86_64 6.0.2-1.fc43               updates    286.4 KiB
 sed                               x86_64 4.9-5.fc43                 fedora     857.3 KiB
 shadow-utils                      x86_64 2:4.18.0-3.fc43            fedora       3.9 MiB
 tar                               x86_64 2:1.35-6.fc43              fedora       2.9 MiB
 unzip                             x86_64 6.0-67.fc43                fedora     386.3 KiB
 util-linux                        x86_64 2.41.6-1.fc43              updates      3.5 MiB
 which                             x86_64 2.23-3.fc43                fedora      83.5 KiB
 xz                                x86_64 1:5.8.1-4.fc43             updates      1.3 MiB
Installing dependencies:
 add-determinism                   x86_64 0.6.0-3.fc43               updates      2.3 MiB
 alternatives                      x86_64 1.33-3.fc43                updates     62.2 KiB
 ansible-srpm-macros               noarch 1-18.1.fc43                fedora      35.7 KiB
 audit-libs                        x86_64 4.2.1-1.fc43               updates    382.5 KiB
 binutils                          x86_64 2.45.1-5.fc43              updates     27.3 MiB
 build-reproducibility-srpm-macros noarch 0.6.0-3.fc43               updates    735.0   B
 bzip2-libs                        x86_64 1.0.8-21.fc43              fedora      80.6 KiB
 ca-certificates                   noarch 2026.2.92_v9.0.319-2.fc43  updates      2.7 MiB
 cmake-srpm-macros                 noarch 3.31.11-1.fc43             updates    524.0   B
 coreutils-common                  x86_64 9.7-10.fc43                updates     11.3 MiB
 crypto-policies                   noarch 20251125-1.git63291f8.fc43 updates    147.1 KiB
 curl                              x86_64 8.15.0-12.fc43             updates    461.4 KiB
 cyrus-sasl-lib                    x86_64 2.1.28-33.fc43             fedora       2.3 MiB
 debugedit                         x86_64 5.2-3.fc43                 fedora     214.0 KiB
 dwz                               x86_64 0.16-2.fc43                fedora     287.1 KiB
 ed                                x86_64 1.22.2-1.fc43              fedora     148.1 KiB
 efi-srpm-macros                   noarch 6-4.fc43                   fedora      40.1 KiB
 elfutils                          x86_64 0.197-1.fc43               updates      3.1 MiB
 elfutils-debuginfod-client        x86_64 0.197-1.fc43               updates     83.8 KiB
 elfutils-default-yama-scope       noarch 0.197-1.fc43               updates      1.8 KiB
 elfutils-libelf                   x86_64 0.197-1.fc43               updates      1.6 MiB
 elfutils-libs                     x86_64 0.197-1.fc43               updates    695.4 KiB
 fedora-gpg-keys                   noarch 43-2                       updates    259.0 KiB
 fedora-release                    noarch 43-27                      updates      0.0   B
 fedora-release-identity-basic     noarch 43-27                      updates    631.0   B
 fedora-repos                      noarch 43-2                       updates      4.9 KiB
 file                              x86_64 5.46-9.fc43                updates    100.2 KiB
 file-libs                         x86_64 5.46-9.fc43                updates     11.9 MiB
 filesystem                        x86_64 3.18-50.fc43               fedora     112.0   B
 filesystem-srpm-macros            noarch 3.18-50.fc43               fedora      38.2 KiB
 fonts-srpm-macros                 noarch 1:2.0.5-23.fc43            fedora      55.8 KiB
 forge-srpm-macros                 noarch 0.4.0-3.fc43               fedora      38.9 KiB
 fpc-srpm-macros                   noarch 1.3-15.fc43                fedora     144.0   B
 gap-srpm-macros                   noarch 2-1.fc43                   fedora       2.1 KiB
 gdb-minimal                       x86_64 18.1-1.fc43                updates     13.8 MiB
 gdbm-libs                         x86_64 1:1.23-10.fc43             fedora     129.9 KiB
 ghc-srpm-macros                   noarch 1.9.2-3.fc43               fedora     779.0   B
 glibc                             x86_64 2.42-17.fc43               updates      6.7 MiB
 glibc-common                      x86_64 2.42-17.fc43               updates      1.0 MiB
 glibc-gconv-extra                 x86_64 2.42-17.fc43               updates      7.2 MiB
 gmp                               x86_64 1:6.3.0-4.fc43             fedora     811.2 KiB
 gnat-srpm-macros                  noarch 7-1.fc43                   updates      1.0 KiB
 gnome-srpm-macros                 noarch 1.0-1.fc43                 updates      3.5 KiB
 gnulib-l10n                       noarch 20241231-1.fc43            fedora     655.0 KiB
 gnupg2                            x86_64 2.4.9-5.fc43               updates      6.6 MiB
 gnupg2-dirmngr                    x86_64 2.4.9-5.fc43               updates    618.4 KiB
 gnupg2-gpg-agent                  x86_64 2.4.9-5.fc43               updates    671.4 KiB
 gnupg2-gpgconf                    x86_64 2.4.9-5.fc43               updates    250.0 KiB
 gnupg2-keyboxd                    x86_64 2.4.9-5.fc43               updates    201.4 KiB
 gnupg2-verify                     x86_64 2.4.9-5.fc43               updates    348.5 KiB
 gnutls                            x86_64 3.8.13-1.fc43              updates      3.8 MiB
 go-srpm-macros                    noarch 3.8.0-1.fc43               fedora      61.9 KiB
 gpgverify                         noarch 2.2-3.fc43                 fedora       8.7 KiB
 ima-evm-utils-libs                x86_64 1.6.2-6.fc43               fedora      60.7 KiB
 jansson                           x86_64 2.14-3.fc43                fedora      89.1 KiB
 java-srpm-macros                  noarch 1-7.fc43                   fedora     870.0   B
 json-c                            x86_64 0.18-7.fc43                fedora      82.7 KiB
 kernel-srpm-macros                noarch 1.0-27.fc43                fedora       1.9 KiB
 keyutils-libs                     x86_64 1.6.3-6.fc43               fedora      54.3 KiB
 krb5-libs                         x86_64 1.22.2-4.fc43              updates      2.3 MiB
 libacl                            x86_64 2.4.0-1.fc43               updates     39.9 KiB
 libarchive                        x86_64 3.8.4-1.fc43               updates    955.3 KiB
 libassuan                         x86_64 2.5.7-4.fc43               fedora     163.8 KiB
 libattr                           x86_64 2.6.0-1.fc43               updates     24.3 KiB
 libblkid                          x86_64 2.41.6-1.fc43              updates    262.3 KiB
 libbrotli                         x86_64 1.2.1-1.fc43               updates    865.0 KiB
 libcap                            x86_64 2.76-4.fc43                updates    209.1 KiB
 libcap-ng                         x86_64 0.9.6-1.fc43               updates     68.8 KiB
 libcom_err                        x86_64 1.47.3-2.fc43              fedora      63.1 KiB
 libcurl                           x86_64 8.15.0-12.fc43             updates    907.0 KiB
 libeconf                          x86_64 0.7.9-2.fc43               fedora      64.9 KiB
 libevent                          x86_64 2.1.13-1.fc43              updates    884.4 KiB
 libfdisk                          x86_64 2.41.6-1.fc43              updates    380.3 KiB
 libffi                            x86_64 3.5.2-1.fc43               updates     83.8 KiB
 libfsverity                       x86_64 1.6-3.fc43                 fedora      28.5 KiB
 libgcc                            x86_64 15.3.1-1.fc43              updates    266.6 KiB
 libgcrypt                         x86_64 1.11.1-4.fc43              updates      1.6 MiB
 libgomp                           x86_64 15.3.1-1.fc43              updates    541.8 KiB
 libgpg-error                      x86_64 1.55-2.fc43                fedora     915.3 KiB
 libidn2                           x86_64 2.3.8-2.fc43               fedora     552.5 KiB
 libksba                           x86_64 1.6.7-4.fc43               fedora     398.5 KiB
 liblastlog2                       x86_64 2.41.6-1.fc43              updates     37.6 KiB
 libmount                          x86_64 2.41.6-1.fc43              updates    376.6 KiB
 libnghttp2                        x86_64 1.66.0-3.fc43              updates    162.1 KiB
 libpfm                            x86_64 4.14.1-2.fc43              updates      2.4 MiB
 libpkgconf                        x86_64 2.3.0-3.fc43               fedora      78.1 KiB
 libpsl                            x86_64 0.21.5-6.fc43              fedora      76.4 KiB
 libselinux                        x86_64 3.9-5.fc43                 fedora     193.1 KiB
 libsemanage                       x86_64 3.9-4.fc43                 fedora     308.5 KiB
 libsepol                          x86_64 3.9-2.fc43                 fedora     822.0 KiB
 libsmartcols                      x86_64 2.41.6-1.fc43              updates    180.3 KiB
 libssh                            x86_64 0.11.5-1.fc43              updates    573.1 KiB
 libssh-config                     noarch 0.11.5-1.fc43              updates    277.0   B
 libstdc++                         x86_64 15.3.1-1.fc43              updates      2.8 MiB
 libtasn1                          x86_64 4.21.0-1.fc43              updates    176.7 KiB
 libtool-ltdl                      x86_64 2.5.4-8.fc43               updates     70.1 KiB
 libunistring                      x86_64 1.1-10.fc43                fedora       1.7 MiB
 libusb1                           x86_64 1.0.30-1.fc43              updates    176.0 KiB
 libuuid                           x86_64 2.41.6-1.fc43              updates     37.2 KiB
 libverto                          x86_64 0.3.2-11.fc43              fedora      25.4 KiB
 libxcrypt                         x86_64 4.5.2-1.fc43               updates    285.3 KiB
 libxml2                           x86_64 2.12.10-5.fc43             fedora       1.7 MiB
 libzstd                           x86_64 1.5.7-2.fc43               fedora     799.9 KiB
 lua-libs                          x86_64 5.4.8-4.fc43               updates    281.9 KiB
 lua-srpm-macros                   noarch 1-16.fc43                  fedora       1.3 KiB
 lz4-libs                          x86_64 1.10.0-3.fc43              fedora     161.4 KiB
 mpfr                              x86_64 4.2.2-2.fc43               fedora     832.8 KiB
 ncurses-base                      noarch 6.5-7.20250614.fc43        fedora     328.1 KiB
 ncurses-libs                      x86_64 6.5-7.20250614.fc43        fedora     946.3 KiB
 nettle                            x86_64 3.10.1-2.fc43              fedora     790.6 KiB
 npth                              x86_64 1.8-3.fc43                 fedora      49.6 KiB
 ocaml-srpm-macros                 noarch 11-2.fc43                  fedora       1.9 KiB
 openblas-srpm-macros              noarch 2-20.fc43                  fedora     112.0   B
 openldap                          x86_64 2.6.15-1.fc43              updates    659.2 KiB
 openssl-libs                      x86_64 1:3.5.9-1.fc43             updates      8.9 MiB
 p11-kit                           x86_64 0.26.5-1.fc43              updates      2.5 MiB
 p11-kit-trust                     x86_64 0.26.5-1.fc43              updates    466.3 KiB
 package-notes-srpm-macros         noarch 0.20-1.fc43                updates      1.6 KiB
 pam-libs                          x86_64 1.7.1-5.fc43               updates    126.5 KiB
 pcre2                             x86_64 10.47-1.fc43               updates    702.6 KiB
 pcre2-syntax                      noarch 10.47-1.fc43               updates    281.9 KiB
 perl-srpm-macros                  noarch 1-60.fc43                  fedora     861.0   B
 pkgconf                           x86_64 2.3.0-3.fc43               fedora      88.5 KiB
 pkgconf-m4                        noarch 2.3.0-3.fc43               fedora      14.4 KiB
 pkgconf-pkg-config                x86_64 2.3.0-3.fc43               fedora     989.0   B
 popt                              x86_64 1.19-9.fc43                fedora     132.8 KiB
 publicsuffix-list-dafsa           noarch 20260116-1.fc43            updates     70.4 KiB
 pyproject-srpm-macros             noarch 1.24.0-1.fc43              updates      3.4 KiB
 python-srpm-macros                noarch 3.14-5.fc43                fedora      51.5 KiB
 qt5-srpm-macros                   noarch 5.15.18-1.fc43             updates    500.0   B
 qt6-srpm-macros                   noarch 6.10.3-1.fc43              updates    472.0   B
 readline                          x86_64 8.3-2.fc43                 fedora     511.7 KiB
 rpm                               x86_64 6.0.2-1.fc43               updates      3.1 MiB
 rpm-build-libs                    x86_64 6.0.2-1.fc43               updates    264.3 KiB
 rpm-libs                          x86_64 6.0.2-1.fc43               updates    933.5 KiB
 rpm-sequoia                       x86_64 1.10.3-1.fc43              updates      2.4 MiB
 rpm-sign-libs                     x86_64 6.0.2-1.fc43               updates     39.6 KiB
 rust-srpm-macros                  noarch 28.5-1.fc43                updates      5.5 KiB
 setup                             noarch 2.15.0-26.fc43             fedora     725.0 KiB
 sqlite-libs                       x86_64 3.50.2-3.fc43              updates      1.5 MiB
 systemd-libs                      x86_64 258.11-1.fc43              updates      2.3 MiB
 systemd-standalone-sysusers       x86_64 258.11-1.fc43              updates    297.4 KiB
 tpm2-tss                          x86_64 4.1.3-8.fc43               fedora       1.6 MiB
 tree-sitter-srpm-macros           noarch 0.4.2-1.fc43               fedora       8.3 KiB
 util-linux-core                   x86_64 2.41.6-1.fc43              updates      1.5 MiB
 xxhash-libs                       x86_64 0.8.4-2.fc43               updates     90.0 KiB
 xz-libs                           x86_64 1:5.8.1-4.fc43             updates    217.8 KiB
 zig-srpm-macros                   noarch 1-5.fc43                   fedora       1.1 KiB
 zip                               x86_64 3.0-44.fc43                fedora     694.5 KiB
 zlib-ng-compat                    x86_64 2.3.3-2.fc43               updates    161.7 KiB
 zstd                              x86_64 1.5.7-2.fc43               fedora       1.7 MiB
Installing groups:
 Buildsystem building group                                                              

Transaction Summary:
 Installing:       173 packages

Total size of inbound packages is 60 MiB. Need to download 60 MiB.
After this operation, 204 MiB extra will be used (install 204 MiB, remove 0 B).
[  1/173] bzip2-0:1.0.8-21.fc43.x86_64  100% |   5.6 MiB/s |  51.6 KiB |  00m00s
[  2/173] diffutils-0:3.12-3.fc43.x86_6 100% |  95.8 MiB/s | 392.3 KiB |  00m00s
[  3/173] cpio-0:2.15-6.fc43.x86_64     100% |  17.9 MiB/s | 293.1 KiB |  00m00s
[  4/173] bash-0:5.3.0-2.fc43.x86_64    100% |  85.0 MiB/s |   1.9 MiB |  00m00s
[  5/173] findutils-1:4.10.0-6.fc43.x86 100% |  59.7 MiB/s | 550.0 KiB |  00m00s
[  6/173] grep-0:3.12-2.fc43.x86_64     100% |  41.7 MiB/s | 299.1 KiB |  00m00s
[  7/173] gzip-0:1.13-4.fc43.x86_64     100% |  55.4 MiB/s | 170.1 KiB |  00m00s
[  8/173] patch-0:2.8-2.fc43.x86_64     100% |  37.0 MiB/s | 113.8 KiB |  00m00s
[  9/173] sed-0:4.9-5.fc43.x86_64       100% |  77.4 MiB/s | 317.1 KiB |  00m00s
[ 10/173] tar-2:1.35-6.fc43.x86_64      100% | 104.5 MiB/s | 856.4 KiB |  00m00s
[ 11/173] shadow-utils-2:4.18.0-3.fc43. 100% | 116.6 MiB/s |   1.3 MiB |  00m00s
[ 12/173] unzip-0:6.0-67.fc43.x86_64    100% |  16.3 MiB/s | 183.7 KiB |  00m00s
[ 13/173] which-0:2.23-3.fc43.x86_64    100% |   8.2 MiB/s |  41.7 KiB |  00m00s
[ 14/173] fedora-release-common-0:43-27 100% |   5.5 MiB/s |  22.6 KiB |  00m00s
[ 15/173] coreutils-0:9.7-10.fc43.x86_6 100% | 142.2 MiB/s |   1.1 MiB |  00m00s
[ 16/173] glibc-minimal-langpack-0:2.42 100% |  15.3 MiB/s |  47.1 KiB |  00m00s
[ 17/173] gawk-0:5.3.2-2.fc43.x86_64    100% | 112.5 MiB/s |   1.1 MiB |  00m00s
[ 18/173] info-0:7.2-7.fc43.x86_64      100% |  25.5 MiB/s | 182.7 KiB |  00m00s
[ 19/173] redhat-rpm-config-0:343-14.fc 100% |  12.4 MiB/s |  76.1 KiB |  00m00s
[ 20/173] rpm-build-0:6.0.2-1.fc43.x86_ 100% |  33.0 MiB/s | 135.3 KiB |  00m00s
[ 21/173] xz-1:5.8.1-4.fc43.x86_64      100% | 111.9 MiB/s | 572.7 KiB |  00m00s
[ 22/173] util-linux-0:2.41.6-1.fc43.x8 100% | 149.1 MiB/s |   1.2 MiB |  00m00s
[ 23/173] ncurses-libs-0:6.5-7.20250614 100% |  65.0 MiB/s | 332.7 KiB |  00m00s
[ 24/173] filesystem-0:3.18-50.fc43.x86 100% | 111.1 MiB/s |   1.3 MiB |  00m00s
[ 25/173] bzip2-libs-0:1.0.8-21.fc43.x8 100% |   7.0 MiB/s |  43.1 KiB |  00m00s
[ 26/173] libselinux-0:3.9-5.fc43.x86_6 100% |  19.1 MiB/s |  97.7 KiB |  00m00s
[ 27/173] libeconf-0:0.7.9-2.fc43.x86_6 100% |  17.2 MiB/s |  35.2 KiB |  00m00s
[ 28/173] ed-0:1.22.2-1.fc43.x86_64     100% |  20.4 MiB/s |  83.7 KiB |  00m00s
[ 29/173] libsemanage-0:3.9-4.fc43.x86_ 100% |  40.2 MiB/s | 123.5 KiB |  00m00s
[ 30/173] setup-0:2.15.0-26.fc43.noarch 100% |  51.2 MiB/s | 157.3 KiB |  00m00s
[ 31/173] gmp-1:6.3.0-4.fc43.x86_64     100% |  78.0 MiB/s | 319.3 KiB |  00m00s
[ 32/173] mpfr-0:4.2.2-2.fc43.x86_64    100% |  67.8 MiB/s | 347.0 KiB |  00m00s
[ 33/173] readline-0:8.3-2.fc43.x86_64  100% |  27.4 MiB/s | 224.6 KiB |  00m00s
[ 34/173] coreutils-common-0:9.7-10.fc4 100% | 150.0 MiB/s |   2.1 MiB |  00m00s
[ 35/173] glibc-common-0:2.42-17.fc43.x 100% |  36.2 MiB/s | 333.9 KiB |  00m00s
[ 36/173] ansible-srpm-macros-0:1-18.1. 100% |   4.9 MiB/s |  19.9 KiB |  00m00s
[ 37/173] dwz-0:0.16-2.fc43.x86_64      100% |  44.1 MiB/s | 135.5 KiB |  00m00s
[ 38/173] efi-srpm-macros-0:6-4.fc43.no 100% |   7.3 MiB/s |  22.4 KiB |  00m00s
[ 39/173] filesystem-srpm-macros-0:3.18 100% |   8.6 MiB/s |  26.4 KiB |  00m00s
[ 40/173] fonts-srpm-macros-1:2.0.5-23. 100% |   8.8 MiB/s |  27.2 KiB |  00m00s
[ 41/173] fpc-srpm-macros-0:1.3-15.fc43 100% |   2.6 MiB/s |   7.9 KiB |  00m00s
[ 42/173] forge-srpm-macros-0:0.4.0-3.f 100% |   6.5 MiB/s |  20.1 KiB |  00m00s
[ 43/173] gap-srpm-macros-0:2-1.fc43.no 100% |   4.4 MiB/s |   9.0 KiB |  00m00s
[ 44/173] ghc-srpm-macros-0:1.9.2-3.fc4 100% |   2.8 MiB/s |   8.7 KiB |  00m00s
[ 45/173] go-srpm-macros-0:3.8.0-1.fc43 100% |   9.2 MiB/s |  28.3 KiB |  00m00s
[ 46/173] kernel-srpm-macros-0:1.0-27.f 100% |   4.4 MiB/s |   8.9 KiB |  00m00s
[ 47/173] java-srpm-macros-0:1-7.fc43.n 100% |   2.6 MiB/s |   7.9 KiB |  00m00s
[ 48/173] lua-srpm-macros-0:1-16.fc43.n 100% |   2.9 MiB/s |   8.8 KiB |  00m00s
[ 49/173] openblas-srpm-macros-0:2-20.f 100% |   2.5 MiB/s |   7.6 KiB |  00m00s
[ 50/173] ocaml-srpm-macros-0:11-2.fc43 100% |   1.8 MiB/s |   9.3 KiB |  00m00s
[ 51/173] perl-srpm-macros-0:1-60.fc43. 100% |   1.6 MiB/s |   8.3 KiB |  00m00s
[ 52/173] python-srpm-macros-0:3.14-5.f 100% |   5.7 MiB/s |  23.4 KiB |  00m00s
[ 53/173] zig-srpm-macros-0:1-5.fc43.no 100% |   4.1 MiB/s |   8.4 KiB |  00m00s
[ 54/173] tree-sitter-srpm-macros-0:0.4 100% |   4.3 MiB/s |  13.4 KiB |  00m00s
[ 55/173] zip-0:3.0-44.fc43.x86_64      100% |  51.1 MiB/s | 261.6 KiB |  00m00s
[ 56/173] cmake-srpm-macros-0:3.31.11-1 100% |   2.4 MiB/s |   9.9 KiB |  00m00s
[ 57/173] gnome-srpm-macros-0:1.0-1.fc4 100% |   2.1 MiB/s |   8.6 KiB |  00m00s
[ 58/173] debugedit-0:5.2-3.fc43.x86_64 100% |  16.7 MiB/s |  85.6 KiB |  00m00s
[ 59/173] popt-0:1.19-9.fc43.x86_64     100% |  12.8 MiB/s |  65.7 KiB |  00m00s
[ 60/173] zstd-0:1.5.7-2.fc43.x86_64    100% |  79.1 MiB/s | 485.9 KiB |  00m00s
[ 61/173] util-linux-core-0:2.41.6-1.fc 100% | 107.8 MiB/s | 551.9 KiB |  00m00s
[ 62/173] rpm-0:6.0.2-1.fc43.x86_64     100% |  80.1 MiB/s | 574.0 KiB |  00m00s
[ 63/173] xz-libs-1:5.8.1-4.fc43.x86_64 100% |  18.4 MiB/s | 112.8 KiB |  00m00s
[ 64/173] ncurses-base-0:6.5-7.20250614 100% |  28.7 MiB/s |  88.2 KiB |  00m00s
[ 65/173] libsepol-0:3.9-2.fc43.x86_64  100% |  84.3 MiB/s | 345.4 KiB |  00m00s
[ 66/173] gnulib-l10n-0:20241231-1.fc43 100% |  29.3 MiB/s | 150.2 KiB |  00m00s
[ 67/173] lz4-libs-0:1.10.0-3.fc43.x86_ 100% |  19.0 MiB/s |  78.0 KiB |  00m00s
[ 68/173] rpm-libs-0:6.0.2-1.fc43.x86_6 100% |  77.8 MiB/s | 398.4 KiB |  00m00s
[ 69/173] glibc-0:2.42-17.fc43.x86_64   100% | 122.7 MiB/s |   2.2 MiB |  00m00s
[ 70/173] libzstd-0:1.5.7-2.fc43.x86_64 100% |  23.6 MiB/s | 314.6 KiB |  00m00s
[ 71/173] glibc-gconv-extra-0:2.42-17.f 100% |  83.6 MiB/s |   1.6 MiB |  00m00s
[ 72/173] ima-evm-utils-libs-0:1.6.2-6. 100% | 837.8 KiB/s |  29.3 KiB |  00m00s
[ 73/173] rpm-sign-libs-0:6.0.2-1.fc43. 100% | 688.7 KiB/s |  25.5 KiB |  00m00s
[ 74/173] rpm-build-libs-0:6.0.2-1.fc43 100% |   3.2 MiB/s | 124.1 KiB |  00m00s
[ 75/173] libfsverity-0:1.6-3.fc43.x86_ 100% |   3.6 MiB/s |  18.6 KiB |  00m00s
[ 76/173] json-c-0:0.18-7.fc43.x86_64   100% |  11.0 MiB/s |  45.0 KiB |  00m00s
[ 77/173] tpm2-tss-0:4.1.3-8.fc43.x86_6 100% |  69.3 MiB/s | 425.9 KiB |  00m00s
[ 78/173] libblkid-0:2.41.6-1.fc43.x86_ 100% |  30.3 MiB/s | 124.1 KiB |  00m00s
[ 79/173] libuuid-0:2.41.6-1.fc43.x86_6 100% |   6.6 MiB/s |  26.9 KiB |  00m00s
[ 80/173] libmount-0:2.41.6-1.fc43.x86_ 100% |  26.8 MiB/s | 164.5 KiB |  00m00s
[ 81/173] libgcc-0:15.3.1-1.fc43.x86_64 100% |  34.9 MiB/s | 142.9 KiB |  00m00s
[ 82/173] libsmartcols-0:2.41.6-1.fc43. 100% |  16.5 MiB/s |  84.6 KiB |  00m00s
[ 83/173] libxcrypt-0:4.5.2-1.fc43.x86_ 100% |  25.0 MiB/s | 128.1 KiB |  00m00s
[ 84/173] audit-libs-0:4.2.1-1.fc43.x86 100% |  34.1 MiB/s | 139.6 KiB |  00m00s
[ 85/173] systemd-libs-0:258.11-1.fc43. 100% | 116.1 MiB/s | 832.0 KiB |  00m00s
[ 86/173] libcap-ng-0:0.9.6-1.fc43.x86_ 100% |   8.4 MiB/s |  34.3 KiB |  00m00s
[ 87/173] libfdisk-0:2.41.6-1.fc43.x86_ 100% |  39.6 MiB/s | 162.3 KiB |  00m00s
[ 88/173] liblastlog2-0:2.41.6-1.fc43.x 100% |   7.9 MiB/s |  24.3 KiB |  00m00s
[ 89/173] pam-libs-0:1.7.1-5.fc43.x86_6 100% |  18.4 MiB/s |  56.4 KiB |  00m00s
[ 90/173] zlib-ng-compat-0:2.3.3-2.fc43 100% |  21.7 MiB/s |  88.7 KiB |  00m00s
[ 91/173] libcap-0:2.76-4.fc43.x86_64   100% |  16.8 MiB/s |  86.1 KiB |  00m00s
[ 92/173] gpgverify-0:2.2-3.fc43.noarch 100% |   2.7 MiB/s |  11.1 KiB |  00m00s
[ 93/173] libassuan-0:2.5.7-4.fc43.x86_ 100% |  16.5 MiB/s |  67.4 KiB |  00m00s
[ 94/173] gnupg2-0:2.4.9-5.fc43.x86_64  100% | 148.8 MiB/s |   1.6 MiB |  00m00s
[ 95/173] npth-0:1.8-3.fc43.x86_64      100% |   8.4 MiB/s |  25.7 KiB |  00m00s
[ 96/173] libgpg-error-0:1.55-2.fc43.x8 100% |  39.8 MiB/s | 244.3 KiB |  00m00s
[ 97/173] gnupg2-dirmngr-0:2.4.9-5.fc43 100% |  53.7 MiB/s | 275.0 KiB |  00m00s
[ 98/173] gnupg2-gpg-agent-0:2.4.9-5.fc 100% |  66.7 MiB/s | 273.4 KiB |  00m00s
[ 99/173] gnupg2-gpgconf-0:2.4.9-5.fc43 100% |  28.2 MiB/s | 115.3 KiB |  00m00s
[100/173] gnupg2-keyboxd-0:2.4.9-5.fc43 100% |  23.1 MiB/s |  94.7 KiB |  00m00s
[101/173] gnupg2-verify-0:2.4.9-5.fc43. 100% |  55.8 MiB/s | 171.6 KiB |  00m00s
[102/173] libksba-0:1.6.7-4.fc43.x86_64 100% |  39.2 MiB/s | 160.4 KiB |  00m00s
[103/173] libacl-0:2.4.0-1.fc43.x86_64  100% |  12.7 MiB/s |  26.1 KiB |  00m00s
[104/173] lua-libs-0:5.4.8-4.fc43.x86_6 100% |  32.5 MiB/s | 133.1 KiB |  00m00s
[105/173] libstdc++-0:15.3.1-1.fc43.x86 100% | 151.5 MiB/s | 931.1 KiB |  00m00s
[106/173] rpm-sequoia-0:1.10.3-1.fc43.x 100% | 114.8 MiB/s | 940.6 KiB |  00m00s
[107/173] sqlite-libs-0:3.50.2-3.fc43.x 100% | 105.9 MiB/s | 758.9 KiB |  00m00s
[108/173] elfutils-libelf-0:0.197-1.fc4 100% |  42.7 MiB/s | 262.6 KiB |  00m00s
[109/173] elfutils-libs-0:0.197-1.fc43. 100% |  66.7 MiB/s | 273.0 KiB |  00m00s
[110/173] elfutils-debuginfod-client-0: 100% |  15.1 MiB/s |  46.3 KiB |  00m00s
[111/173] elfutils-0:0.197-1.fc43.x86_6 100% | 128.1 MiB/s | 655.7 KiB |  00m00s
[112/173] file-0:5.46-9.fc43.x86_64     100% |  15.8 MiB/s |  48.5 KiB |  00m00s
[113/173] file-libs-0:5.46-9.fc43.x86_6 100% | 138.3 MiB/s | 849.9 KiB |  00m00s
[114/173] libgomp-0:15.3.1-1.fc43.x86_6 100% |  74.9 MiB/s | 383.6 KiB |  00m00s
[115/173] jansson-0:2.14-3.fc43.x86_64  100% |  14.7 MiB/s |  45.3 KiB |  00m00s
[116/173] libarchive-0:3.8.4-1.fc43.x86 100% |  51.6 MiB/s | 422.6 KiB |  00m00s
[117/173] libxml2-0:2.12.10-5.fc43.x86_ 100% | 112.7 MiB/s | 692.7 KiB |  00m00s
[118/173] pkgconf-pkg-config-0:2.3.0-3. 100% |   2.3 MiB/s |   9.6 KiB |  00m00s
[119/173] pkgconf-0:2.3.0-3.fc43.x86_64 100% |  10.9 MiB/s |  44.6 KiB |  00m00s
[120/173] pkgconf-m4-0:2.3.0-3.fc43.noa 100% |   4.5 MiB/s |  13.9 KiB |  00m00s
[121/173] libpkgconf-0:2.3.0-3.fc43.x86 100% |   7.4 MiB/s |  37.9 KiB |  00m00s
[122/173] binutils-0:2.45.1-5.fc43.x86_ 100% | 185.5 MiB/s |   5.9 MiB |  00m00s
[123/173] build-reproducibility-srpm-ma 100% |   1.5 MiB/s |  11.9 KiB |  00m00s
[124/173] curl-0:8.15.0-12.fc43.x86_64  100% |  20.1 MiB/s | 226.8 KiB |  00m00s
[125/173] gnat-srpm-macros-0:7-1.fc43.n 100% | 721.4 KiB/s |   8.7 KiB |  00m00s
[126/173] package-notes-srpm-macros-0:0 100% | 821.1 KiB/s |   9.9 KiB |  00m00s
[127/173] add-determinism-0:0.6.0-3.fc4 100% |  43.6 MiB/s | 893.7 KiB |  00m00s
[128/173] pyproject-srpm-macros-0:1.24. 100% |   2.1 MiB/s |  15.4 KiB |  00m00s
[129/173] qt5-srpm-macros-0:5.15.18-1.f 100% |   1.2 MiB/s |   8.6 KiB |  00m00s
[130/173] qt6-srpm-macros-0:6.10.3-1.fc 100% |   4.5 MiB/s |   9.1 KiB |  00m00s
[131/173] rust-srpm-macros-0:28.5-1.fc4 100% |   5.5 MiB/s |  11.2 KiB |  00m00s
[132/173] libgcrypt-0:1.11.1-4.fc43.x86 100% |  58.2 MiB/s | 596.0 KiB |  00m00s
[133/173] gnutls-0:3.8.13-1.fc43.x86_64 100% |  92.6 MiB/s |   1.4 MiB |  00m00s
[134/173] openssl-libs-1:3.5.9-1.fc43.x 100% | 125.2 MiB/s |   2.6 MiB |  00m00s
[135/173] libidn2-0:2.3.8-2.fc43.x86_64 100% |  21.3 MiB/s | 174.9 KiB |  00m00s
[136/173] libunistring-0:1.1-10.fc43.x8 100% | 106.0 MiB/s | 542.9 KiB |  00m00s
[137/173] nettle-0:3.10.1-2.fc43.x86_64 100% |  69.0 MiB/s | 424.2 KiB |  00m00s
[138/173] openldap-0:2.6.15-1.fc43.x86_ 100% |  36.2 MiB/s | 259.7 KiB |  00m00s
[139/173] cyrus-sasl-lib-0:2.1.28-33.fc 100% | 109.9 MiB/s | 787.9 KiB |  00m00s
[140/173] gdbm-libs-1:1.23-10.fc43.x86_ 100% |  13.9 MiB/s |  56.8 KiB |  00m00s
[141/173] fedora-repos-0:43-2.noarch    100% |   2.2 MiB/s |   8.9 KiB |  00m00s
[142/173] elfutils-default-yama-scope-0 100% |   5.6 MiB/s |  11.5 KiB |  00m00s
[143/173] fedora-gpg-keys-0:43-2.noarch 100% |  45.0 MiB/s | 184.2 KiB |  00m00s
[144/173] libpfm-0:4.14.1-2.fc43.x86_64 100% |  67.2 MiB/s | 275.3 KiB |  00m00s
[145/173] libattr-0:2.6.0-1.fc43.x86_64 100% |   8.7 MiB/s |  17.9 KiB |  00m00s
[146/173] alternatives-0:1.33-3.fc43.x8 100% |   6.6 MiB/s |  40.6 KiB |  00m00s
[147/173] libusb1-0:1.0.30-1.fc43.x86_6 100% |  13.1 MiB/s |  80.5 KiB |  00m00s
[148/173] pcre2-0:10.47-1.fc43.x86_64   100% |  37.3 MiB/s | 267.2 KiB |  00m00s
[149/173] pcre2-syntax-0:10.47-1.fc43.n 100% |  40.2 MiB/s | 164.7 KiB |  00m00s
[150/173] ca-certificates-0:2026.2.92_v 100% | 136.7 MiB/s | 980.2 KiB |  00m00s
[151/173] crypto-policies-0:20251125-1. 100% |  15.9 MiB/s |  97.8 KiB |  00m00s
[152/173] libevent-0:2.1.13-1.fc43.x86_ 100% |  50.7 MiB/s | 259.5 KiB |  00m00s
[153/173] libtool-ltdl-0:2.5.4-8.fc43.x 100% |  17.7 MiB/s |  36.2 KiB |  00m00s
[154/173] libtasn1-0:4.21.0-1.fc43.x86_ 100% |  24.3 MiB/s |  74.5 KiB |  00m00s
[155/173] p11-kit-trust-0:0.26.5-1.fc43 100% |  27.5 MiB/s | 140.6 KiB |  00m00s
[156/173] p11-kit-0:0.26.5-1.fc43.x86_6 100% |  66.3 MiB/s | 542.8 KiB |  00m00s
[157/173] libffi-0:3.5.2-1.fc43.x86_64  100% |   6.7 MiB/s |  41.1 KiB |  00m00s
[158/173] systemd-standalone-sysusers-0 100% |  24.3 MiB/s | 149.4 KiB |  00m00s
[159/173] xxhash-libs-0:0.8.4-2.fc43.x8 100% |   7.5 MiB/s |  38.6 KiB |  00m00s
[160/173] fedora-release-0:43-27.noarch 100% |   3.8 MiB/s |  11.5 KiB |  00m00s
[161/173] fedora-release-identity-basic 100% |   4.0 MiB/s |  12.3 KiB |  00m00s
[162/173] libcurl-0:8.15.0-12.fc43.x86_ 100% |  78.9 MiB/s | 403.9 KiB |  00m00s
[163/173] libpsl-0:0.21.5-6.fc43.x86_64 100% |  12.7 MiB/s |  65.0 KiB |  00m00s
[164/173] gdb-minimal-0:18.1-1.fc43.x86 100% | 196.2 MiB/s |   4.5 MiB |  00m00s
[165/173] libssh-config-0:0.11.5-1.fc43 100% |   1.1 MiB/s |   8.9 KiB |  00m00s
[166/173] libssh-0:0.11.5-1.fc43.x86_64 100% |  28.6 MiB/s | 234.6 KiB |  00m00s
[167/173] publicsuffix-list-dafsa-0:202 100% |  14.7 MiB/s |  60.3 KiB |  00m00s
[168/173] keyutils-libs-0:1.6.3-6.fc43. 100% |   7.7 MiB/s |  31.4 KiB |  00m00s
[169/173] krb5-libs-0:1.22.2-4.fc43.x86 100% |  94.0 MiB/s | 770.4 KiB |  00m00s
[170/173] libcom_err-0:1.47.3-2.fc43.x8 100% |   6.5 MiB/s |  26.8 KiB |  00m00s
[171/173] libverto-0:0.3.2-11.fc43.x86_ 100% |   5.0 MiB/s |  20.7 KiB |  00m00s
[172/173] libbrotli-0:1.2.1-1.fc43.x86_ 100% |  85.3 MiB/s | 349.5 KiB |  00m00s
[173/173] libnghttp2-0:1.66.0-3.fc43.x8 100% |  17.3 MiB/s |  71.0 KiB |  00m00s
--------------------------------------------------------------------------------
[173/173] Total                         100% | 141.8 MiB/s |  59.9 MiB |  00m00s
Running transaction
Importing OpenPGP key 0x31645531:
 UserID     : "Fedora (43) <fedora-43-primary@fedoraproject.org>"
 Fingerprint: C6E7F081CF80E13146676E88829B606631645531
 From       : file:///usr/share/distribution-gpg-keys/fedora/RPM-GPG-KEY-fedora-43-primary
The key was successfully imported.
[  1/175] Verify package files          100% | 789.0   B/s | 173.0   B |  00m00s
[  2/175] Prepare transaction           100% |   4.3 KiB/s | 173.0   B |  00m00s
[  3/175] Installing libgcc-0:15.3.1-1. 100% | 262.0 MiB/s | 268.3 KiB |  00m00s
[  4/175] Installing publicsuffix-list- 100% |   0.0   B/s |  71.1 KiB |  00m00s
[  5/175] Installing libssh-config-0:0. 100% |   0.0   B/s | 816.0   B |  00m00s
[  6/175] Installing fedora-release-ide 100% | 867.2 KiB/s | 888.0   B |  00m00s
[  7/175] Installing fedora-gpg-keys-0: 100% |  57.8 MiB/s | 355.0 KiB |  00m00s
[  8/175] Installing fedora-repos-0:43- 100% |   0.0   B/s |   5.7 KiB |  00m00s
[  9/175] Installing fedora-release-com 100% |  24.3 MiB/s |  24.8 KiB |  00m00s
[ 10/175] Installing fedora-release-0:4 100% |  15.1 KiB/s | 124.0   B |  00m00s
>>> Running sysusers scriptlet: setup-0:2.15.0-26.fc43.noarch                   
>>> Finished sysusers scriptlet: setup-0:2.15.0-26.fc43.noarch                  
>>> Scriptlet output:                                                           
>>> Creating group 'adm' with GID 4.                                            
>>> Creating group 'audio' with GID 63.                                         
>>> Creating group 'cdrom' with GID 11.                                         
>>> Creating group 'clock' with GID 103.                                        
>>> Creating group 'dialout' with GID 18.                                       
>>> Creating group 'disk' with GID 6.                                           
>>> Creating group 'floppy' with GID 19.                                        
>>> Creating group 'ftp' with GID 50.                                           
>>> Creating group 'games' with GID 20.                                         
>>> Creating group 'input' with GID 104.                                        
>>> Creating group 'kmem' with GID 9.                                           
>>> Creating group 'kvm' with GID 36.                                           
>>> Creating group 'lock' with GID 54.                                          
>>> Creating group 'lp' with GID 7.                                             
>>> Creating group 'mail' with GID 12.                                          
>>> Creating group 'man' with GID 15.                                           
>>> Creating group 'mem' with GID 8.                                            
>>> Creating group 'nobody' with GID 65534.                                     
>>> Creating group 'render' with GID 105.                                       
>>> Creating group 'root' with GID 0.                                           
>>> Creating group 'sgx' with GID 106.                                          
>>> Creating group 'sys' with GID 3.                                            
>>> Creating group 'tape' with GID 33.                                          
>>> Creating group 'tty' with GID 5.                                            
>>> Creating group 'users' with GID 100.                                        
>>> Creating group 'utmp' with GID 22.                                          
>>> Creating group 'video' with GID 39.                                         
>>> Creating group 'wheel' with GID 10.                                         
>>> Creating user 'adm' (adm) with UID 3 and GID 4.                             
>>> Creating group 'bin' with GID 1.                                            
>>> Creating user 'bin' (bin) with UID 1 and GID 1.                             
>>> Creating group 'daemon' with GID 2.                                         
>>> Creating user 'daemon' (daemon) with UID 2 and GID 2.                       
>>> Creating user 'ftp' (FTP User) with UID 14 and GID 50.                      
>>> Creating user 'games' (games) with UID 12 and GID 100.                      
>>> Creating user 'halt' (halt) with UID 7 and GID 0.                           
>>> Creating user 'lp' (lp) with UID 4 and GID 7.                               
>>> Creating user 'mail' (mail) with UID 8 and GID 12.                          
>>> Creating user 'nobody' (Kernel Overflow User) with UID 65534 and GID 65534. 
>>> Creating user 'operator' (operator) with UID 11 and GID 0.                  
>>> Creating user 'root' (Super User) with UID 0 and GID 0.                     
>>> Creating user 'shutdown' (shutdown) with UID 6 and GID 0.                   
>>> Creating user 'sync' (sync) with UID 5 and GID 0.                           
>>>                                                                             
[ 11/175] Installing setup-0:2.15.0-26. 100% |  51.0 MiB/s | 730.6 KiB |  00m00s
>>> [RPM] /etc/hosts created as /etc/hosts.rpmnew                               
[ 12/175] Installing filesystem-0:3.18- 100% |   3.1 MiB/s | 212.8 KiB |  00m00s
[ 13/175] Installing pcre2-syntax-0:10. 100% | 277.7 MiB/s | 284.3 KiB |  00m00s
[ 14/175] Installing rust-srpm-macros-0 100% |   0.0   B/s |   6.4 KiB |  00m00s
[ 15/175] Installing qt6-srpm-macros-0: 100% |   0.0   B/s | 748.0   B |  00m00s
[ 16/175] Installing qt5-srpm-macros-0: 100% |   0.0   B/s | 776.0   B |  00m00s
[ 17/175] Installing package-notes-srpm 100% |   0.0   B/s |   2.1 KiB |  00m00s
[ 18/175] Installing gnat-srpm-macros-0 100% |   0.0   B/s |   1.3 KiB |  00m00s
[ 19/175] Installing pkgconf-m4-0:2.3.0 100% |   0.0   B/s |  14.8 KiB |  00m00s
[ 20/175] Installing gnulib-l10n-0:2024 100% | 215.5 MiB/s | 661.9 KiB |  00m00s
[ 21/175] Installing coreutils-common-0 100% | 418.2 MiB/s |  11.3 MiB |  00m00s
[ 22/175] Installing ncurses-base-0:6.5 100% |  86.3 MiB/s | 353.5 KiB |  00m00s
[ 23/175] Installing bash-0:5.3.0-2.fc4 100% | 281.0 MiB/s |   8.4 MiB |  00m00s
[ 24/175] Installing glibc-common-0:2.4 100% |  63.7 MiB/s |   1.0 MiB |  00m00s
[ 25/175] Installing glibc-gconv-extra- 100% | 303.3 MiB/s |   7.3 MiB |  00m00s
[ 26/175] Installing glibc-0:2.42-17.fc 100% | 191.2 MiB/s |   6.7 MiB |  00m00s
[ 27/175] Installing ncurses-libs-0:6.5 100% | 310.1 MiB/s | 952.8 KiB |  00m00s
[ 28/175] Installing glibc-minimal-lang 100% |   0.0   B/s | 124.0   B |  00m00s
[ 29/175] Installing zlib-ng-compat-0:2 100% | 158.7 MiB/s | 162.6 KiB |  00m00s
[ 30/175] Installing bzip2-libs-0:1.0.8 100% |  79.8 MiB/s |  81.7 KiB |  00m00s
[ 31/175] Installing libgpg-error-0:1.5 100% |  64.3 MiB/s | 921.1 KiB |  00m00s
[ 32/175] Installing libstdc++-0:15.3.1 100% | 407.0 MiB/s |   2.8 MiB |  00m00s
[ 33/175] Installing xz-libs-1:5.8.1-4. 100% | 213.8 MiB/s | 218.9 KiB |  00m00s
[ 34/175] Installing libassuan-0:2.5.7- 100% | 161.7 MiB/s | 165.6 KiB |  00m00s
[ 35/175] Installing libgcrypt-0:1.11.1 100% | 393.7 MiB/s |   1.6 MiB |  00m00s
[ 36/175] Installing readline-0:8.3-2.f 100% | 250.9 MiB/s | 513.9 KiB |  00m00s
[ 37/175] Installing gmp-1:6.3.0-4.fc43 100% | 397.2 MiB/s | 813.5 KiB |  00m00s
[ 38/175] Installing libuuid-0:2.41.6-1 100% |   0.0   B/s |  38.4 KiB |  00m00s
[ 39/175] Installing libacl-0:2.4.0-1.f 100% |   0.0   B/s |  40.7 KiB |  00m00s
[ 40/175] Installing popt-0:1.19-9.fc43 100% |  68.1 MiB/s | 139.4 KiB |  00m00s
[ 41/175] Installing libzstd-0:1.5.7-2. 100% | 391.2 MiB/s | 801.1 KiB |  00m00s
[ 42/175] Installing elfutils-libelf-0: 100% | 542.8 MiB/s |   1.6 MiB |  00m00s
[ 43/175] Installing npth-0:1.8-3.fc43. 100% |   0.0   B/s |  50.7 KiB |  00m00s
[ 44/175] Installing libblkid-0:2.41.6- 100% | 257.3 MiB/s | 263.5 KiB |  00m00s
[ 45/175] Installing libxcrypt-0:4.5.2- 100% | 281.3 MiB/s | 288.0 KiB |  00m00s
[ 46/175] Installing sqlite-libs-0:3.50 100% | 379.1 MiB/s |   1.5 MiB |  00m00s
[ 47/175] Installing gnupg2-gpgconf-0:2 100% |  20.5 MiB/s | 252.1 KiB |  00m00s
[ 48/175] Installing json-c-0:0.18-7.fc 100% |   0.0   B/s |  84.0 KiB |  00m00s
[ 49/175] Installing libunistring-0:1.1 100% | 345.3 MiB/s |   1.7 MiB |  00m00s
[ 50/175] Installing libidn2-0:2.3.8-2. 100% | 181.9 MiB/s | 558.7 KiB |  00m00s
[ 51/175] Installing libtasn1-0:4.21.0- 100% |  21.8 MiB/s | 178.6 KiB |  00m00s
[ 52/175] Installing crypto-policies-0: 100% |  42.0 MiB/s | 172.2 KiB |  00m00s
[ 53/175] Installing gnupg2-verify-0:2. 100% |  26.3 MiB/s | 349.9 KiB |  00m00s
[ 54/175] Installing dwz-0:0.16-2.fc43. 100% |  23.5 MiB/s | 288.5 KiB |  00m00s
[ 55/175] Installing mpfr-0:4.2.2-2.fc4 100% | 271.6 MiB/s | 834.4 KiB |  00m00s
[ 56/175] Installing gawk-0:5.3.2-2.fc4 100% | 106.8 MiB/s |   1.8 MiB |  00m00s
[ 57/175] Installing libksba-0:1.6.7-4. 100% | 391.7 MiB/s | 401.1 KiB |  00m00s
[ 58/175] Installing unzip-0:6.0-67.fc4 100% |  31.7 MiB/s | 389.8 KiB |  00m00s
[ 59/175] Installing file-libs-0:5.46-9 100% | 697.7 MiB/s |  11.9 MiB |  00m00s
[ 60/175] Installing file-0:5.46-9.fc43 100% |   8.3 MiB/s | 101.7 KiB |  00m00s
[ 61/175] Installing libeconf-0:0.7.9-2 100% |  65.0 MiB/s |  66.5 KiB |  00m00s
[ 62/175] Installing libsepol-0:3.9-2.f 100% | 401.8 MiB/s | 822.9 KiB |  00m00s
[ 63/175] Installing lz4-libs-0:1.10.0- 100% | 158.6 MiB/s | 162.5 KiB |  00m00s
[ 64/175] Installing libsmartcols-0:2.4 100% | 177.2 MiB/s | 181.5 KiB |  00m00s
[ 65/175] Installing libcap-ng-0:0.9.6- 100% |  69.0 MiB/s |  70.6 KiB |  00m00s
[ 66/175] Installing audit-libs-0:4.2.1 100% | 375.9 MiB/s | 384.9 KiB |  00m00s
[ 67/175] Installing pam-libs-0:1.7.1-5 100% | 125.7 MiB/s | 128.8 KiB |  00m00s
[ 68/175] Installing libcap-0:2.76-4.fc 100% |  17.4 MiB/s | 214.3 KiB |  00m00s
[ 69/175] Installing systemd-libs-0:258 100% | 391.3 MiB/s |   2.3 MiB |  00m00s
[ 70/175] Installing lua-libs-0:5.4.8-4 100% | 276.7 MiB/s | 283.3 KiB |  00m00s
[ 71/175] Installing libattr-0:2.6.0-1. 100% |   0.0   B/s |  25.2 KiB |  00m00s
[ 72/175] Installing alternatives-0:1.3 100% |   5.7 MiB/s |  63.8 KiB |  00m00s
[ 73/175] Installing pcre2-0:10.47-1.fc 100% | 343.8 MiB/s | 704.1 KiB |  00m00s
[ 74/175] Installing libselinux-0:3.9-5 100% | 189.8 MiB/s | 194.4 KiB |  00m00s
[ 75/175] Installing grep-0:3.12-2.fc43 100% |  66.8 MiB/s |   1.0 MiB |  00m00s
[ 76/175] Installing findutils-1:4.10.0 100% | 116.1 MiB/s |   1.9 MiB |  00m00s
[ 77/175] Installing sed-0:4.9-5.fc43.x 100% |  60.4 MiB/s | 865.5 KiB |  00m00s
[ 78/175] Installing xz-1:5.8.1-4.fc43. 100% |  83.2 MiB/s |   1.3 MiB |  00m00s
[ 79/175] Installing libmount-0:2.41.6- 100% | 368.9 MiB/s | 377.8 KiB |  00m00s
[ 80/175] Installing libffi-0:3.5.2-1.f 100% |  83.2 MiB/s |  85.2 KiB |  00m00s
[ 81/175] Installing p11-kit-0:0.26.5-1 100% | 141.4 MiB/s |   2.5 MiB |  00m00s
[ 82/175] Installing p11-kit-trust-0:0. 100% |  22.8 MiB/s | 467.9 KiB |  00m00s
[ 83/175] Installing openssl-libs-1:3.5 100% | 406.5 MiB/s |   8.9 MiB |  00m00s
[ 84/175] Installing coreutils-0:9.7-10 100% | 169.7 MiB/s |   5.4 MiB |  00m00s
[ 85/175] Installing ca-certificates-0: 100% |   2.4 MiB/s |   2.5 MiB |  00m01s
[ 86/175] Installing gzip-0:1.13-4.fc43 100% |  29.6 MiB/s | 394.4 KiB |  00m00s
[ 87/175] Installing libfsverity-0:1.6- 100% |   0.0   B/s |  29.5 KiB |  00m00s
[ 88/175] Installing rpm-sequoia-0:1.10 100% | 350.2 MiB/s |   2.5 MiB |  00m00s
[ 89/175] Installing libevent-0:2.1.13- 100% | 289.1 MiB/s | 888.2 KiB |  00m00s
[ 90/175] Installing util-linux-core-0: 100% |  86.8 MiB/s |   1.5 MiB |  00m00s
[ 91/175] Installing tar-2:1.35-6.fc43. 100% | 155.7 MiB/s |   3.0 MiB |  00m00s
[ 92/175] Installing libsemanage-0:3.9- 100% | 303.0 MiB/s | 310.2 KiB |  00m00s
[ 93/175] Installing systemd-standalone 100% |  24.3 MiB/s | 298.1 KiB |  00m00s
[ 94/175] Installing rpm-libs-0:6.0.2-1 100% | 304.4 MiB/s | 935.1 KiB |  00m00s
[ 95/175] Installing libusb1-0:1.0.30-1 100% |  21.7 MiB/s | 177.7 KiB |  00m00s
>>> Running sysusers scriptlet: tpm2-tss-0:4.1.3-8.fc43.x86_64                  
>>> Finished sysusers scriptlet: tpm2-tss-0:4.1.3-8.fc43.x86_64                 
>>> Scriptlet output:                                                           
>>> Creating group 'tss' with GID 59.                                           
>>> Creating user 'tss' (Account used for TPM access) with UID 59 and GID 59.   
>>>                                                                             
[ 96/175] Installing tpm2-tss-0:4.1.3-8 100% | 314.4 MiB/s |   1.6 MiB |  00m00s
[ 97/175] Installing ima-evm-utils-libs 100% |  60.5 MiB/s |  62.0 KiB |  00m00s
[ 98/175] Installing gnupg2-gpg-agent-0 100% |  31.4 MiB/s | 675.4 KiB |  00m00s
[ 99/175] Installing liblastlog2-0:2.41 100% |   6.5 MiB/s |  39.8 KiB |  00m00s
[100/175] Installing zstd-0:1.5.7-2.fc4 100% | 106.9 MiB/s |   1.7 MiB |  00m00s
[101/175] Installing zip-0:3.0-44.fc43. 100% |  52.5 MiB/s | 698.4 KiB |  00m00s
[102/175] Installing gnupg2-keyboxd-0:2 100% |  28.3 MiB/s | 202.7 KiB |  00m00s
[103/175] Installing libpsl-0:0.21.5-6. 100% |  75.7 MiB/s |  77.5 KiB |  00m00s
[104/175] Installing libfdisk-0:2.41.6- 100% | 372.5 MiB/s | 381.4 KiB |  00m00s
[105/175] Installing nettle-0:3.10.1-2. 100% | 258.4 MiB/s | 793.7 KiB |  00m00s
[106/175] Installing gnutls-0:3.8.13-1. 100% | 376.7 MiB/s |   3.8 MiB |  00m00s
[107/175] Installing libxml2-0:2.12.10- 100% | 106.5 MiB/s |   1.7 MiB |  00m00s
[108/175] Installing libarchive-0:3.8.4 100% | 311.6 MiB/s | 957.2 KiB |  00m00s
[109/175] Installing bzip2-0:1.0.8-21.f 100% |   8.1 MiB/s |  99.8 KiB |  00m00s
[110/175] Installing add-determinism-0: 100% | 143.9 MiB/s |   2.3 MiB |  00m00s
[111/175] Installing build-reproducibil 100% |   0.0   B/s |   1.0 KiB |  00m00s
[112/175] Installing cpio-0:2.15-6.fc43 100% |  73.3 MiB/s |   1.1 MiB |  00m00s
[113/175] Installing diffutils-0:3.12-3 100% |  97.6 MiB/s |   1.6 MiB |  00m00s
[114/175] Installing ed-0:1.22.2-1.fc43 100% |  12.2 MiB/s | 150.4 KiB |  00m00s
[115/175] Installing patch-0:2.8-2.fc43 100% |  18.3 MiB/s | 224.3 KiB |  00m00s
[116/175] Installing libgomp-0:15.3.1-1 100% | 265.2 MiB/s | 543.2 KiB |  00m00s
[117/175] Installing jansson-0:2.14-3.f 100% |  88.3 MiB/s |  90.5 KiB |  00m00s
[118/175] Installing libpkgconf-0:2.3.0 100% |   0.0   B/s |  79.2 KiB |  00m00s
[119/175] Installing pkgconf-0:2.3.0-3. 100% |   7.4 MiB/s |  91.0 KiB |  00m00s
[120/175] Installing pkgconf-pkg-config 100% | 161.2 KiB/s |   1.8 KiB |  00m00s
[121/175] Installing gdbm-libs-1:1.23-1 100% | 128.5 MiB/s | 131.6 KiB |  00m00s
[122/175] Installing cyrus-sasl-lib-0:2 100% | 143.5 MiB/s |   2.3 MiB |  00m00s
[123/175] Installing libpfm-0:4.14.1-2. 100% | 592.1 MiB/s |   2.4 MiB |  00m00s
[124/175] Installing libtool-ltdl-0:2.5 100% |  69.6 MiB/s |  71.2 KiB |  00m00s
[125/175] Installing openldap-0:2.6.15- 100% | 323.7 MiB/s | 663.0 KiB |  00m00s
[126/175] Installing gnupg2-dirmngr-0:2 100% |  30.3 MiB/s | 621.1 KiB |  00m00s
[127/175] Installing gnupg2-0:2.4.9-5.f 100% | 234.5 MiB/s |   6.6 MiB |  00m00s
[128/175] Installing rpm-sign-libs-0:6. 100% |   0.0   B/s |  40.5 KiB |  00m00s
[129/175] Installing gpgverify-0:2.2-3. 100% |   0.0   B/s |   9.4 KiB |  00m00s
[130/175] Installing xxhash-libs-0:0.8. 100% |  89.2 MiB/s |  91.4 KiB |  00m00s
[131/175] Installing keyutils-libs-0:1. 100% |   0.0   B/s |  55.7 KiB |  00m00s
[132/175] Installing libcom_err-0:1.47. 100% |   0.0   B/s |  64.2 KiB |  00m00s
[133/175] Installing libverto-0:0.3.2-1 100% |  26.6 MiB/s |  27.2 KiB |  00m00s
[134/175] Installing krb5-libs-0:1.22.2 100% | 333.1 MiB/s |   2.3 MiB |  00m00s
[135/175] Installing libssh-0:0.11.5-1. 100% | 280.8 MiB/s | 575.1 KiB |  00m00s
[136/175] Installing libbrotli-0:1.2.1- 100% | 282.3 MiB/s | 867.3 KiB |  00m00s
[137/175] Installing libnghttp2-0:1.66. 100% | 159.4 MiB/s | 163.2 KiB |  00m00s
[138/175] Installing libcurl-0:8.15.0-1 100% | 295.6 MiB/s | 908.1 KiB |  00m00s
[139/175] Installing curl-0:8.15.0-12.f 100% |  20.6 MiB/s | 463.9 KiB |  00m00s
[140/175] Installing rpm-0:6.0.2-1.fc43 100% |  83.1 MiB/s |   2.6 MiB |  00m00s
[141/175] Installing efi-srpm-macros-0: 100% |  40.2 MiB/s |  41.1 KiB |  00m00s
[142/175] Installing java-srpm-macros-0 100% |   0.0   B/s |   1.1 KiB |  00m00s
[143/175] Installing lua-srpm-macros-0: 100% |   0.0   B/s |   1.9 KiB |  00m00s
[144/175] Installing tree-sitter-srpm-m 100% |   0.0   B/s |   9.3 KiB |  00m00s
[145/175] Installing zig-srpm-macros-0: 100% |   0.0   B/s |   1.7 KiB |  00m00s
[146/175] Installing cmake-srpm-macros- 100% |   0.0   B/s | 804.0   B |  00m00s
[147/175] Installing filesystem-srpm-ma 100% |   0.0   B/s |  38.9 KiB |  00m00s
[148/175] Installing elfutils-default-y 100% | 340.5 KiB/s |   2.0 KiB |  00m00s
[149/175] Installing elfutils-libs-0:0. 100% | 227.0 MiB/s | 697.2 KiB |  00m00s
[150/175] Installing elfutils-debuginfo 100% |   6.5 MiB/s |  86.0 KiB |  00m00s
[151/175] Installing elfutils-0:0.197-1 100% | 164.3 MiB/s |   3.1 MiB |  00m00s
[152/175] Installing binutils-0:2.45.1- 100% | 346.5 MiB/s |  27.4 MiB |  00m00s
[153/175] Installing gdb-minimal-0:18.1 100% | 294.1 MiB/s |  13.8 MiB |  00m00s
[154/175] Installing debugedit-0:5.2-3. 100% |  16.3 MiB/s | 217.3 KiB |  00m00s
[155/175] Installing rpm-build-libs-0:6 100% | 258.9 MiB/s | 265.1 KiB |  00m00s
[156/175] Installing gnome-srpm-macros- 100% |   0.0   B/s |   4.1 KiB |  00m00s
[157/175] Installing perl-srpm-macros-0 100% |   0.0   B/s |   1.1 KiB |  00m00s
[158/175] Installing openblas-srpm-macr 100% |   0.0   B/s | 392.0   B |  00m00s
[159/175] Installing ocaml-srpm-macros- 100% |   0.0   B/s |   2.1 KiB |  00m00s
[160/175] Installing kernel-srpm-macros 100% |   0.0   B/s |   2.3 KiB |  00m00s
[161/175] Installing ghc-srpm-macros-0: 100% |   0.0   B/s |   1.0 KiB |  00m00s
[162/175] Installing gap-srpm-macros-0: 100% |   0.0   B/s |   2.7 KiB |  00m00s
[163/175] Installing fpc-srpm-macros-0: 100% |   0.0   B/s | 420.0   B |  00m00s
[164/175] Installing ansible-srpm-macro 100% |   0.0   B/s |  36.2 KiB |  00m00s
[165/175] Installing rpm-build-0:6.0.2- 100% |  20.6 MiB/s | 295.6 KiB |  00m00s
[166/175] Installing pyproject-srpm-mac 100% |   0.0   B/s |   4.0 KiB |  00m00s
[167/175] Installing redhat-rpm-config- 100% | 184.7 MiB/s | 189.1 KiB |  00m00s
[168/175] Installing forge-srpm-macros- 100% |   0.0   B/s |  40.3 KiB |  00m00s
[169/175] Installing fonts-srpm-macros- 100% |   0.0   B/s |  57.0 KiB |  00m00s
[170/175] Installing go-srpm-macros-0:3 100% |   0.0   B/s |  63.0 KiB |  00m00s
[171/175] Installing python-srpm-macros 100% |  51.6 MiB/s |  52.8 KiB |  00m00s
[172/175] Installing util-linux-0:2.41. 100% | 101.8 MiB/s |   3.6 MiB |  00m00s
[173/175] Installing shadow-utils-2:4.1 100% | 147.0 MiB/s |   4.0 MiB |  00m00s
[174/175] Installing which-0:2.23-3.fc4 100% |   7.0 MiB/s |  85.7 KiB |  00m00s
[175/175] Installing info-0:7.2-7.fc43. 100% | 258.4 KiB/s | 354.3 KiB |  00m01s
Complete!
Finish: installing minimal buildroot with dnf5
Start: creating root cache
Finish: creating root cache
Finish: chroot init
INFO: Installed packages:
INFO: add-determinism-0.6.0-3.fc43.x86_64
alternatives-1.33-3.fc43.x86_64
ansible-srpm-macros-1-18.1.fc43.noarch
audit-libs-4.2.1-1.fc43.x86_64
bash-5.3.0-2.fc43.x86_64
binutils-2.45.1-5.fc43.x86_64
build-reproducibility-srpm-macros-0.6.0-3.fc43.noarch
bzip2-1.0.8-21.fc43.x86_64
bzip2-libs-1.0.8-21.fc43.x86_64
ca-certificates-2026.2.92_v9.0.319-2.fc43.noarch
cmake-srpm-macros-3.31.11-1.fc43.noarch
coreutils-9.7-10.fc43.x86_64
coreutils-common-9.7-10.fc43.x86_64
cpio-2.15-6.fc43.x86_64
crypto-policies-20251125-1.git63291f8.fc43.noarch
curl-8.15.0-12.fc43.x86_64
cyrus-sasl-lib-2.1.28-33.fc43.x86_64
debugedit-5.2-3.fc43.x86_64
diffutils-3.12-3.fc43.x86_64
dwz-0.16-2.fc43.x86_64
ed-1.22.2-1.fc43.x86_64
efi-srpm-macros-6-4.fc43.noarch
elfutils-0.197-1.fc43.x86_64
elfutils-debuginfod-client-0.197-1.fc43.x86_64
elfutils-default-yama-scope-0.197-1.fc43.noarch
elfutils-libelf-0.197-1.fc43.x86_64
elfutils-libs-0.197-1.fc43.x86_64
fedora-gpg-keys-43-2.noarch
fedora-release-43-27.noarch
fedora-release-common-43-27.noarch
fedora-release-identity-basic-43-27.noarch
fedora-repos-43-2.noarch
file-5.46-9.fc43.x86_64
file-libs-5.46-9.fc43.x86_64
filesystem-3.18-50.fc43.x86_64
filesystem-srpm-macros-3.18-50.fc43.noarch
findutils-4.10.0-6.fc43.x86_64
fonts-srpm-macros-2.0.5-23.fc43.noarch
forge-srpm-macros-0.4.0-3.fc43.noarch
fpc-srpm-macros-1.3-15.fc43.noarch
gap-srpm-macros-2-1.fc43.noarch
gawk-5.3.2-2.fc43.x86_64
gdb-minimal-18.1-1.fc43.x86_64
gdbm-libs-1.23-10.fc43.x86_64
ghc-srpm-macros-1.9.2-3.fc43.noarch
glibc-2.42-17.fc43.x86_64
glibc-common-2.42-17.fc43.x86_64
glibc-gconv-extra-2.42-17.fc43.x86_64
glibc-minimal-langpack-2.42-17.fc43.x86_64
gmp-6.3.0-4.fc43.x86_64
gnat-srpm-macros-7-1.fc43.noarch
gnome-srpm-macros-1.0-1.fc43.noarch
gnulib-l10n-20241231-1.fc43.noarch
gnupg2-2.4.9-5.fc43.x86_64
gnupg2-dirmngr-2.4.9-5.fc43.x86_64
gnupg2-gpg-agent-2.4.9-5.fc43.x86_64
gnupg2-gpgconf-2.4.9-5.fc43.x86_64
gnupg2-keyboxd-2.4.9-5.fc43.x86_64
gnupg2-verify-2.4.9-5.fc43.x86_64
gnutls-3.8.13-1.fc43.x86_64
go-srpm-macros-3.8.0-1.fc43.noarch
gpg-pubkey-c6e7f081cf80e13146676e88829b606631645531-66b6dccf
gpgverify-2.2-3.fc43.noarch
grep-3.12-2.fc43.x86_64
gzip-1.13-4.fc43.x86_64
ima-evm-utils-libs-1.6.2-6.fc43.x86_64
info-7.2-7.fc43.x86_64
jansson-2.14-3.fc43.x86_64
java-srpm-macros-1-7.fc43.noarch
json-c-0.18-7.fc43.x86_64
kernel-srpm-macros-1.0-27.fc43.noarch
keyutils-libs-1.6.3-6.fc43.x86_64
krb5-libs-1.22.2-4.fc43.x86_64
libacl-2.4.0-1.fc43.x86_64
libarchive-3.8.4-1.fc43.x86_64
libassuan-2.5.7-4.fc43.x86_64
libattr-2.6.0-1.fc43.x86_64
libblkid-2.41.6-1.fc43.x86_64
libbrotli-1.2.1-1.fc43.x86_64
libcap-2.76-4.fc43.x86_64
libcap-ng-0.9.6-1.fc43.x86_64
libcom_err-1.47.3-2.fc43.x86_64
libcurl-8.15.0-12.fc43.x86_64
libeconf-0.7.9-2.fc43.x86_64
libevent-2.1.13-1.fc43.x86_64
libfdisk-2.41.6-1.fc43.x86_64
libffi-3.5.2-1.fc43.x86_64
libfsverity-1.6-3.fc43.x86_64
libgcc-15.3.1-1.fc43.x86_64
libgcrypt-1.11.1-4.fc43.x86_64
libgomp-15.3.1-1.fc43.x86_64
libgpg-error-1.55-2.fc43.x86_64
libidn2-2.3.8-2.fc43.x86_64
libksba-1.6.7-4.fc43.x86_64
liblastlog2-2.41.6-1.fc43.x86_64
libmount-2.41.6-1.fc43.x86_64
libnghttp2-1.66.0-3.fc43.x86_64
libpfm-4.14.1-2.fc43.x86_64
libpkgconf-2.3.0-3.fc43.x86_64
libpsl-0.21.5-6.fc43.x86_64
libselinux-3.9-5.fc43.x86_64
libsemanage-3.9-4.fc43.x86_64
libsepol-3.9-2.fc43.x86_64
libsmartcols-2.41.6-1.fc43.x86_64
libssh-0.11.5-1.fc43.x86_64
libssh-config-0.11.5-1.fc43.noarch
libstdc++-15.3.1-1.fc43.x86_64
libtasn1-4.21.0-1.fc43.x86_64
libtool-ltdl-2.5.4-8.fc43.x86_64
libunistring-1.1-10.fc43.x86_64
libusb1-1.0.30-1.fc43.x86_64
libuuid-2.41.6-1.fc43.x86_64
libverto-0.3.2-11.fc43.x86_64
libxcrypt-4.5.2-1.fc43.x86_64
libxml2-2.12.10-5.fc43.x86_64
libzstd-1.5.7-2.fc43.x86_64
lua-libs-5.4.8-4.fc43.x86_64
lua-srpm-macros-1-16.fc43.noarch
lz4-libs-1.10.0-3.fc43.x86_64
mpfr-4.2.2-2.fc43.x86_64
ncurses-base-6.5-7.20250614.fc43.noarch
ncurses-libs-6.5-7.20250614.fc43.x86_64
nettle-3.10.1-2.fc43.x86_64
npth-1.8-3.fc43.x86_64
ocaml-srpm-macros-11-2.fc43.noarch
openblas-srpm-macros-2-20.fc43.noarch
openldap-2.6.15-1.fc43.x86_64
openssl-libs-3.5.9-1.fc43.x86_64
p11-kit-0.26.5-1.fc43.x86_64
p11-kit-trust-0.26.5-1.fc43.x86_64
package-notes-srpm-macros-0.20-1.fc43.noarch
pam-libs-1.7.1-5.fc43.x86_64
patch-2.8-2.fc43.x86_64
pcre2-10.47-1.fc43.x86_64
pcre2-syntax-10.47-1.fc43.noarch
perl-srpm-macros-1-60.fc43.noarch
pkgconf-2.3.0-3.fc43.x86_64
pkgconf-m4-2.3.0-3.fc43.noarch
pkgconf-pkg-config-2.3.0-3.fc43.x86_64
popt-1.19-9.fc43.x86_64
publicsuffix-list-dafsa-20260116-1.fc43.noarch
pyproject-srpm-macros-1.24.0-1.fc43.noarch
python-srpm-macros-3.14-5.fc43.noarch
qt5-srpm-macros-5.15.18-1.fc43.noarch
qt6-srpm-macros-6.10.3-1.fc43.noarch
readline-8.3-2.fc43.x86_64
redhat-rpm-config-343-14.fc43.noarch
rpm-6.0.2-1.fc43.x86_64
rpm-build-6.0.2-1.fc43.x86_64
rpm-build-libs-6.0.2-1.fc43.x86_64
rpm-libs-6.0.2-1.fc43.x86_64
rpm-sequoia-1.10.3-1.fc43.x86_64
rpm-sign-libs-6.0.2-1.fc43.x86_64
rust-srpm-macros-28.5-1.fc43.noarch
sed-4.9-5.fc43.x86_64
setup-2.15.0-26.fc43.noarch
shadow-utils-4.18.0-3.fc43.x86_64
sqlite-libs-3.50.2-3.fc43.x86_64
systemd-libs-258.11-1.fc43.x86_64
systemd-standalone-sysusers-258.11-1.fc43.x86_64
tar-1.35-6.fc43.x86_64
tpm2-tss-4.1.3-8.fc43.x86_64
tree-sitter-srpm-macros-0.4.2-1.fc43.noarch
unzip-6.0-67.fc43.x86_64
util-linux-2.41.6-1.fc43.x86_64
util-linux-core-2.41.6-1.fc43.x86_64
which-2.23-3.fc43.x86_64
xxhash-libs-0.8.4-2.fc43.x86_64
xz-5.8.1-4.fc43.x86_64
xz-libs-5.8.1-4.fc43.x86_64
zig-srpm-macros-1-5.fc43.noarch
zip-3.0-44.fc43.x86_64
zlib-ng-compat-2.3.3-2.fc43.x86_64
zstd-1.5.7-2.fc43.x86_64
Start: buildsrpm
Start: rpmbuild -bs
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1793923200
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc43.src.rpm
Finish: rpmbuild -bs
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-43-x86_64-1794040424.263307/root/var/log/dnf5.log
INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz
/bin/tar: Removing leading `/' from member names
Finish: buildsrpm
INFO: Done(/var/lib/copr-rpmbuild/workspace/workdir-e025pw8w/python-pyradplan/python-pyradplan.spec) Config(child) 0 minutes 20 seconds
INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results
INFO: Cleaning up build root ('cleanup_on_success=True')
Start: clean chroot
INFO: unmounting tmpfs.
Finish: clean chroot
INFO: Start(/var/lib/copr-rpmbuild/results/python-pyradplan-0.8.0-1.fc43.src.rpm)  Config(fedora-43-x86_64)
Start(bootstrap): chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-43-x86_64-bootstrap-1794040424.263307/root.
INFO: reusing tmpfs at /var/lib/mock/fedora-43-x86_64-bootstrap-1794040424.263307/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start(bootstrap): cleaning package manager metadata
Finish(bootstrap): cleaning package manager metadata
Finish(bootstrap): chroot init
Start: chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-43-x86_64-1794040424.263307/root.
INFO: calling preinit hooks
INFO: enabled root cache
Start: unpacking root cache
Finish: unpacking root cache
INFO: enabled package manager cache
Start: cleaning package manager metadata
Finish: cleaning package manager metadata
INFO: enabled HW Info plugin
INFO: Buildroot is handled by package management downloaded with a bootstrap image:
  rpm-6.0.2-1.fc43.x86_64
  rpm-sequoia-1.10.3-1.fc43.x86_64
  dnf5-5.2.18.0-5.fc43.x86_64
  dnf5-plugins-5.2.18.0-5.fc43.x86_64
Finish: chroot init
Start: build phase for python-pyradplan-0.8.0-1.fc43.src.rpm
Start: build setup for python-pyradplan-0.8.0-1.fc43.src.rpm
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1793923200
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc43.src.rpm
Updating and loading repositories:
 Copr repository                        100% |  11.2 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% |  51.1 KiB/s |  23.5 KiB |  00m00s
 updates                                100% |  69.7 KiB/s |  18.6 KiB |  00m00s
Repositories loaded.
Package                 Arch   Version        Repository      Size
Installing:
 pyproject-rpm-macros   noarch 1.24.0-1.fc43  updates    169.0 KiB
 python3-devel          x86_64 3.14.8-1.fc43  updates      2.0 MiB
 python3-pytest         noarch 8.3.5-9.fc43   fedora      21.6 MiB
Installing dependencies:
 expat                  x86_64 2.9.0-1.fc43   updates    337.6 KiB
 mpdecimal              x86_64 4.0.1-2.fc43   fedora     217.2 KiB
 python-pip-wheel       noarch 25.1.1-19.fc43 updates      1.2 MiB
 python-rpm-macros      noarch 3.14-5.fc43    fedora      23.2 KiB
 python3                x86_64 3.14.8-1.fc43  updates     28.7 KiB
 python3-iniconfig      noarch 2.1.0-3.fc43   fedora      42.4 KiB
 python3-libs           x86_64 3.14.8-1.fc43  updates     43.5 MiB
 python3-packaging      noarch 25.0-7.fc43    fedora     607.4 KiB
 python3-pluggy         noarch 1.6.0-4.fc43   fedora     211.0 KiB
 python3-rpm-generators noarch 14-13.fc43     fedora      81.7 KiB
 python3-rpm-macros     noarch 3.14-5.fc43    fedora       6.5 KiB
 tzdata                 noarch 2026e-1.fc43   updates      1.6 MiB

Transaction Summary:
 Installing:        15 packages

Total size of inbound packages is 15 MiB. Need to download 15 MiB.
After this operation, 72 MiB extra will be used (install 72 MiB, remove 0 B).
[ 1/15] python3-devel-0:3.14.8-1.fc43.x 100% |  35.8 MiB/s | 439.4 KiB |  00m00s
[ 2/15] pyproject-rpm-macros-0:1.24.0-1 100% |   4.4 MiB/s |  58.0 KiB |  00m00s
[ 3/15] python3-iniconfig-0:2.1.0-3.fc4 100% |   4.5 MiB/s |  22.9 KiB |  00m00s
[ 4/15] python3-packaging-0:25.0-7.fc43 100% |  26.3 MiB/s | 161.5 KiB |  00m00s
[ 5/15] python3-pluggy-0:1.6.0-4.fc43.n 100% |  30.0 MiB/s |  61.5 KiB |  00m00s
[ 6/15] python3-pytest-0:8.3.5-9.fc43.n 100% |  96.4 MiB/s |   2.2 MiB |  00m00s
[ 7/15] python-rpm-macros-0:3.14-5.fc43 100% |   4.2 MiB/s |  17.3 KiB |  00m00s
[ 8/15] python3-rpm-macros-0:3.14-5.fc4 100% |   2.3 MiB/s |  11.8 KiB |  00m00s
[ 9/15] mpdecimal-0:4.0.1-2.fc43.x86_64 100% |  19.0 MiB/s |  97.1 KiB |  00m00s
[10/15] expat-0:2.9.0-1.fc43.x86_64     100% |  25.6 MiB/s | 131.1 KiB |  00m00s
[11/15] tzdata-0:2026e-1.fc43.noarch    100% |  87.0 MiB/s | 713.0 KiB |  00m00s
[12/15] python-pip-wheel-0:25.1.1-19.fc 100% |  92.7 MiB/s |   1.2 MiB |  00m00s
[13/15] python3-0:3.14.8-1.fc43.x86_64  100% |   4.1 MiB/s |  29.4 KiB |  00m00s
[14/15] python3-rpm-generators-0:14-13. 100% |   5.6 MiB/s |  28.5 KiB |  00m00s
[15/15] python3-libs-0:3.14.8-1.fc43.x8 100% | 220.3 MiB/s |   9.9 MiB |  00m00s
--------------------------------------------------------------------------------
[15/15] Total                           100% | 212.2 MiB/s |  15.1 MiB |  00m00s
Running transaction
[ 1/17] Verify package files            100% | 288.0   B/s |  15.0   B |  00m00s
[ 2/17] Prepare transaction             100% | 517.0   B/s |  15.0   B |  00m00s
[ 3/17] Installing python-rpm-macros-0: 100% |  23.4 MiB/s |  23.9 KiB |  00m00s
[ 4/17] Installing python3-rpm-macros-0 100% |   0.0   B/s |   6.8 KiB |  00m00s
[ 5/17] Installing pyproject-rpm-macros 100% |  41.9 MiB/s | 171.5 KiB |  00m00s
[ 6/17] Installing tzdata-0:2026e-1.fc4 100% |  70.8 MiB/s |   1.9 MiB |  00m00s
[ 7/17] Installing python-pip-wheel-0:2 100% | 623.0 MiB/s |   1.2 MiB |  00m00s
[ 8/17] Installing expat-0:2.9.0-1.fc43 100% |  23.7 MiB/s | 339.6 KiB |  00m00s
[ 9/17] Installing mpdecimal-0:4.0.1-2. 100% |  42.7 MiB/s | 218.8 KiB |  00m00s
[10/17] Installing python3-libs-0:3.14. 100% | 340.6 MiB/s |  43.9 MiB |  00m00s
[11/17] Installing python3-0:3.14.8-1.f 100% |   2.3 MiB/s |  30.4 KiB |  00m00s
[12/17] Installing python3-packaging-0: 100% | 201.8 MiB/s | 619.9 KiB |  00m00s
[13/17] Installing python3-rpm-generato 100% |   0.0   B/s |  82.9 KiB |  00m00s
[14/17] Installing python3-iniconfig-0: 100% |  45.4 MiB/s |  46.5 KiB |  00m00s
[15/17] Installing python3-pluggy-0:1.6 100% |  70.8 MiB/s | 217.5 KiB |  00m00s
[16/17] Installing python3-pytest-0:8.3 100% | 382.4 MiB/s |  21.8 MiB |  00m00s
[17/17] Installing python3-devel-0:3.14 100% |  55.7 MiB/s |   2.0 MiB |  00m00s
Complete!
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1793923200
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc43.src.rpm
Updating and loading repositories:
 Copr repository                        100% |   9.2 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% |  50.4 KiB/s |  23.5 KiB |  00m00s
 updates                                100% |  58.0 KiB/s |  18.6 KiB |  00m00s
Repositories loaded.
Nothing to do.
Package "pyproject-rpm-macros-1.24.0-1.fc43.noarch" is already installed.
Package "python3-devel-3.14.8-1.fc43.x86_64" is already installed.
Package "python3-pytest-8.3.5-9.fc43.noarch" is already installed.

Finish: build setup for python-pyradplan-0.8.0-1.fc43.src.rpm
Start: rpmbuild python-pyradplan-0.8.0-1.fc43.src.rpm
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%mkbuilddir): /bin/sh -e /var/tmp/rpm-tmp.Kot0LG
Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.Sh0hyr
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ rm -rf pyradplan-0.8.0
+ /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/pyradplan-0.8.0.tar.gz
+ STATUS=0
+ '[' 0 -ne 0 ']'
+ cd pyradplan-0.8.0
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w .
+ sed -i -e '/"pydantic_ai/d' -e '/"types-array-api"/d' -e 's/"numpy>=2.0,<2.4"/"numpy>=2.0"/' pyproject.toml
+ grep -q '"numpy>=2.0"' pyproject.toml
+ grep -E '"pydantic_ai|"types-array-api' pyproject.toml
+ sed -i -e '/^import pyRadPlan\.ai\.agents$/d' -e 's/\[pyRadPlan, pyRadPlan\.ai\.agents\]/[pyRadPlan]/' test/test_lazy_imports.py
+ grep -n 'ai\.agents' test/test_lazy_imports.py
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.Q7Vllv
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ cd pyradplan-0.8.0
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1   '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir --output /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-dep-overrides
Handling setuptools>=64 from build-system.requires
Requirement not satisfied: setuptools>=64
Handling setuptools-scm>=8 from build-system.requires
Requirement not satisfied: setuptools-scm>=8
Exiting dependency generation pass: build backend
+ cat /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires
+ rm -rfv '*.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc43.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 Copr repository                        100% |   6.8 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% |  57.4 KiB/s |  23.5 KiB |  00m00s
 updates                                100% |  74.7 KiB/s |  18.6 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.24.0-1.fc43.noarch" is already installed.
Package "python3-devel-3.14.8-1.fc43.x86_64" is already installed.
Package "python3-pytest-8.3.5-9.fc43.noarch" is already installed.
Package "python3-packaging-25.0-7.fc43.noarch" is already installed.

Package                 Arch   Version        Repository      Size
Installing:
 python3-pip            noarch 25.1.1-19.fc43 updates     12.5 MiB
 python3-setuptools     noarch 78.1.1-15.fc43 fedora       9.0 MiB
 python3-setuptools_scm noarch 8.3.1-8.fc43   fedora     354.5 KiB

Transaction Summary:
 Installing:         3 packages

Total size of inbound packages is 5 MiB. Need to download 5 MiB.
After this operation, 22 MiB extra will be used (install 22 MiB, remove 0 B).
[1/3] python3-setuptools_scm-0:8.3.1-8. 100% |   9.1 MiB/s | 121.7 KiB |  00m00s
[2/3] python3-pip-0:25.1.1-19.fc43.noar 100% | 167.3 MiB/s |   2.8 MiB |  00m00s
[3/3] python3-setuptools-0:78.1.1-15.fc 100% |  68.5 MiB/s |   2.1 MiB |  00m00s
--------------------------------------------------------------------------------
[3/3] Total                             100% | 156.8 MiB/s |   5.0 MiB |  00m00s
Running transaction
[1/5] Verify package files              100% | 187.0   B/s |   3.0   B |  00m00s
[2/5] Prepare transaction               100% | 157.0   B/s |   3.0   B |  00m00s
[3/5] Installing python3-setuptools-0:7 100% | 249.2 MiB/s |   9.2 MiB |  00m00s
[4/5] Installing python3-setuptools_scm 100% |  73.1 MiB/s | 374.4 KiB |  00m00s
[5/5] Installing python3-pip-0:25.1.1-1 100% | 175.2 MiB/s |  12.8 MiB |  00m00s
Complete!
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.Epe8DN
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ cd pyradplan-0.8.0
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1   '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir --output /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-dep-overrides
Handling setuptools>=64 from build-system.requires
Requirement satisfied: setuptools>=64
   (installed: setuptools 78.1.1)
Handling setuptools-scm>=8 from build-system.requires
Requirement satisfied: setuptools-scm>=8
   (installed: setuptools-scm 8.3.1)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running egg_info
writing pyRadPlan.egg-info/PKG-INFO
writing dependency_links to pyRadPlan.egg-info/dependency_links.txt
writing entry points to pyRadPlan.egg-info/entry_points.txt
writing requirements to pyRadPlan.egg-info/requires.txt
writing top-level names to pyRadPlan.egg-info/top_level.txt
reading manifest file 'pyRadPlan.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'pyRadPlan.egg-info/SOURCES.txt'
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running dist_info
writing pyRadPlan.egg-info/PKG-INFO
writing dependency_links to pyRadPlan.egg-info/dependency_links.txt
writing entry points to pyRadPlan.egg-info/entry_points.txt
writing requirements to pyRadPlan.egg-info/requires.txt
writing top-level names to pyRadPlan.egg-info/top_level.txt
reading manifest file 'pyRadPlan.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'pyRadPlan.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/pyradplan-0.8.0.dist-info'
Handling scipy>=1.13.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: scipy>=1.13.0
Handling numpy>=2.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: numpy>=2.0
Handling array_api_compat from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: array_api_compat
Handling array_api_strict from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: array_api_strict
Handling array_api_extra from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: array_api_extra
Handling ipax>=0.13.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: ipax>=0.13.0
Handling pymatreader>=1.0.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: pymatreader>=1.0.0
Handling h5py>=3.11.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: h5py>=3.11.0
Handling regex>=2024.5.15 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: regex>=2024.5.15
Handling pydicom>=2.4.4 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: pydicom>=2.4.4
Handling matplotlib>=3.9.2 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: matplotlib>=3.9.2
Handling pydantic<2.20,>=2.10 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: pydantic<2.20,>=2.10
Handling pydantic-settings<3.0,>=2.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: pydantic-settings<3.0,>=2.0
Handling numpydantic>=1.6.9 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: numpydantic>=1.6.9
Handling SimpleITK>=2.4.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: SimpleITK>=2.4.0
Handling tqdm>=4.0.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: tqdm>=4.0.0
Handling pint>=0.24.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: pint>=0.24.0
Handling jinja2 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: jinja2
Handling huggingface_hub>=0.23 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: huggingface_hub>=0.23
Handling safetensors from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: safetensors
Handling matlabengine; extra == "matlab" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: matlabengine; extra == "matlab"
Handling oct2py; extra == "octave" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: oct2py; extra == "octave"
Handling pyside6-essentials<6.12,>=6.4; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pyside6-essentials<6.12,>=6.4; extra == "gui"
Handling pyqtgraph>=0.12.0; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pyqtgraph>=0.12.0; extra == "gui"
Handling contourpy>=1.0.1; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: contourpy>=1.0.1; extra == "gui"
Handling numba>=0.61.0; extra == "numba" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: numba>=0.61.0; extra == "numba"
Handling torch>=2.0; extra == "torch" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: torch>=2.0; extra == "torch"
Handling cupy>=13.0; extra == "cupy" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: cupy>=13.0; extra == "cupy"
Handling jax>=0.4.35; extra == "jax" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: jax>=0.4.35; extra == "jax"
Handling line_profiler>=4.0; extra == "profiling" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: line_profiler>=4.0; extra == "profiling"
Handling pytest; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest; extra == "dev"
Handling coverage; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: coverage; extra == "dev"
Handling pytest-cov; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest-cov; extra == "dev"
Handling pytest-benchmark; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest-benchmark; extra == "dev"
Handling pre-commit; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pre-commit; extra == "dev"
Handling ruff<0.18; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: ruff<0.18; extra == "dev"
Handling sphinx; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx; extra == "dev"
Handling numpydoc; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: numpydoc; extra == "dev"
Handling pydata-sphinx-theme; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pydata-sphinx-theme; extra == "dev"
Handling sphinx-design; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx-design; extra == "dev"
Handling autodoc_pydantic; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: autodoc_pydantic; extra == "dev"
Handling sphinx-autodoc-typehints; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx-autodoc-typehints; extra == "dev"
Handling myst-nb; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: myst-nb; extra == "dev"
Handling jupytext; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: jupytext; extra == "dev"
+ cat /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires
+ rm -rfv pyradplan-0.8.0.dist-info/
removed 'pyradplan-0.8.0.dist-info/licenses/LICENSE'
removed directory 'pyradplan-0.8.0.dist-info/licenses'
removed 'pyradplan-0.8.0.dist-info/METADATA'
removed 'pyradplan-0.8.0.dist-info/entry_points.txt'
removed 'pyradplan-0.8.0.dist-info/top_level.txt'
removed directory 'pyradplan-0.8.0.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc43.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 Copr repository                        100% |  11.8 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% |  56.6 KiB/s |  23.5 KiB |  00m00s
 updates                                100% |  72.4 KiB/s |  18.6 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.24.0-1.fc43.noarch" is already installed.
Package "python3-devel-3.14.8-1.fc43.x86_64" is already installed.
Package "python3-pytest-8.3.5-9.fc43.noarch" is already installed.
Package "python3-packaging-25.0-7.fc43.noarch" is already installed.
Package "python3-pip-25.1.1-19.fc43.noarch" is already installed.
Package "python3-setuptools-78.1.1-15.fc43.noarch" is already installed.
Package "python3-setuptools_scm-8.3.1-8.fc43.noarch" is already installed.

Package                        Arch   Version                         Repository      Size
Installing:
 python3-array-api-compat      noarch 1.15.0-1.fc43                   copr_base  506.6 KiB
 python3-array-api-extra       noarch 0.11.5-1.fc43                   copr_base    1.4 MiB
 python3-array-api-strict      noarch 2.0.1-7.fc43                    fedora     694.1 KiB
 python3-h5py                  x86_64 3.13.0-6.fc43                   fedora       4.1 MiB
 python3-huggingface-hub       noarch 1.24.0-1.fc43                   copr_base    6.5 MiB
 python3-ipax                  noarch 0.13.0-1.fc43                   copr_base    3.0 MiB
 python3-jinja2                noarch 3.1.6-6.fc43                    fedora       3.1 MiB
 python3-matplotlib            x86_64 3.10.9-1.fc43                   updates     31.7 MiB
 python3-numpy                 x86_64 1:2.3.5-1.fc43                  updates     41.4 MiB
 python3-numpydantic           noarch 2.0.0-1.fc43                    copr_base  621.5 KiB
 python3-pint                  noarch 0.25.2-1.fc43                   updates      3.4 MiB
 python3-pydantic              noarch 2.12.5-4.fc43                   updates      5.1 MiB
 python3-pydantic-settings     noarch 2.15.0-1.fc43                   updates    557.0 KiB
 python3-pydicom               noarch 3.0.2-1.fc43                    updates     13.9 MiB
 python3-pymatreader           noarch 1.3.1-1.fc43                    copr_base   65.9 KiB
 python3-regex                 x86_64 2026.9.10-1.fc43                updates      2.1 MiB
 python3-safetensors           x86_64 0.7.0-1.fc43                    updates    945.3 KiB
 python3-scipy                 x86_64 1.16.2-1.fc43                   fedora      66.5 MiB
 python3-simpleitk             x86_64 2.5.5-12.fc43                   copr_base   15.6 MiB
 python3-tqdm                  noarch 4.67.2-2.fc43                   updates    574.9 KiB
Installing dependencies:
 CharLS                        x86_64 2.4.3-2.fc43                    updates    336.1 KiB
 InsightToolkit5               x86_64 5.4.7-24.fc43                   copr_base   28.7 MiB
 abattis-cantarell-vf-fonts    noarch 0.301-15.fc43                   fedora     192.7 KiB
 abseil-cpp                    x86_64 20250814.2-2.fc43               updates      2.5 MiB
 armadillo12                   x86_64 12.8.1-1.fc43                   copr_base   81.9 KiB
 arpack                        x86_64 3.9.1-7.fc43                    fedora     641.8 KiB
 blosc                         x86_64 1.21.6-7.fc43                   fedora     126.2 KiB
 boost-program-options         x86_64 1.83.0-17.fc43                  fedora     280.8 KiB
 cairo                         x86_64 1.18.6-1.fc43                   updates      1.8 MiB
 cfitsio                       x86_64 4.6.3-1.fc43                    updates      1.9 MiB
 dcmtk                         x86_64 3.7.0^20260922git8d68392-1.fc43 copr_base   42.6 MiB
 default-fonts-core-sans       noarch 4.2-5.fc43                      fedora      11.9 KiB
 dejavu-sans-fonts             noarch 2.37-26.fc43                    fedora       5.5 MiB
 double-conversion             x86_64 3.4.0-1.fc43                    updates     97.9 KiB
 expatpp                       x86_64 0-32.20121019gitd8c1bf8.fc43    fedora      37.9 KiB
 fftw-libs-double              x86_64 3.3.10-16.fc43                  fedora       3.4 MiB
 fftw-libs-single              x86_64 3.3.10-16.fc43                  fedora       3.6 MiB
 flexiblas                     x86_64 3.5.0-1.fc43                    updates     38.0 KiB
 flexiblas-netlib              x86_64 3.5.0-1.fc43                    updates     16.5 MiB
 flexiblas-netlib64            x86_64 3.5.0-1.fc43                    updates     16.7 MiB
 flexiblas-openblas-openmp     x86_64 3.5.0-1.fc43                    updates     39.2 KiB
 flexiblas-openblas-openmp64   x86_64 3.5.0-1.fc43                    updates     39.2 KiB
 fmt                           x86_64 11.2.0-3.fc43                   fedora     266.8 KiB
 fontconfig                    x86_64 2.17.0-3.fc43                   fedora     765.9 KiB
 fonts-filesystem              noarch 1:2.0.5-23.fc43                 fedora       0.0   B
 freetype                      x86_64 2.13.3-4.fc43                   updates    850.1 KiB
 freexl                        x86_64 2.0.0-15.fc43                   fedora      89.3 KiB
 fribidi                       x86_64 1.0.16-3.fc43                   fedora     190.3 KiB
 gdal-libs                     x86_64 3.11.5-1.fc43                   updates     31.2 MiB
 gdcm                          x86_64 3.0.24-14.fc43                  fedora       8.1 MiB
 geos                          x86_64 3.14.1-1.fc43                   updates      4.1 MiB
 giflib                        x86_64 5.2.2-9.fc43                    updates    116.1 KiB
 glib2                         x86_64 2.86.5-1.fc43                   updates     15.0 MiB
 google-noto-fonts-common      noarch 20251101-2.fc43                 updates     17.7 KiB
 google-noto-sans-vf-fonts     noarch 20251101-2.fc43                 updates      1.4 MiB
 gpgme                         x86_64 1.24.3-6.fc43                   fedora     587.9 KiB
 gpgmepp                       x86_64 1.24.3-6.fc43                   fedora     429.2 KiB
 graphite2                     x86_64 1.3.14-19.fc43                  fedora     191.8 KiB
 harfbuzz                      x86_64 11.5.1-2.fc43                   updates      2.8 MiB
 hdf-libs                      x86_64 4.3.0-4.fc43                    fedora     687.2 KiB
 hdf5                          x86_64 1.14.6-6.fc43                   fedora       6.4 MiB
 imath                         x86_64 3.1.12-4.fc43                   fedora     379.7 KiB
 jbigkit-libs                  x86_64 2.1-32.fc43                     fedora     113.5 KiB
 jsoncpp                       x86_64 1.9.6-2.fc43                    fedora     257.6 KiB
 lcms2                         x86_64 2.16-6.fc43                     fedora     433.7 KiB
 libX11                        x86_64 1.8.13-1.fc43                   updates      1.3 MiB
 libX11-common                 noarch 1.8.13-1.fc43                   updates      1.1 MiB
 libXau                        x86_64 1.0.12-3.fc43                   fedora      72.9 KiB
 libXext                       x86_64 1.3.6-4.fc43                    fedora      90.0 KiB
 libXrender                    x86_64 0.9.12-3.fc43                   fedora      46.0 KiB
 libaec                        x86_64 1.1.4-3.fc43                    fedora      90.3 KiB
 libarrow                      x86_64 20.0.0-10.fc43                  updates     26.6 MiB
 libarrow-acero-libs           x86_64 20.0.0-10.fc43                  updates      1.4 MiB
 libarrow-dataset-libs         x86_64 20.0.0-10.fc43                  updates      1.5 MiB
 libarrow-doc                  noarch 20.0.0-10.fc43                  updates    117.1 KiB
 libdc1394                     x86_64 2.2.7-8.fc43                    fedora     350.9 KiB
 libdeflate                    x86_64 1.26-1.fc43                     updates    123.9 KiB
 libgeotiff                    x86_64 1.7.4-3.fc43                    fedora     290.3 KiB
 libgfortran                   x86_64 15.3.1-1.fc43                   updates      3.3 MiB
 libgta                        x86_64 1.2.1-16.fc43                   fedora      66.1 KiB
 libharu                       x86_64 2.4.5-1.fc43                    fedora       1.7 MiB
 libicu                        x86_64 77.1-1.fc43                     fedora      36.4 MiB
 libimagequant                 x86_64 4.0.3-8.fc43                    fedora     690.9 KiB
 libjpeg-turbo                 x86_64 3.1.3-1.fc43                    updates    806.3 KiB
 libkml                        x86_64 1.3.0-56.fc43                   fedora       1.2 MiB
 liblerc                       x86_64 4.0.0-9.fc43                    fedora     624.1 KiB
 liblzf                        x86_64 3.6-33.fc43                     fedora      49.5 KiB
 libminc                       x86_64 2.4.03-27.fc43                  fedora     693.6 KiB
 libogg                        x86_64 2:1.3.6-2.fc43                  updates     45.6 KiB
 liborc2                       x86_64 2.1.4-1.fc43                    updates      1.8 MiB
 libpng                        x86_64 2:1.6.58-1.fc43                 updates    245.7 KiB
 libpq                         x86_64 18.0-3.fc43                     fedora       1.0 MiB
 libqhull_r                    x86_64 1:8.0.2-7.fc43                  fedora     483.3 KiB
 libquadmath                   x86_64 15.3.1-1.fc43                   updates    317.9 KiB
 libraqm                       x86_64 0.10.1-3.fc43                   fedora      28.6 KiB
 libraw1394                    x86_64 2.1.2-24.fc43                   fedora     158.8 KiB
 librttopo                     x86_64 1.1.0-17.fc43                   fedora     504.6 KiB
 libspatialite                 x86_64 5.1.0-11.fc43                   fedora      15.3 MiB
 libtheora                     x86_64 1:1.1.1-40.fc43                 fedora     477.3 KiB
 libtiff                       x86_64 4.7.2-3.fc43                    updates    636.5 KiB
 libtirpc                      x86_64 1.3.7-1.rc1.fc43                updates    200.3 KiB
 libwebp                       x86_64 1.6.0-2.fc43                    fedora     956.6 KiB
 libxcb                        x86_64 1.17.0-6.fc43                   fedora       1.1 MiB
 libyaml                       x86_64 0.2.5-17.fc43                   fedora     134.6 KiB
 libzip                        x86_64 1.11.4-2.fc43                   fedora     135.8 KiB
 lzo                           x86_64 2.10-15.fc43                    fedora     178.9 KiB
 mariadb-connector-c           x86_64 3.4.9-2.fc43                    updates    493.4 KiB
 mariadb-connector-c-config    noarch 3.4.9-2.fc43                    updates     26.4 KiB
 minizip-ng-compat             x86_64 4.0.10-2.fc43                   fedora     162.5 KiB
 muParser                      x86_64 2.3.5-3.fc43                    fedora     551.2 KiB
 netcdf                        x86_64 4.9.2-15.fc43                   fedora       2.3 MiB
 nspr                          x86_64 4.39.0-7.fc43                   updates    315.1 KiB
 nss                           x86_64 3.131.0-1.fc43                  updates      1.9 MiB
 nss-softokn                   x86_64 3.131.0-1.fc43                  updates      2.0 MiB
 nss-softokn-freebl            x86_64 3.131.0-1.fc43                  updates      1.1 MiB
 nss-sysinit                   x86_64 3.131.0-1.fc43                  updates     17.9 KiB
 nss-util                      x86_64 3.131.0-1.fc43                  updates    204.8 KiB
 openblas                      x86_64 0.3.29-2.fc43                   fedora     111.7 KiB
 openblas-openmp               x86_64 0.3.29-2.fc43                   fedora      43.7 MiB
 openblas-openmp64             x86_64 0.3.29-2.fc43                   fedora      43.8 MiB
 openexr-libs                  x86_64 3.2.4-6.fc43                    fedora       6.5 MiB
 openjpeg                      x86_64 2.5.4-1.fc43                    fedora     456.3 KiB
 parquet-libs                  x86_64 20.0.0-10.fc43                  updates      3.3 MiB
 pixman                        x86_64 0.46.2-2.fc43                   fedora     710.3 KiB
 poppler                       x86_64 25.07.0-5.fc43                  updates      3.8 MiB
 poppler-data                  noarch 0.4.11-10.fc43                  fedora      12.3 MiB
 proj                          x86_64 9.6.2-2.fc43                    fedora       4.5 MiB
 proj-data                     noarch 9.6.2-2.fc43                    fedora       9.3 MiB
 protobuf                      x86_64 3.19.6-18.fc43                  fedora       3.3 MiB
 pugixml                       x86_64 1.16-1.fc43                     updates    278.5 KiB
 python3-annotated-types       noarch 0.7.0-8.fc43                    fedora      85.1 KiB
 python3-anyio                 noarch 4.13.0-2.fc43                   updates      1.5 MiB
 python3-cached_property       noarch 2.0.1-6.fc43                    fedora      35.2 KiB
 python3-cairo                 x86_64 1.28.0-4.fc43                   fedora     492.9 KiB
 python3-certifi               noarch 2025.07.09-3.fc43               fedora       7.4 KiB
 python3-charset-normalizer    noarch 3.4.3-3.fc43                    fedora     355.7 KiB
 python3-click                 noarch 1:8.1.7-12.fc43                 updates      1.2 MiB
 python3-contourpy             x86_64 1.4.0-1.fc43                    updates    857.1 KiB
 python3-cycler                noarch 0.11.0-19.fc43                  fedora      37.8 KiB
 python3-dateutil              noarch 1:2.9.0.post0-6.fc43            fedora     877.5 KiB
 python3-dotenv                noarch 1.2.2-1.fc43                    updates    149.4 KiB
 python3-filelock              noarch 3.15.4-10.fc43                  fedora      97.0 KiB
 python3-flexcache             noarch 0.3-10.fc43                     fedora      57.0 KiB
 python3-flexparser            noarch 0.4-6.fc43                      fedora     263.5 KiB
 python3-fonttools             x86_64 4.61.0-1.fc43                   updates     19.2 MiB
 python3-fsspec                noarch 2026.6.0-1.fc43                 updates      1.9 MiB
 python3-h11                   noarch 0.16.0-5.fc43                   fedora     279.9 KiB
 python3-httpcore              noarch 1.0.9-5.fc43                    fedora     853.9 KiB
 python3-httpx                 noarch 0.28.1-9.fc43                   fedora     992.1 KiB
 python3-idna                  noarch 3.18-1.fc43                     updates    522.3 KiB
 python3-kiwisolver            x86_64 1.5.1-1.fc43                    updates    153.8 KiB
 python3-markupsafe            x86_64 3.0.2-6.fc43                    fedora      61.5 KiB
 python3-matplotlib-data-fonts x86_64 3.10.9-1.fc43                   updates      7.9 MiB
 python3-numpy-f2py            x86_64 1:2.3.5-1.fc43                  updates      2.1 MiB
 python3-olefile               noarch 0.47-12.fc43                    updates    346.5 KiB
 python3-pillow                x86_64 11.3.0-10.fc43                  updates      4.2 MiB
 python3-platformdirs          noarch 4.4.0-1.fc43                    updates    182.4 KiB
 python3-pooch                 noarch 1.9.0-1.fc43                    updates    656.8 KiB
 python3-pydantic-core         x86_64 2.41.5-11.fc43                  updates      5.3 MiB
 python3-pyparsing             noarch 3.1.2-14.fc43                   fedora       1.0 MiB
 python3-pyyaml                x86_64 6.0.2-7.fc43                    fedora     793.4 KiB
 python3-requests              noarch 2.33.1-1.fc43                   updates    477.3 KiB
 python3-six                   noarch 1.17.0-7.fc43                   fedora     118.0 KiB
 python3-typing-extensions     noarch 4.16.0-4.fc43                   updates    552.0 KiB
 python3-typing-inspection     noarch 0.4.4-1.fc43                    updates     93.4 KiB
 python3-urllib3               noarch 2.8.0-3.fc43                    updates      1.2 MiB
 python3-xmltodict             noarch 0.14.2-4.fc43                   fedora      56.0 KiB
 re2                           x86_64 2:20251105-1.fc43               updates    583.4 KiB
 rply                          x86_64 1.1.4-11.fc43                   updates    115.5 KiB
 simpleitk                     x86_64 2.5.5-12.fc43                   copr_base  285.9 MiB
 snappy                        x86_64 1.2.2-2.fc43                    fedora      71.8 KiB
 thrift                        x86_64 0.20.0-8.fc43                   fedora       5.5 MiB
 unixODBC                      x86_64 2.3.14-2.fc43                   updates      1.0 MiB
 uriparser                     x86_64 1.0.2-1.fc43                    updates    184.4 KiB
 utf8proc                      x86_64 2.10.0-2.fc43                   fedora     372.0 KiB
 viskores                      x86_64 1.2.0-3.fc43                    copr_base  133.7 MiB
 vtk                           x86_64 9.7.0-7.fc43                    copr_base  241.5 MiB
 vxl                           x86_64 3.5.0-16.fc43                   fedora      53.1 MiB
 xerces-c                      x86_64 3.3.0-8.fc43                    fedora       3.8 MiB
 xml-common                    noarch 0.6.3-67.fc43                   fedora      78.4 KiB

Transaction Summary:
 Installing:       180 packages

Total size of inbound packages is 277 MiB. Need to download 277 MiB.
After this operation, 1 GiB extra will be used (install 1 GiB, remove 0 B).
[  1/180] python3-array-api-strict-0:2. 100% |  11.5 MiB/s | 164.4 KiB |  00m00s
[  2/180] python3-h5py-0:3.13.0-6.fc43. 100% |  32.5 MiB/s |   1.1 MiB |  00m00s
[  3/180] python3-array-api-extra-0:0.1 100% | 925.7 KiB/s | 272.2 KiB |  00m00s
[  4/180] python3-jinja2-0:3.1.6-6.fc43 100% | 167.9 MiB/s | 515.8 KiB |  00m00s
[  5/180] python3-ipax-0:0.13.0-1.fc43. 100% |   2.2 MiB/s | 574.3 KiB |  00m00s
[  6/180] python3-scipy-0:1.16.2-1.fc43 100% | 226.9 MiB/s |  16.3 MiB |  00m00s
[  7/180] python3-array-api-compat-0:1. 100% | 320.0 KiB/s | 123.2 KiB |  00m00s
[  8/180] python3-pydantic-0:2.12.5-4.f 100% |  82.7 MiB/s |   1.0 MiB |  00m00s
[  9/180] python3-pydantic-settings-0:2 100% |  56.6 MiB/s | 173.7 KiB |  00m00s
[ 10/180] python3-numpydantic-0:2.0.0-1 100% | 685.7 KiB/s | 186.5 KiB |  00m00s
[ 11/180] python3-simpleitk-0:2.5.5-12. 100% |   7.4 MiB/s |   2.0 MiB |  00m00s
[ 12/180] python3-huggingface-hub-0:1.2 100% |   5.2 MiB/s |   1.3 MiB |  00m00s
[ 13/180] python3-pint-0:0.25.2-1.fc43. 100% |  80.5 MiB/s | 742.1 KiB |  00m00s
[ 14/180] python3-pydicom-0:3.0.2-1.fc4 100% |  95.4 MiB/s |   3.1 MiB |  00m00s
[ 15/180] python3-numpy-1:2.3.5-1.fc43. 100% | 149.5 MiB/s |   8.2 MiB |  00m00s
[ 16/180] python3-regex-0:2026.9.10-1.f 100% |  81.8 MiB/s | 418.7 KiB |  00m00s
[ 17/180] python3-safetensors-0:0.7.0-1 100% |  55.5 MiB/s | 397.9 KiB |  00m00s
[ 18/180] python3-tqdm-0:4.67.2-2.fc43. 100% |  43.1 MiB/s | 176.3 KiB |  00m00s
[ 19/180] hdf5-0:1.14.6-6.fc43.x86_64   100% | 104.9 MiB/s |   2.0 MiB |  00m00s
[ 20/180] liblzf-0:3.6-33.fc43.x86_64   100% |  13.6 MiB/s |  27.8 KiB |  00m00s
[ 21/180] python3-cached_property-0:2.0 100% |  10.0 MiB/s |  20.5 KiB |  00m00s
[ 22/180] python3-six-0:1.17.0-7.fc43.n 100% |  20.4 MiB/s |  41.8 KiB |  00m00s
[ 23/180] python3-markupsafe-0:3.0.2-6. 100% |  10.3 MiB/s |  31.7 KiB |  00m00s
[ 24/180] python3-matplotlib-0:3.10.9-1 100% |  36.2 MiB/s |   7.9 MiB |  00m00s
[ 25/180] python3-annotated-types-0:0.7 100% |  15.4 MiB/s |  31.6 KiB |  00m00s
[ 26/180] python3-pydantic-core-0:2.41. 100% | 102.3 MiB/s |   1.7 MiB |  00m00s
[ 27/180] python3-typing-inspection-0:0 100% |  11.0 MiB/s |  33.8 KiB |  00m00s
[ 28/180] python3-filelock-0:3.15.4-10. 100% |  10.9 MiB/s |  44.8 KiB |  00m00s
[ 29/180] python3-httpx-0:0.28.1-9.fc43 100% |  41.7 MiB/s | 213.7 KiB |  00m00s
[ 30/180] python3-pyyaml-0:6.0.2-7.fc43 100% |  38.5 MiB/s | 236.4 KiB |  00m00s
[ 31/180] dejavu-sans-fonts-0:2.37-26.f 100% | 189.3 MiB/s |   1.3 MiB |  00m00s
[ 32/180] libqhull_r-1:8.0.2-7.fc43.x86 100% |  49.3 MiB/s | 201.9 KiB |  00m00s
[ 33/180] python3-cycler-0:0.11.0-19.fc 100% |  10.7 MiB/s |  21.9 KiB |  00m00s
[ 34/180] python3-dateutil-1:2.9.0.post 100% |  56.0 MiB/s | 344.3 KiB |  00m00s
[ 35/180] python3-pyparsing-0:3.1.2-14. 100% |  93.4 MiB/s | 286.9 KiB |  00m00s
[ 36/180] python3-matplotlib-data-fonts 100% | 234.1 MiB/s |   2.3 MiB |  00m00s
[ 37/180] python3-numpy-f2py-1:2.3.5-1. 100% |  83.1 MiB/s | 510.8 KiB |  00m00s
[ 38/180] python3-flexcache-0:0.3-10.fc 100% |  13.3 MiB/s |  27.2 KiB |  00m00s
[ 39/180] python3-flexparser-0:0.4-6.fc 100% |  26.9 MiB/s |  55.1 KiB |  00m00s
[ 40/180] python3-xmltodict-0:0.14.2-4. 100% |   3.7 MiB/s |  26.6 KiB |  00m00s
[ 41/180] libaec-0:1.1.4-3.fc43.x86_64  100% |  18.4 MiB/s |  37.6 KiB |  00m00s
[ 42/180] python3-pymatreader-0:1.3.1-1 100% | 129.4 KiB/s |  27.6 KiB |  00m00s
[ 43/180] vxl-0:3.5.0-16.fc43.x86_64    100% | 216.5 MiB/s |  14.3 MiB |  00m00s
[ 44/180] python3-certifi-0:2025.07.09- 100% |   7.3 MiB/s |  15.0 KiB |  00m00s
[ 45/180] python3-httpcore-0:1.0.9-5.fc 100% |  38.4 MiB/s | 157.5 KiB |  00m00s
[ 46/180] libyaml-0:0.2.5-17.fc43.x86_6 100% |  58.7 MiB/s |  60.1 KiB |  00m00s
[ 47/180] fonts-filesystem-1:2.0.5-23.f 100% |   4.2 MiB/s |   8.7 KiB |  00m00s
[ 48/180] InsightToolkit5-0:5.4.7-24.fc 100% |  11.2 MiB/s |   4.8 MiB |  00m00s
[ 49/180] dcmtk-0:3.7.0^20260922git8d68 100% |  16.3 MiB/s |   8.1 MiB |  00m00s
[ 50/180] fftw-libs-double-0:3.3.10-16. 100% | 142.8 MiB/s |   1.1 MiB |  00m00s
[ 51/180] fftw-libs-single-0:3.3.10-16. 100% | 134.2 MiB/s |   1.2 MiB |  00m00s
[ 52/180] gdcm-0:3.0.24-14.fc43.x86_64  100% | 168.1 MiB/s |   1.5 MiB |  00m00s
[ 53/180] libminc-0:2.4.03-27.fc43.x86_ 100% |  67.9 MiB/s | 278.0 KiB |  00m00s
[ 54/180] expatpp-0:0-32.20121019gitd8c 100% |  10.6 MiB/s |  21.8 KiB |  00m00s
[ 55/180] libdc1394-0:2.2.7-8.fc43.x86_ 100% |  25.9 MiB/s | 132.8 KiB |  00m00s
[ 56/180] libgeotiff-0:1.7.4-3.fc43.x86 100% |  31.5 MiB/s |  96.6 KiB |  00m00s
[ 57/180] minizip-ng-compat-0:4.0.10-2. 100% |  16.4 MiB/s |  67.1 KiB |  00m00s
[ 58/180] python3-h11-0:0.16.0-5.fc43.n 100% |  19.8 MiB/s |  81.0 KiB |  00m00s
[ 59/180] simpleitk-0:2.5.5-12.fc43.x86 100% |  34.6 MiB/s |  42.4 MiB |  00m01s
[ 60/180] fmt-0:11.2.0-3.fc43.x86_64    100% |  32.6 MiB/s | 100.2 KiB |  00m00s
[ 61/180] jsoncpp-0:1.9.6-2.fc43.x86_64 100% |  24.7 MiB/s | 101.1 KiB |  00m00s
[ 62/180] libharu-0:2.4.5-1.fc43.x86_64 100% | 112.7 MiB/s | 577.0 KiB |  00m00s
[ 63/180] libtheora-1:1.1.1-40.fc43.x86 100% |  55.4 MiB/s | 170.3 KiB |  00m00s
[ 64/180] netcdf-0:4.9.2-15.fc43.x86_64 100% | 114.5 MiB/s | 820.6 KiB |  00m00s
[ 65/180] proj-0:9.6.2-2.fc43.x86_64    100% | 133.0 MiB/s |   1.6 MiB |  00m00s
[ 66/180] openjpeg-0:2.5.4-1.fc43.x86_6 100% |  93.9 MiB/s | 192.2 KiB |  00m00s
[ 67/180] libraw1394-0:2.1.2-24.fc43.x8 100% |  20.9 MiB/s |  64.1 KiB |  00m00s
[ 68/180] blosc-0:1.21.6-7.fc43.x86_64  100% |  28.4 MiB/s |  58.1 KiB |  00m00s
[ 69/180] libzip-0:1.11.4-2.fc43.x86_64 100% |  17.1 MiB/s |  70.0 KiB |  00m00s
[ 70/180] proj-data-0:9.6.2-2.fc43.noar 100% |  36.6 MiB/s |   1.3 MiB |  00m00s
[ 71/180] snappy-0:1.2.2-2.fc43.x86_64  100% |  12.9 MiB/s |  39.6 KiB |  00m00s
[ 72/180] python3-typing-extensions-0:4 100% |  37.2 MiB/s | 114.1 KiB |  00m00s
[ 73/180] python3-dotenv-0:1.2.2-1.fc43 100% |  28.0 MiB/s |  57.4 KiB |  00m00s
[ 74/180] viskores-0:1.2.0-3.fc43.x86_6 100% |  30.0 MiB/s |  16.5 MiB |  00m01s
[ 75/180] python3-platformdirs-0:4.4.0- 100% |   3.0 MiB/s |  49.1 KiB |  00m00s
[ 76/180] flexiblas-0:3.5.0-1.fc43.x86_ 100% |  11.3 MiB/s |  23.1 KiB |  00m00s
[ 77/180] flexiblas-openblas-openmp-0:3 100% |  16.9 MiB/s |  17.3 KiB |  00m00s
[ 78/180] openblas-openmp-0:0.3.29-2.fc 100% | 214.4 MiB/s |   5.4 MiB |  00m00s
[ 79/180] freetype-0:2.13.3-4.fc43.x86_ 100% |  40.2 MiB/s | 411.7 KiB |  00m00s
[ 80/180] python3-cairo-0:1.28.0-4.fc43 100% |  41.1 MiB/s | 126.2 KiB |  00m00s
[ 81/180] python3-contourpy-0:1.4.0-1.f 100% | 139.9 MiB/s | 286.5 KiB |  00m00s
[ 82/180] python3-fonttools-0:4.61.0-1. 100% | 244.7 MiB/s |   3.2 MiB |  00m00s
[ 83/180] python3-kiwisolver-0:1.5.1-1. 100% |  33.7 MiB/s |  69.0 KiB |  00m00s
[ 84/180] python3-pillow-0:11.3.0-10.fc 100% |  56.8 MiB/s |   1.0 MiB |  00m00s
[ 85/180] lcms2-0:2.16-6.fc43.x86_64    100% |  59.5 MiB/s | 182.8 KiB |  00m00s
[ 86/180] libimagequant-0:4.0.3-8.fc43. 100% |  61.3 MiB/s | 313.8 KiB |  00m00s
[ 87/180] libraqm-0:0.10.1-3.fc43.x86_6 100% |  10.9 MiB/s |  22.4 KiB |  00m00s
[ 88/180] libwebp-0:1.6.0-2.fc43.x86_64 100% |  52.8 MiB/s | 324.2 KiB |  00m00s
[ 89/180] libxcb-0:1.17.0-6.fc43.x86_64 100% | 116.1 MiB/s | 237.7 KiB |  00m00s
[ 90/180] fribidi-0:1.0.16-3.fc43.x86_6 100% |  52.1 MiB/s |  53.3 KiB |  00m00s
[ 91/180] libXau-0:1.0.12-3.fc43.x86_64 100% |  10.9 MiB/s |  33.4 KiB |  00m00s
[ 92/180] harfbuzz-0:11.5.1-2.fc43.x86_ 100% | 215.6 MiB/s |   1.1 MiB |  00m00s
[ 93/180] graphite2-0:1.3.14-19.fc43.x8 100% |  46.7 MiB/s |  95.6 KiB |  00m00s
[ 94/180] libpng-2:1.6.58-1.fc43.x86_64 100% |  40.9 MiB/s | 125.8 KiB |  00m00s
[ 95/180] libgfortran-0:15.3.1-1.fc43.x 100% | 158.5 MiB/s | 973.6 KiB |  00m00s
[ 96/180] flexiblas-netlib-0:3.5.0-1.fc 100% |  34.1 MiB/s |   4.5 MiB |  00m00s
[ 97/180] libquadmath-0:15.3.1-1.fc43.x 100% |  41.7 MiB/s | 213.7 KiB |  00m00s
[ 98/180] libjpeg-turbo-0:3.1.3-1.fc43. 100% |  77.1 MiB/s | 236.8 KiB |  00m00s
[ 99/180] libtiff-0:4.7.2-3.fc43.x86_64 100% |  55.1 MiB/s | 225.7 KiB |  00m00s
[100/180] jbigkit-libs-0:2.1-32.fc43.x8 100% |  26.0 MiB/s |  53.2 KiB |  00m00s
[101/180] liblerc-0:4.0.0-9.fc43.x86_64 100% |  70.0 MiB/s | 215.1 KiB |  00m00s
[102/180] rply-0:1.1.4-11.fc43.x86_64   100% |   8.9 MiB/s |  45.4 KiB |  00m00s
[103/180] python3-pooch-0:1.9.0-1.fc43. 100% |  47.5 MiB/s | 145.9 KiB |  00m00s
[104/180] python3-idna-0:3.18-1.fc43.no 100% |  34.0 MiB/s | 104.3 KiB |  00m00s
[105/180] cairo-0:1.18.6-1.fc43.x86_64  100% | 178.2 MiB/s | 729.7 KiB |  00m00s
[106/180] python3-anyio-0:4.13.0-2.fc43 100% |  30.8 MiB/s | 315.8 KiB |  00m00s
[107/180] libXext-0:1.3.6-4.fc43.x86_64 100% |  12.7 MiB/s |  39.1 KiB |  00m00s
[108/180] fontconfig-0:2.17.0-3.fc43.x8 100% |  44.3 MiB/s | 272.2 KiB |  00m00s
[109/180] lzo-0:2.10-15.fc43.x86_64     100% |  33.9 MiB/s |  69.4 KiB |  00m00s
[110/180] libXrender-0:0.9.12-3.fc43.x8 100% |   8.7 MiB/s |  26.9 KiB |  00m00s
[111/180] default-fonts-core-sans-0:4.2 100% |   9.7 MiB/s |  29.9 KiB |  00m00s
[112/180] pixman-0:0.46.2-2.fc43.x86_64 100% |  71.4 MiB/s | 292.5 KiB |  00m00s
[113/180] xml-common-0:0.6.3-67.fc43.no 100% |   7.6 MiB/s |  31.0 KiB |  00m00s
[114/180] openblas-0:0.3.29-2.fc43.x86_ 100% |  10.3 MiB/s |  42.3 KiB |  00m00s
[115/180] abattis-cantarell-vf-fonts-0: 100% |  14.7 MiB/s | 120.1 KiB |  00m00s
[116/180] libogg-2:1.3.6-2.fc43.x86_64  100% |  16.3 MiB/s |  33.5 KiB |  00m00s
[117/180] libX11-0:1.8.13-1.fc43.x86_64 100% | 128.0 MiB/s | 655.2 KiB |  00m00s
[118/180] libX11-common-0:1.8.13-1.fc43 100% |  28.6 MiB/s | 175.6 KiB |  00m00s
[119/180] CharLS-0:2.4.3-2.fc43.x86_64  100% |  29.3 MiB/s | 120.0 KiB |  00m00s
[120/180] google-noto-fonts-common-0:20 100% |   8.5 MiB/s |  17.3 KiB |  00m00s
[121/180] google-noto-sans-vf-fonts-0:2 100% |  54.6 MiB/s | 614.5 KiB |  00m00s
[122/180] freexl-0:2.0.0-15.fc43.x86_64 100% |   5.0 MiB/s |  45.8 KiB |  00m00s
[123/180] imath-0:3.1.12-4.fc43.x86_64  100% |  16.5 MiB/s | 101.4 KiB |  00m00s
[124/180] libgta-0:1.2.1-16.fc43.x86_64 100% |  11.7 MiB/s |  35.9 KiB |  00m00s
[125/180] libkml-0:1.3.0-56.fc43.x86_64 100% |  69.8 MiB/s | 357.3 KiB |  00m00s
[126/180] libpq-0:18.0-3.fc43.x86_64    100% |  53.4 MiB/s | 273.6 KiB |  00m00s
[127/180] libspatialite-0:5.1.0-11.fc43 100% |  88.3 MiB/s |   3.1 MiB |  00m00s
[128/180] muParser-0:2.3.5-3.fc43.x86_6 100% |  34.8 MiB/s | 178.4 KiB |  00m00s
[129/180] gdal-libs-0:3.11.5-1.fc43.x86 100% | 107.2 MiB/s |  10.3 MiB |  00m00s
[130/180] openexr-libs-0:3.2.4-6.fc43.x 100% |  58.8 MiB/s |   1.1 MiB |  00m00s
[131/180] vtk-0:9.7.0-7.fc43.x86_64     100% |  37.1 MiB/s |  40.7 MiB |  00m01s
[132/180] xerces-c-0:3.3.0-8.fc43.x86_6 100% |  24.8 MiB/s |   1.0 MiB |  00m00s
[133/180] librttopo-0:1.1.0-17.fc43.x86 100% |   5.2 MiB/s | 206.9 KiB |  00m00s
[134/180] pugixml-0:1.16-1.fc43.x86_64  100% |  21.5 MiB/s | 109.9 KiB |  00m00s
[135/180] python3-click-1:8.1.7-12.fc43 100% |  49.6 MiB/s | 254.2 KiB |  00m00s
[136/180] python3-fsspec-0:2026.6.0-1.f 100% |  77.2 MiB/s | 474.5 KiB |  00m00s
[137/180] double-conversion-0:3.4.0-1.f 100% |  12.7 MiB/s |  51.9 KiB |  00m00s
[138/180] python3-requests-0:2.33.1-1.f 100% |  39.2 MiB/s | 160.6 KiB |  00m00s
[139/180] python3-charset-normalizer-0: 100% |  28.6 MiB/s | 117.3 KiB |  00m00s
[140/180] python3-olefile-0:0.47-12.fc4 100% |  24.4 MiB/s |  74.9 KiB |  00m00s
[141/180] cfitsio-0:4.6.3-1.fc43.x86_64 100% |  60.5 MiB/s | 619.7 KiB |  00m00s
[142/180] glib2-0:2.86.5-1.fc43.x86_64  100% | 171.3 MiB/s |   3.1 MiB |  00m00s
[143/180] giflib-0:5.2.2-9.fc43.x86_64  100% |   7.3 MiB/s |  52.2 KiB |  00m00s
[144/180] utf8proc-0:2.10.0-2.fc43.x86_ 100% |  16.0 MiB/s |  81.8 KiB |  00m00s
[145/180] libarrow-doc-0:20.0.0-10.fc43 100% |   9.4 MiB/s |  28.8 KiB |  00m00s
[146/180] parquet-libs-0:20.0.0-10.fc43 100% |  66.7 MiB/s |   1.0 MiB |  00m00s
[147/180] libarrow-dataset-libs-0:20.0. 100% |  55.5 MiB/s | 511.1 KiB |  00m00s
[148/180] libarrow-0:20.0.0-10.fc43.x86 100% | 113.6 MiB/s |   6.2 MiB |  00m00s
[149/180] thrift-0:0.20.0-8.fc43.x86_64 100% |  75.1 MiB/s |   1.8 MiB |  00m00s
[150/180] libarrow-acero-libs-0:20.0.0- 100% |  63.0 MiB/s | 516.0 KiB |  00m00s
[151/180] libdeflate-0:1.26-1.fc43.x86_ 100% |  13.3 MiB/s |  68.3 KiB |  00m00s
[152/180] mariadb-connector-c-config-0: 100% |   5.3 MiB/s |  21.6 KiB |  00m00s
[153/180] mariadb-connector-c-0:3.4.9-2 100% |  40.3 MiB/s | 206.4 KiB |  00m00s
[154/180] gpgmepp-0:1.24.3-6.fc43.x86_6 100% |  34.9 MiB/s | 142.9 KiB |  00m00s
[155/180] poppler-0:25.07.0-5.fc43.x86_ 100% | 145.0 MiB/s |   1.3 MiB |  00m00s
[156/180] poppler-data-0:0.4.11-10.fc43 100% | 163.8 MiB/s |   2.0 MiB |  00m00s
[157/180] gpgme-0:1.24.3-6.fc43.x86_64  100% |  26.7 MiB/s | 218.5 KiB |  00m00s
[158/180] unixODBC-0:2.3.14-2.fc43.x86_ 100% |  81.0 MiB/s | 414.5 KiB |  00m00s
[159/180] uriparser-0:1.0.2-1.fc43.x86_ 100% |  14.0 MiB/s |  71.8 KiB |  00m00s
[160/180] python3-urllib3-0:2.8.0-3.fc4 100% |  75.7 MiB/s | 309.9 KiB |  00m00s
[161/180] nspr-0:4.39.0-7.fc43.x86_64   100% |  33.8 MiB/s | 138.4 KiB |  00m00s
[162/180] nss-softokn-0:3.131.0-1.fc43. 100% |  86.1 MiB/s | 440.7 KiB |  00m00s
[163/180] nss-0:3.131.0-1.fc43.x86_64   100% |  70.8 MiB/s | 725.0 KiB |  00m00s
[164/180] nss-util-0:3.131.0-1.fc43.x86 100% |  17.1 MiB/s |  87.3 KiB |  00m00s
[165/180] geos-0:3.14.1-1.fc43.x86_64   100% |   9.8 MiB/s |   1.3 MiB |  00m00s
[166/180] nss-sysinit-0:3.131.0-1.fc43. 100% |   6.4 MiB/s |  19.6 KiB |  00m00s
[167/180] nss-softokn-freebl-0:3.131.0- 100% |  76.7 MiB/s | 392.7 KiB |  00m00s
[168/180] boost-program-options-0:1.83. 100% |  10.8 MiB/s | 110.7 KiB |  00m00s
[169/180] liborc2-0:2.1.4-1.fc43.x86_64 100% |  44.9 MiB/s | 551.3 KiB |  00m00s
[170/180] protobuf-0:3.19.6-18.fc43.x86 100% |  71.9 MiB/s |   1.0 MiB |  00m00s
[171/180] re2-2:20251105-1.fc43.x86_64  100% |  37.6 MiB/s | 231.3 KiB |  00m00s
[172/180] hdf-libs-0:4.3.0-4.fc43.x86_6 100% |  35.6 MiB/s | 291.3 KiB |  00m00s
[173/180] abseil-cpp-0:20250814.2-2.fc4 100% |  57.8 MiB/s | 768.8 KiB |  00m00s
[174/180] libtirpc-0:1.3.7-1.rc1.fc43.x 100% |  11.6 MiB/s |  94.9 KiB |  00m00s
[175/180] arpack-0:3.9.1-7.fc43.x86_64  100% |  40.9 MiB/s | 209.6 KiB |  00m00s
[176/180] libicu-0:77.1-1.fc43.x86_64   100% | 178.4 MiB/s |  10.7 MiB |  00m00s
[177/180] flexiblas-netlib64-0:3.5.0-1. 100% | 113.9 MiB/s |   4.3 MiB |  00m00s
[178/180] flexiblas-openblas-openmp64-0 100% |   4.2 MiB/s |  17.2 KiB |  00m00s
[179/180] openblas-openmp64-0:0.3.29-2. 100% | 326.7 MiB/s |   5.2 MiB |  00m00s
[180/180] armadillo12-0:12.8.1-1.fc43.x 100% | 141.0 KiB/s |  28.9 KiB |  00m00s
--------------------------------------------------------------------------------
[180/180] Total                         100% |  98.9 MiB/s | 277.0 MiB |  00m03s
Running transaction
[  1/182] Verify package files          100% | 177.0   B/s | 180.0   B |  00m01s
[  2/182] Prepare transaction           100% |   2.1 KiB/s | 180.0   B |  00m00s
[  3/182] Installing libpng-2:1.6.58-1. 100% | 241.1 MiB/s | 246.9 KiB |  00m00s
[  4/182] Installing libjpeg-turbo-0:3. 100% | 394.5 MiB/s | 808.0 KiB |  00m00s
[  5/182] Installing libgfortran-0:15.3 100% | 417.9 MiB/s |   3.3 MiB |  00m00s
[  6/182] Installing python3-typing-ext 100% | 541.3 MiB/s | 554.3 KiB |  00m00s
[  7/182] Installing nspr-0:4.39.0-7.fc 100% | 309.5 MiB/s | 317.0 KiB |  00m00s
[  8/182] Installing nss-util-0:3.131.0 100% | 200.9 MiB/s | 205.7 KiB |  00m00s
[  9/182] Installing libarrow-doc-0:20. 100% |   0.0   B/s | 117.9 KiB |  00m00s
[ 10/182] Installing python3-idna-0:3.1 100% |  37.0 MiB/s | 530.2 KiB |  00m00s
[ 11/182] Installing openjpeg-0:2.5.4-1 100% | 223.8 MiB/s | 458.2 KiB |  00m00s
[ 12/182] Installing fonts-filesystem-1 100% |   0.0   B/s | 788.0   B |  00m00s
[ 13/182] Installing libaec-0:1.1.4-3.f 100% |  90.6 MiB/s |  92.8 KiB |  00m00s
[ 14/182] Installing hdf5-0:1.14.6-6.fc 100% | 220.1 MiB/s |   6.4 MiB |  00m00s
[ 15/182] Installing geos-0:3.14.1-1.fc 100% | 206.1 MiB/s |   4.1 MiB |  00m00s
[ 16/182] Installing libwebp-0:1.6.0-2. 100% | 312.7 MiB/s | 960.7 KiB |  00m00s
[ 17/182] Installing snappy-0:1.2.2-2.f 100% |  71.8 MiB/s |  73.5 KiB |  00m00s
[ 18/182] Installing minizip-ng-compat- 100% | 159.5 MiB/s | 163.3 KiB |  00m00s
[ 19/182] Installing freexl-0:2.0.0-15. 100% |  88.3 MiB/s |  90.5 KiB |  00m00s
[ 20/182] Installing blosc-0:1.21.6-7.f 100% | 126.0 MiB/s | 129.0 KiB |  00m00s
[ 21/182] Installing python3-typing-ins 100% |  95.2 MiB/s |  97.5 KiB |  00m00s
[ 22/182] Installing libdeflate-0:1.26- 100% | 122.4 MiB/s | 125.3 KiB |  00m00s
[ 23/182] Installing imath-0:3.1.12-4.f 100% | 186.5 MiB/s | 381.9 KiB |  00m00s
[ 24/182] Installing libogg-2:1.3.6-2.f 100% |   0.0   B/s |  47.1 KiB |  00m00s
[ 25/182] Installing openblas-0:0.3.29- 100% |   0.0   B/s | 113.5 KiB |  00m00s
[ 26/182] Installing liblerc-0:4.0.0-9. 100% | 305.5 MiB/s | 625.6 KiB |  00m00s
[ 27/182] Installing libquadmath-0:15.3 100% | 311.7 MiB/s | 319.2 KiB |  00m00s
[ 28/182] Installing lcms2-0:2.16-6.fc4 100% | 212.6 MiB/s | 435.3 KiB |  00m00s
[ 29/182] Installing python3-platformdi 100% | 184.4 MiB/s | 188.8 KiB |  00m00s
[ 30/182] Installing python3-certifi-0: 100% |  10.9 MiB/s |  11.2 KiB |  00m00s
[ 31/182] Installing libqhull_r-1:8.0.2 100% | 472.8 MiB/s | 484.1 KiB |  00m00s
[ 32/182] Installing python3-six-0:1.17 100% | 117.5 MiB/s | 120.3 KiB |  00m00s
[ 33/182] Installing python3-array-api- 100% | 172.3 MiB/s | 529.3 KiB |  00m00s
[ 34/182] Installing python3-array-api- 100% | 236.9 MiB/s |   1.4 MiB |  00m00s
[ 35/182] Installing python3-dateutil-1 100% | 290.1 MiB/s | 891.1 KiB |  00m00s
[ 36/182] Installing openblas-openmp-0: 100% | 642.5 MiB/s |  43.7 MiB |  00m00s
[ 37/182] Installing flexiblas-netlib-0 100% | 336.9 MiB/s |  16.5 MiB |  00m00s
[ 38/182] Installing flexiblas-0:3.5.0- 100% |   0.0   B/s |  39.2 KiB |  00m00s
[ 39/182] Installing flexiblas-openblas 100% |  13.0 MiB/s |  40.1 KiB |  00m00s
[ 40/182] Installing python3-numpy-1:2. 100% | 365.6 MiB/s |  41.7 MiB |  00m00s
[ 41/182] Installing python3-numpy-f2py 100% |  98.7 MiB/s |   2.2 MiB |  00m00s
[ 42/182] Installing python3-contourpy- 100% | 212.4 MiB/s | 870.1 KiB |  00m00s
[ 43/182] Installing openblas-openmp64- 100% | 654.1 MiB/s |  43.8 MiB |  00m00s
[ 44/182] Installing flexiblas-openblas 100% |   0.0   B/s |  40.1 KiB |  00m00s
[ 45/182] Installing flexiblas-netlib64 100% | 347.8 MiB/s |  16.7 MiB |  00m00s
[ 46/182] Installing arpack-0:3.9.1-7.f 100% | 314.4 MiB/s | 643.9 KiB |  00m00s
[ 47/182] Installing armadillo12-0:12.8 100% |  81.3 MiB/s |  83.2 KiB |  00m00s
[ 48/182] Installing libtheora-1:1.1.1- 100% | 234.2 MiB/s | 479.7 KiB |  00m00s
[ 49/182] Installing openexr-libs-0:3.2 100% | 503.6 MiB/s |   6.5 MiB |  00m00s
[ 50/182] Installing librttopo-0:1.1.0- 100% | 247.3 MiB/s | 506.4 KiB |  00m00s
[ 51/182] Installing dejavu-sans-fonts- 100% | 459.1 MiB/s |   5.5 MiB |  00m00s
[ 52/182] Installing abattis-cantarell- 100% | 189.9 MiB/s | 194.4 KiB |  00m00s
[ 53/182] Installing python3-anyio-0:4. 100% | 297.2 MiB/s |   1.5 MiB |  00m00s
[ 54/182] Installing python3-urllib3-0: 100% | 294.8 MiB/s |   1.2 MiB |  00m00s
[ 55/182] Installing nss-softokn-freebl 100% | 356.8 MiB/s |   1.1 MiB |  00m00s
[ 56/182] Installing nss-softokn-0:3.13 100% | 403.8 MiB/s |   2.0 MiB |  00m00s
[ 57/182] Installing nss-sysinit-0:3.13 100% |   1.6 MiB/s |  19.1 KiB |  00m00s
[ 58/182] Installing nss-0:3.131.0-1.fc 100% | 159.3 MiB/s |   1.9 MiB |  00m00s
[ 59/182] Installing python3-pydantic-c 100% | 382.2 MiB/s |   5.4 MiB |  00m00s
[ 60/182] Installing python3-flexcache- 100% |  59.2 MiB/s |  60.6 KiB |  00m00s
[ 61/182] Installing python3-flexparser 100% | 260.8 MiB/s | 267.1 KiB |  00m00s
[ 62/182] Installing libharu-0:2.4.5-1. 100% | 282.5 MiB/s |   1.7 MiB |  00m00s
[ 63/182] Installing libtirpc-0:1.3.7-1 100% | 197.6 MiB/s | 202.3 KiB |  00m00s
[ 64/182] Installing hdf-libs-0:4.3.0-4 100% | 224.1 MiB/s | 688.3 KiB |  00m00s
[ 65/182] Installing abseil-cpp-0:20250 100% | 251.5 MiB/s |   2.5 MiB |  00m00s
[ 66/182] Installing libicu-0:77.1-1.fc 100% | 404.3 MiB/s |  36.4 MiB |  00m00s
[ 67/182] Installing re2-2:20251105-1.f 100% | 285.5 MiB/s | 584.7 KiB |  00m00s
[ 68/182] Installing protobuf-0:3.19.6- 100% | 408.7 MiB/s |   3.3 MiB |  00m00s
[ 69/182] Installing liborc2-0:2.1.4-1. 100% | 359.8 MiB/s |   1.8 MiB |  00m00s
[ 70/182] Installing boost-program-opti 100% | 275.2 MiB/s | 281.8 KiB |  00m00s
[ 71/182] Installing uriparser-0:1.0.2- 100% |  13.0 MiB/s | 186.6 KiB |  00m00s
[ 72/182] Installing libkml-0:1.3.0-56. 100% | 294.6 MiB/s |   1.2 MiB |  00m00s
[ 73/182] Installing unixODBC-0:2.3.14- 100% |  63.5 MiB/s |   1.0 MiB |  00m00s
[ 74/182] Installing gpgme-0:1.24.3-6.f 100% |  44.4 MiB/s | 590.4 KiB |  00m00s
[ 75/182] Installing gpgmepp-0:1.24.3-6 100% | 210.1 MiB/s | 430.4 KiB |  00m00s
[ 76/182] Installing poppler-data-0:0.4 100% | 399.7 MiB/s |  12.4 MiB |  00m00s
[ 77/182] Installing mariadb-connector- 100% |   0.0   B/s |  27.2 KiB |  00m00s
[ 78/182] Installing mariadb-connector- 100% | 243.7 MiB/s | 499.1 KiB |  00m00s
[ 79/182] Installing thrift-0:0.20.0-8. 100% | 210.1 MiB/s |   5.5 MiB |  00m00s
[ 80/182] Installing utf8proc-0:2.10.0- 100% | 364.7 MiB/s | 373.4 KiB |  00m00s
[ 81/182] Installing libarrow-0:20.0.0- 100% | 501.8 MiB/s |  26.6 MiB |  00m00s
[ 82/182] Installing parquet-libs-0:20. 100% | 412.3 MiB/s |   3.3 MiB |  00m00s
[ 83/182] Installing libarrow-acero-lib 100% | 349.5 MiB/s |   1.4 MiB |  00m00s
[ 84/182] Installing libarrow-dataset-l 100% | 367.6 MiB/s |   1.5 MiB |  00m00s
[ 85/182] Installing giflib-0:5.2.2-9.f 100% | 115.0 MiB/s | 117.8 KiB |  00m00s
[ 86/182] Installing cfitsio-0:4.6.3-1. 100% | 379.4 MiB/s |   1.9 MiB |  00m00s
[ 87/182] Installing glib2-0:2.86.5-1.f 100% | 319.2 MiB/s |  15.0 MiB |  00m00s
[ 88/182] Installing python3-olefile-0: 100% | 341.6 MiB/s | 349.8 KiB |  00m00s
[ 89/182] Installing python3-charset-no 100% |  27.5 MiB/s | 365.8 KiB |  00m00s
[ 90/182] Installing python3-requests-0 100% | 239.0 MiB/s | 489.5 KiB |  00m00s
[ 91/182] Installing python3-pooch-0:1. 100% |  82.1 MiB/s | 672.8 KiB |  00m00s
[ 92/182] Installing python3-scipy-0:1. 100% | 382.5 MiB/s |  66.9 MiB |  00m00s
[ 93/182] Installing double-conversion- 100% |  97.3 MiB/s |  99.6 KiB |  00m00s
[ 94/182] Installing python3-fsspec-0:2 100% | 273.3 MiB/s |   1.9 MiB |  00m00s
[ 95/182] Installing python3-click-1:8. 100% | 390.3 MiB/s |   1.2 MiB |  00m00s
[ 96/182] Installing pugixml-0:1.16-1.f 100% | 273.4 MiB/s | 279.9 KiB |  00m00s
[ 97/182] Installing xerces-c-0:3.3.0-8 100% | 471.2 MiB/s |   3.8 MiB |  00m00s
[ 98/182] Installing muParser-0:2.3.5-3 100% | 269.9 MiB/s | 552.7 KiB |  00m00s
[ 99/182] Installing libpq-0:18.0-3.fc4 100% | 334.7 MiB/s |   1.0 MiB |  00m00s
[100/182] Installing libgta-0:1.2.1-16. 100% |  65.9 MiB/s |  67.5 KiB |  00m00s
[101/182] Installing google-noto-fonts- 100% |   0.0   B/s |  18.5 KiB |  00m00s
[102/182] Installing google-noto-sans-v 100% | 347.8 MiB/s |   1.4 MiB |  00m00s
[103/182] Installing default-fonts-core 100% |  17.8 MiB/s |  18.2 KiB |  00m00s
[104/182] Installing CharLS-0:2.4.3-2.f 100% | 329.9 MiB/s | 337.8 KiB |  00m00s
[105/182] Installing gdcm-0:3.0.24-14.f 100% | 476.6 MiB/s |   8.1 MiB |  00m00s
[106/182] Installing libX11-common-0:1. 100% |  98.9 MiB/s |   1.2 MiB |  00m00s
[107/182] Installing xml-common-0:0.6.3 100% |  79.2 MiB/s |  81.1 KiB |  00m00s
[108/182] Installing pixman-0:0.46.2-2. 100% | 347.4 MiB/s | 711.4 KiB |  00m00s
[109/182] Installing lzo-0:2.10-15.fc43 100% | 176.3 MiB/s | 180.5 KiB |  00m00s
[110/182] Installing rply-0:1.1.4-11.fc 100% |   8.9 MiB/s | 118.4 KiB |  00m00s
[111/182] Installing jbigkit-libs-0:2.1 100% | 112.7 MiB/s | 115.4 KiB |  00m00s
[112/182] Installing libtiff-0:4.7.2-3. 100% | 207.9 MiB/s | 638.8 KiB |  00m00s
[113/182] Installing dcmtk-0:3.7.0^2026 100% | 418.9 MiB/s |  42.7 MiB |  00m00s
[114/182] Installing graphite2-0:1.3.14 100% |  14.6 MiB/s | 193.9 KiB |  00m00s
[115/182] Installing freetype-0:2.13.3- 100% | 277.3 MiB/s | 851.8 KiB |  00m00s
[116/182] Installing harfbuzz-0:11.5.1- 100% | 353.5 MiB/s |   2.8 MiB |  00m00s
[117/182] Installing fontconfig-0:2.17. 100% | 758.0 KiB/s | 785.3 KiB |  00m01s
[118/182] Installing poppler-0:25.07.0- 100% | 381.4 MiB/s |   3.8 MiB |  00m00s
[119/182] Installing libXau-0:1.0.12-3. 100% |  72.7 MiB/s |  74.5 KiB |  00m00s
[120/182] Installing libxcb-0:1.17.0-6. 100% | 270.0 MiB/s |   1.1 MiB |  00m00s
[121/182] Installing libX11-0:1.8.13-1. 100% | 320.4 MiB/s |   1.3 MiB |  00m00s
[122/182] Installing libXext-0:1.3.6-4. 100% |  89.1 MiB/s |  91.2 KiB |  00m00s
[123/182] Installing libXrender-0:0.9.1 100% |  46.2 MiB/s |  47.3 KiB |  00m00s
[124/182] Installing cairo-0:1.18.6-1.f 100% | 355.3 MiB/s |   1.8 MiB |  00m00s
[125/182] Installing python3-cairo-0:1. 100% | 242.4 MiB/s | 496.4 KiB |  00m00s
[126/182] Installing fribidi-0:1.0.16-3 100% |  14.5 MiB/s | 192.8 KiB |  00m00s
[127/182] Installing libraqm-0:0.10.1-3 100% |   0.0   B/s |  29.7 KiB |  00m00s
[128/182] Installing libimagequant-0:4. 100% | 225.4 MiB/s | 692.5 KiB |  00m00s
[129/182] Installing python3-pillow-0:1 100% | 331.2 MiB/s |   4.3 MiB |  00m00s
[130/182] Installing python3-kiwisolver 100% |  38.6 MiB/s | 157.9 KiB |  00m00s
[131/182] Installing python3-fonttools- 100% | 358.6 MiB/s |  19.4 MiB |  00m00s
[132/182] Installing python3-dotenv-0:1 100% |  76.2 MiB/s | 156.0 KiB |  00m00s
[133/182] Installing proj-data-0:9.6.2- 100% | 514.5 MiB/s |   9.3 MiB |  00m00s
[134/182] Installing proj-0:9.6.2-2.fc4 100% | 167.9 MiB/s |   4.5 MiB |  00m00s
[135/182] Installing libgeotiff-0:1.7.4 100% |  22.1 MiB/s | 293.8 KiB |  00m00s
[136/182] Installing libspatialite-0:5. 100% | 509.8 MiB/s |  15.3 MiB |  00m00s
[137/182] Installing libzip-0:1.11.4-2. 100% | 133.7 MiB/s | 137.0 KiB |  00m00s
[138/182] Installing netcdf-0:4.9.2-15. 100% | 123.5 MiB/s |   2.3 MiB |  00m00s
[139/182] Installing libminc-0:2.4.03-2 100% | 226.6 MiB/s | 696.1 KiB |  00m00s
[140/182] Installing gdal-libs-0:3.11.5 100% | 385.2 MiB/s |  31.2 MiB |  00m00s
[141/182] Installing libraw1394-0:2.1.2 100% |  12.2 MiB/s | 162.2 KiB |  00m00s
[142/182] Installing libdc1394-0:2.2.7- 100% | 344.5 MiB/s | 352.8 KiB |  00m00s
[143/182] Installing jsoncpp-0:1.9.6-2. 100% | 253.1 MiB/s | 259.2 KiB |  00m00s
[144/182] Installing fmt-0:11.2.0-3.fc4 100% | 262.0 MiB/s | 268.3 KiB |  00m00s
[145/182] Installing viskores-0:1.2.0-3 100% | 639.6 MiB/s | 133.7 MiB |  00m00s
[146/182] Installing vtk-0:9.7.0-7.fc43 100% | 542.9 MiB/s | 241.6 MiB |  00m00s
[147/182] Installing python3-h11-0:0.16 100% | 140.6 MiB/s | 287.9 KiB |  00m00s
[148/182] Installing python3-httpcore-0 100% | 213.5 MiB/s | 874.4 KiB |  00m00s
[149/182] Installing python3-httpx-0:0. 100% | 328.0 MiB/s |   1.0 MiB |  00m00s
[150/182] Installing expatpp-0:0-32.201 100% |  19.2 MiB/s |  39.3 KiB |  00m00s
[151/182] Installing vxl-0:3.5.0-16.fc4 100% | 354.6 MiB/s |  53.2 MiB |  00m00s
[152/182] Installing fftw-libs-single-0 100% | 400.0 MiB/s |   3.6 MiB |  00m00s
[153/182] Installing fftw-libs-double-0 100% | 378.7 MiB/s |   3.4 MiB |  00m00s
[154/182] Installing InsightToolkit5-0: 100% | 503.3 MiB/s |  28.7 MiB |  00m00s
[155/182] Installing simpleitk-0:2.5.5- 100% | 624.3 MiB/s | 285.9 MiB |  00m00s
[156/182] Installing libyaml-0:0.2.5-17 100% | 132.8 MiB/s | 136.0 KiB |  00m00s
[157/182] Installing python3-pyyaml-0:6 100% | 262.8 MiB/s | 807.4 KiB |  00m00s
[158/182] Installing python3-xmltodict- 100% |  57.1 MiB/s |  58.5 KiB |  00m00s
[159/182] Installing python3-matplotlib 100% | 416.5 MiB/s |   7.9 MiB |  00m00s
[160/182] Installing python3-pyparsing- 100% | 343.1 MiB/s |   1.0 MiB |  00m00s
[161/182] Installing python3-cycler-0:0 100% |  39.3 MiB/s |  40.3 KiB |  00m00s
[162/182] Installing python3-filelock-0 100% | 101.4 MiB/s | 103.9 KiB |  00m00s
[163/182] Installing python3-annotated- 100% |  86.2 MiB/s |  88.2 KiB |  00m00s
[164/182] Installing python3-pydantic-0 100% | 366.7 MiB/s |   5.1 MiB |  00m00s
[165/182] Installing python3-markupsafe 100% |  64.2 MiB/s |  65.8 KiB |  00m00s
[166/182] Installing python3-cached_pro 100% |  37.3 MiB/s |  38.2 KiB |  00m00s
[167/182] Installing liblzf-0:3.6-33.fc 100% |   4.2 MiB/s |  51.9 KiB |  00m00s
[168/182] Installing python3-h5py-0:3.1 100% | 347.8 MiB/s |   4.2 MiB |  00m00s
[169/182] Installing python3-tqdm-0:4.6 100% |  38.9 MiB/s | 597.7 KiB |  00m00s
[170/182] Installing python3-huggingfac 100% | 206.3 MiB/s |   6.6 MiB |  00m00s
[171/182] Installing python3-pymatreade 100% |  68.6 MiB/s |  70.2 KiB |  00m00s
[172/182] Installing python3-jinja2-0:3 100% | 386.5 MiB/s |   3.1 MiB |  00m00s
[173/182] Installing python3-numpydanti 100% | 158.7 MiB/s | 650.0 KiB |  00m00s
[174/182] Installing python3-pydantic-s 100% | 111.9 MiB/s | 573.0 KiB |  00m00s
[175/182] Installing python3-matplotlib 100% | 425.9 MiB/s |  31.9 MiB |  00m00s
[176/182] Installing python3-simpleitk- 100% | 579.8 MiB/s |  15.7 MiB |  00m00s
[177/182] Installing python3-pint-0:0.2 100% | 144.4 MiB/s |   3.5 MiB |  00m00s
[178/182] Installing python3-array-api- 100% | 175.2 MiB/s | 717.6 KiB |  00m00s
[179/182] Installing python3-ipax-0:0.1 100% | 333.5 MiB/s |   3.0 MiB |  00m00s
[180/182] Installing python3-safetensor 100% | 309.3 MiB/s | 950.1 KiB |  00m00s
[181/182] Installing python3-regex-0:20 100% | 300.1 MiB/s |   2.1 MiB |  00m00s
[182/182] Installing python3-pydicom-0: 100% |  88.9 MiB/s |  14.0 MiB |  00m00s
Warning: skipped OpenPGP checks for 13 packages from repository: copr_base
Complete!
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.nNhkpR
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ cd pyradplan-0.8.0
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1   '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir --output /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-dep-overrides
Handling setuptools>=64 from build-system.requires
Requirement satisfied: setuptools>=64
   (installed: setuptools 78.1.1)
Handling setuptools-scm>=8 from build-system.requires
Requirement satisfied: setuptools-scm>=8
   (installed: setuptools-scm 8.3.1)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running egg_info
writing pyRadPlan.egg-info/PKG-INFO
writing dependency_links to pyRadPlan.egg-info/dependency_links.txt
writing entry points to pyRadPlan.egg-info/entry_points.txt
writing requirements to pyRadPlan.egg-info/requires.txt
writing top-level names to pyRadPlan.egg-info/top_level.txt
reading manifest file 'pyRadPlan.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'pyRadPlan.egg-info/SOURCES.txt'
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running dist_info
writing pyRadPlan.egg-info/PKG-INFO
writing dependency_links to pyRadPlan.egg-info/dependency_links.txt
writing entry points to pyRadPlan.egg-info/entry_points.txt
writing requirements to pyRadPlan.egg-info/requires.txt
writing top-level names to pyRadPlan.egg-info/top_level.txt
reading manifest file 'pyRadPlan.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'pyRadPlan.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/pyradplan-0.8.0.dist-info'
Handling scipy>=1.13.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: scipy>=1.13.0
   (installed: scipy 1.16.2)
Handling numpy>=2.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: numpy>=2.0
   (installed: numpy 2.3.5)
Handling array_api_compat from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: array_api_compat
   (installed: array_api_compat 1.15.0)
Handling array_api_strict from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: array_api_strict
   (installed: array_api_strict 2.0.1)
Handling array_api_extra from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: array_api_extra
   (installed: array_api_extra 0.11.5)
Handling ipax>=0.13.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: ipax>=0.13.0
   (installed: ipax 0.13.0)
Handling pymatreader>=1.0.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pymatreader>=1.0.0
   (installed: pymatreader 1.3.1)
Handling h5py>=3.11.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: h5py>=3.11.0
   (installed: h5py 3.13.0)
Handling regex>=2024.5.15 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: regex>=2024.5.15
   (installed: regex 2026.9.10)
Handling pydicom>=2.4.4 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pydicom>=2.4.4
   (installed: pydicom 3.0.2)
Handling matplotlib>=3.9.2 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: matplotlib>=3.9.2
   (installed: matplotlib 3.10.9)
Handling pydantic<2.20,>=2.10 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pydantic<2.20,>=2.10
   (installed: pydantic 2.12.5)
Handling pydantic-settings<3.0,>=2.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pydantic-settings<3.0,>=2.0
   (installed: pydantic-settings 2.15.0)
Handling numpydantic>=1.6.9 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: numpydantic>=1.6.9
   (installed: numpydantic 2.0.0)
Handling SimpleITK>=2.4.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: SimpleITK>=2.4.0
Handling tqdm>=4.0.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: tqdm>=4.0.0
   (installed: tqdm 4.67.2)
Handling pint>=0.24.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pint>=0.24.0
   (installed: pint 0.25.2)
Handling jinja2 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: jinja2
   (installed: jinja2 3.1.6)
Handling huggingface_hub>=0.23 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: huggingface_hub>=0.23
   (installed: huggingface_hub 1.24.0)
Handling safetensors from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: safetensors
   (installed: safetensors 0.7.0)
Handling matlabengine; extra == "matlab" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: matlabengine; extra == "matlab"
Handling oct2py; extra == "octave" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: oct2py; extra == "octave"
Handling pyside6-essentials<6.12,>=6.4; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pyside6-essentials<6.12,>=6.4; extra == "gui"
Handling pyqtgraph>=0.12.0; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pyqtgraph>=0.12.0; extra == "gui"
Handling contourpy>=1.0.1; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: contourpy>=1.0.1; extra == "gui"
Handling numba>=0.61.0; extra == "numba" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: numba>=0.61.0; extra == "numba"
Handling torch>=2.0; extra == "torch" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: torch>=2.0; extra == "torch"
Handling cupy>=13.0; extra == "cupy" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: cupy>=13.0; extra == "cupy"
Handling jax>=0.4.35; extra == "jax" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: jax>=0.4.35; extra == "jax"
Handling line_profiler>=4.0; extra == "profiling" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: line_profiler>=4.0; extra == "profiling"
Handling pytest; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest; extra == "dev"
Handling coverage; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: coverage; extra == "dev"
Handling pytest-cov; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest-cov; extra == "dev"
Handling pytest-benchmark; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest-benchmark; extra == "dev"
Handling pre-commit; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pre-commit; extra == "dev"
Handling ruff<0.18; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: ruff<0.18; extra == "dev"
Handling sphinx; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx; extra == "dev"
Handling numpydoc; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: numpydoc; extra == "dev"
Handling pydata-sphinx-theme; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pydata-sphinx-theme; extra == "dev"
Handling sphinx-design; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx-design; extra == "dev"
Handling autodoc_pydantic; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: autodoc_pydantic; extra == "dev"
Handling sphinx-autodoc-typehints; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx-autodoc-typehints; extra == "dev"
Handling myst-nb; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: myst-nb; extra == "dev"
Handling jupytext; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: jupytext; extra == "dev"
+ cat /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires
+ rm -rfv pyradplan-0.8.0.dist-info/
removed 'pyradplan-0.8.0.dist-info/licenses/LICENSE'
removed directory 'pyradplan-0.8.0.dist-info/licenses'
removed 'pyradplan-0.8.0.dist-info/METADATA'
removed 'pyradplan-0.8.0.dist-info/top_level.txt'
removed 'pyradplan-0.8.0.dist-info/entry_points.txt'
removed directory 'pyradplan-0.8.0.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc43.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 Copr repository                        100% |  10.9 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% |  50.5 KiB/s |  23.5 KiB |  00m00s
 updates                                100% | 114.2 KiB/s |  18.6 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.24.0-1.fc43.noarch" is already installed.
Package "python3-devel-3.14.8-1.fc43.x86_64" is already installed.
Package "python3-pytest-8.3.5-9.fc43.noarch" is already installed.
Package "python3-array-api-compat-1.15.0-1.fc43.noarch" is already installed.
Package "python3-array-api-extra-0.11.5-1.fc43.noarch" is already installed.
Package "python3-array-api-strict-2.0.1-7.fc43.noarch" is already installed.
Package "python3-h5py-3.13.0-6.fc43.x86_64" is already installed.
Package "python3-huggingface-hub-1.24.0-1.fc43.noarch" is already installed.
Package "python3-ipax-0.13.0-1.fc43.noarch" is already installed.
Package "python3-jinja2-3.1.6-6.fc43.noarch" is already installed.
Package "python3-matplotlib-3.10.9-1.fc43.x86_64" is already installed.
Package "python3-numpy-1:2.3.5-1.fc43.x86_64" is already installed.
Package "python3-numpydantic-2.0.0-1.fc43.noarch" is already installed.
Package "python3-packaging-25.0-7.fc43.noarch" is already installed.
Package "python3-pint-0.25.2-1.fc43.noarch" is already installed.
Package "python3-pip-25.1.1-19.fc43.noarch" is already installed.
Package "python3-pydicom-3.0.2-1.fc43.noarch" is already installed.
Package "python3-pymatreader-1.3.1-1.fc43.noarch" is already installed.
Package "python3-regex-2026.9.10-1.fc43.x86_64" is already installed.
Package "python3-safetensors-0.7.0-1.fc43.x86_64" is already installed.
Package "python3-scipy-1.16.2-1.fc43.x86_64" is already installed.
Package "python3-setuptools-78.1.1-15.fc43.noarch" is already installed.
Package "python3-setuptools_scm-8.3.1-8.fc43.noarch" is already installed.
Package "python3-simpleitk-2.5.5-12.fc43.x86_64" is already installed.
Package "python3-tqdm-4.67.2-2.fc43.noarch" is already installed.

Nothing to do.
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.crX1aq
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ cd pyradplan-0.8.0
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1   '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir --output /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-dep-overrides
Handling setuptools>=64 from build-system.requires
Requirement satisfied: setuptools>=64
   (installed: setuptools 78.1.1)
Handling setuptools-scm>=8 from build-system.requires
Requirement satisfied: setuptools-scm>=8
   (installed: setuptools-scm 8.3.1)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running egg_info
writing pyRadPlan.egg-info/PKG-INFO
writing dependency_links to pyRadPlan.egg-info/dependency_links.txt
writing entry points to pyRadPlan.egg-info/entry_points.txt
writing requirements to pyRadPlan.egg-info/requires.txt
writing top-level names to pyRadPlan.egg-info/top_level.txt
reading manifest file 'pyRadPlan.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'pyRadPlan.egg-info/SOURCES.txt'
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running dist_info
writing pyRadPlan.egg-info/PKG-INFO
writing dependency_links to pyRadPlan.egg-info/dependency_links.txt
writing entry points to pyRadPlan.egg-info/entry_points.txt
writing requirements to pyRadPlan.egg-info/requires.txt
writing top-level names to pyRadPlan.egg-info/top_level.txt
reading manifest file 'pyRadPlan.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'pyRadPlan.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/pyradplan-0.8.0.dist-info'
Handling scipy>=1.13.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: scipy>=1.13.0
   (installed: scipy 1.16.2)
Handling numpy>=2.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: numpy>=2.0
   (installed: numpy 2.3.5)
Handling array_api_compat from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: array_api_compat
   (installed: array_api_compat 1.15.0)
Handling array_api_strict from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: array_api_strict
   (installed: array_api_strict 2.0.1)
Handling array_api_extra from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: array_api_extra
   (installed: array_api_extra 0.11.5)
Handling ipax>=0.13.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: ipax>=0.13.0
   (installed: ipax 0.13.0)
Handling pymatreader>=1.0.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pymatreader>=1.0.0
   (installed: pymatreader 1.3.1)
Handling h5py>=3.11.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: h5py>=3.11.0
   (installed: h5py 3.13.0)
Handling regex>=2024.5.15 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: regex>=2024.5.15
   (installed: regex 2026.9.10)
Handling pydicom>=2.4.4 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pydicom>=2.4.4
   (installed: pydicom 3.0.2)
Handling matplotlib>=3.9.2 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: matplotlib>=3.9.2
   (installed: matplotlib 3.10.9)
Handling pydantic<2.20,>=2.10 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pydantic<2.20,>=2.10
   (installed: pydantic 2.12.5)
Handling pydantic-settings<3.0,>=2.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pydantic-settings<3.0,>=2.0
   (installed: pydantic-settings 2.15.0)
Handling numpydantic>=1.6.9 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: numpydantic>=1.6.9
   (installed: numpydantic 2.0.0)
Handling SimpleITK>=2.4.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement not satisfied: SimpleITK>=2.4.0
Handling tqdm>=4.0.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: tqdm>=4.0.0
   (installed: tqdm 4.67.2)
Handling pint>=0.24.0 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: pint>=0.24.0
   (installed: pint 0.25.2)
Handling jinja2 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: jinja2
   (installed: jinja2 3.1.6)
Handling huggingface_hub>=0.23 from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: huggingface_hub>=0.23
   (installed: huggingface_hub 1.24.0)
Handling safetensors from hook generated metadata: Requires-Dist (pyradplan)
Requirement satisfied: safetensors
   (installed: safetensors 0.7.0)
Handling matlabengine; extra == "matlab" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: matlabengine; extra == "matlab"
Handling oct2py; extra == "octave" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: oct2py; extra == "octave"
Handling pyside6-essentials<6.12,>=6.4; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pyside6-essentials<6.12,>=6.4; extra == "gui"
Handling pyqtgraph>=0.12.0; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pyqtgraph>=0.12.0; extra == "gui"
Handling contourpy>=1.0.1; extra == "gui" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: contourpy>=1.0.1; extra == "gui"
Handling numba>=0.61.0; extra == "numba" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: numba>=0.61.0; extra == "numba"
Handling torch>=2.0; extra == "torch" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: torch>=2.0; extra == "torch"
Handling cupy>=13.0; extra == "cupy" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: cupy>=13.0; extra == "cupy"
Handling jax>=0.4.35; extra == "jax" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: jax>=0.4.35; extra == "jax"
Handling line_profiler>=4.0; extra == "profiling" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: line_profiler>=4.0; extra == "profiling"
Handling pytest; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest; extra == "dev"
Handling coverage; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: coverage; extra == "dev"
Handling pytest-cov; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest-cov; extra == "dev"
Handling pytest-benchmark; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pytest-benchmark; extra == "dev"
Handling pre-commit; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pre-commit; extra == "dev"
Handling ruff<0.18; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: ruff<0.18; extra == "dev"
Handling sphinx; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx; extra == "dev"
Handling numpydoc; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: numpydoc; extra == "dev"
Handling pydata-sphinx-theme; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: pydata-sphinx-theme; extra == "dev"
Handling sphinx-design; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx-design; extra == "dev"
Handling autodoc_pydantic; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: autodoc_pydantic; extra == "dev"
Handling sphinx-autodoc-typehints; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: sphinx-autodoc-typehints; extra == "dev"
Handling myst-nb; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: myst-nb; extra == "dev"
Handling jupytext; extra == "dev" from hook generated metadata: Requires-Dist (pyradplan)
Ignoring alien requirement: jupytext; extra == "dev"
+ cat /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-buildrequires
+ rm -rfv pyradplan-0.8.0.dist-info/
removed 'pyradplan-0.8.0.dist-info/licenses/LICENSE'
removed directory 'pyradplan-0.8.0.dist-info/licenses'
removed 'pyradplan-0.8.0.dist-info/METADATA'
removed 'pyradplan-0.8.0.dist-info/entry_points.txt'
removed 'pyradplan-0.8.0.dist-info/top_level.txt'
removed directory 'pyradplan-0.8.0.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%build): /bin/sh -e /var/tmp/rpm-tmp.nsiq18
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd pyradplan-0.8.0
+ mkdir -p /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_wheel.py /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir
Processing /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0
  Preparing metadata (pyproject.toml): started
  Running command Preparing metadata (pyproject.toml)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    dist._finalize_license_expression()
  /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    self._finalize_license_expression()
  running dist_info
  creating /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info
  writing /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/PKG-INFO
  writing dependency_links to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/dependency_links.txt
  writing entry points to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/entry_points.txt
  writing requirements to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/requires.txt
  writing top-level names to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/top_level.txt
  writing manifest file '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/SOURCES.txt'
  reading manifest file '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/SOURCES.txt'
  adding license file 'LICENSE'
  writing manifest file '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyRadPlan.egg-info/SOURCES.txt'
  creating '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-6atd1u7o/pyradplan-0.8.0.dist-info'
  Preparing metadata (pyproject.toml): finished with status 'done'
Building wheels for collected packages: pyRadPlan
  Building wheel for pyRadPlan (pyproject.toml): started
  Running command Building wheel for pyRadPlan (pyproject.toml)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    dist._finalize_license_expression()
  /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    self._finalize_license_expression()
  running bdist_wheel
  running build
  running build_py
  creating build/lib/pyRadPlan
  copying pyRadPlan/_settings.py -> build/lib/pyRadPlan
  copying pyRadPlan/_lazy.py -> build/lib/pyRadPlan
  copying pyRadPlan/__init__.py -> build/lib/pyRadPlan
  creating build/lib/pyRadPlan/visualization
  copying pyRadPlan/visualization/_plot_slice.py -> build/lib/pyRadPlan/visualization
  copying pyRadPlan/visualization/_plot_multiple_slices.py -> build/lib/pyRadPlan/visualization
  copying pyRadPlan/visualization/__init__.py -> build/lib/pyRadPlan/visualization
  creating build/lib/pyRadPlan/util
  copying pyRadPlan/util/openmp.py -> build/lib/pyRadPlan/util
  copying pyRadPlan/util/logging_utils.py -> build/lib/pyRadPlan/util
  copying pyRadPlan/util/keyboard_listener.py -> build/lib/pyRadPlan/util
  copying pyRadPlan/util/helpers.py -> build/lib/pyRadPlan/util
  copying pyRadPlan/util/_jupyter.py -> build/lib/pyRadPlan/util
  copying pyRadPlan/util/__init__.py -> build/lib/pyRadPlan/util
  creating build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_steeringinformation.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_ray.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_rangeshifter.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_presets.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_generate_stf.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_exceptions.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_beamlet.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/_beam.py -> build/lib/pyRadPlan/stf
  copying pyRadPlan/stf/__init__.py -> build/lib/pyRadPlan/stf
  creating build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_worst_case.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_set.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_random.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_nominal.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_legacy.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_importance.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_gridded.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_explicit.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_distributions.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_dimensions.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/_base.py -> build/lib/pyRadPlan/scenarios
  copying pyRadPlan/scenarios/__init__.py -> build/lib/pyRadPlan/scenarios
  creating build/lib/pyRadPlan/raytracer
  copying pyRadPlan/raytracer/_siddon.py -> build/lib/pyRadPlan/raytracer
  copying pyRadPlan/raytracer/_kernels_numba.py -> build/lib/pyRadPlan/raytracer
  copying pyRadPlan/raytracer/_kernels.py -> build/lib/pyRadPlan/raytracer
  copying pyRadPlan/raytracer/_base.py -> build/lib/pyRadPlan/raytracer
  copying pyRadPlan/raytracer/__init__.py -> build/lib/pyRadPlan/raytracer
  creating build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_sqrt_beta_dose.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_resolver.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_rbe_x_dose.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_let_x_dose.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_let.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_effect.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_dose.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_base.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/_alpha_dose.py -> build/lib/pyRadPlan/quantities
  copying pyRadPlan/quantities/__init__.py -> build/lib/pyRadPlan/quantities
  creating build/lib/pyRadPlan/plan
  copying pyRadPlan/plan/_plans.py -> build/lib/pyRadPlan/plan
  copying pyRadPlan/plan/__init__.py -> build/lib/pyRadPlan/plan
  creating build/lib/pyRadPlan/optimization
  copying pyRadPlan/optimization/_fluence_optimization.py -> build/lib/pyRadPlan/optimization
  copying pyRadPlan/optimization/__init__.py -> build/lib/pyRadPlan/optimization
  creating build/lib/pyRadPlan/matRad
  copying pyRadPlan/matRad/_octave.py -> build/lib/pyRadPlan/matRad
  copying pyRadPlan/matRad/_matlab.py -> build/lib/pyRadPlan/matRad
  copying pyRadPlan/matRad/_engine.py -> build/lib/pyRadPlan/matRad
  copying pyRadPlan/matRad/__init__.py -> build/lib/pyRadPlan/matRad
  creating build/lib/pyRadPlan/machines
  copying pyRadPlan/machines/_validate.py -> build/lib/pyRadPlan/machines
  copying pyRadPlan/machines/_load.py -> build/lib/pyRadPlan/machines
  copying pyRadPlan/machines/__init__.py -> build/lib/pyRadPlan/machines
  creating build/lib/pyRadPlan/io
  copying pyRadPlan/io/_load_save.py -> build/lib/pyRadPlan/io
  copying pyRadPlan/io/_helpers.py -> build/lib/pyRadPlan/io
  copying pyRadPlan/io/_factory.py -> build/lib/pyRadPlan/io
  copying pyRadPlan/io/__init__.py -> build/lib/pyRadPlan/io
  creating build/lib/pyRadPlan/gui
  copying pyRadPlan/gui/workspace.py -> build/lib/pyRadPlan/gui
  copying pyRadPlan/gui/apps.py -> build/lib/pyRadPlan/gui
  copying pyRadPlan/gui/__init__.py -> build/lib/pyRadPlan/gui
  creating build/lib/pyRadPlan/geometry
  copying pyRadPlan/geometry/lps.py -> build/lib/pyRadPlan/geometry
  copying pyRadPlan/geometry/__init__.py -> build/lib/pyRadPlan/geometry
  creating build/lib/pyRadPlan/dose
  copying pyRadPlan/dose/_calc_dose.py -> build/lib/pyRadPlan/dose
  copying pyRadPlan/dose/__init__.py -> build/lib/pyRadPlan/dose
  creating build/lib/pyRadPlan/dij
  copying pyRadPlan/dij/_dij.py -> build/lib/pyRadPlan/dij
  copying pyRadPlan/dij/_compose_beam_dijs.py -> build/lib/pyRadPlan/dij
  copying pyRadPlan/dij/__init__.py -> build/lib/pyRadPlan/dij
  creating build/lib/pyRadPlan/ct
  copying pyRadPlan/ct/_hlut.py -> build/lib/pyRadPlan/ct
  copying pyRadPlan/ct/_ct.py -> build/lib/pyRadPlan/ct
  copying pyRadPlan/ct/__init__.py -> build/lib/pyRadPlan/ct
  creating build/lib/pyRadPlan/cst
  copying pyRadPlan/cst/_voi.py -> build/lib/pyRadPlan/cst
  copying pyRadPlan/cst/_cst.py -> build/lib/pyRadPlan/cst
  copying pyRadPlan/cst/__init__.py -> build/lib/pyRadPlan/cst
  creating build/lib/pyRadPlan/core
  copying pyRadPlan/core/resample.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/np2sitk.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/datamodel.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/_units.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/_progress.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/_paths.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/_grids.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/_exceptions.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/_configurable.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/_base_data.py -> build/lib/pyRadPlan/core
  copying pyRadPlan/core/__init__.py -> build/lib/pyRadPlan/core
  creating build/lib/pyRadPlan/biology
  copying pyRadPlan/biology/__init__.py -> build/lib/pyRadPlan/biology
  creating build/lib/pyRadPlan/analysis
  copying pyRadPlan/analysis/_units.py -> build/lib/pyRadPlan/analysis
  copying pyRadPlan/analysis/_qi.py -> build/lib/pyRadPlan/analysis
  copying pyRadPlan/analysis/_dvh_plot.py -> build/lib/pyRadPlan/analysis
  copying pyRadPlan/analysis/_dvh.py -> build/lib/pyRadPlan/analysis
  copying pyRadPlan/analysis/__init__.py -> build/lib/pyRadPlan/analysis
  creating build/lib/pyRadPlan/ai
  copying pyRadPlan/ai/__init__.py -> build/lib/pyRadPlan/ai
  creating build/lib/pyRadPlan/stf/generators
  copying pyRadPlan/stf/generators/_vhee.py -> build/lib/pyRadPlan/stf/generators
  copying pyRadPlan/stf/generators/_photons.py -> build/lib/pyRadPlan/stf/generators
  copying pyRadPlan/stf/generators/_ions.py -> build/lib/pyRadPlan/stf/generators
  copying pyRadPlan/stf/generators/_factory.py -> build/lib/pyRadPlan/stf/generators
  copying pyRadPlan/stf/generators/_externalbeam.py -> build/lib/pyRadPlan/stf/generators
  copying pyRadPlan/stf/generators/_base.py -> build/lib/pyRadPlan/stf/generators
  copying pyRadPlan/stf/generators/__init__.py -> build/lib/pyRadPlan/stf/generators
  creating build/lib/pyRadPlan/optimization/solvers
  copying pyRadPlan/optimization/solvers/_scipy_solver.py -> build/lib/pyRadPlan/optimization/solvers
  copying pyRadPlan/optimization/solvers/_ipopt.py -> build/lib/pyRadPlan/optimization/solvers
  copying pyRadPlan/optimization/solvers/_ipax_solver.py -> build/lib/pyRadPlan/optimization/solvers
  copying pyRadPlan/optimization/solvers/_factory.py -> build/lib/pyRadPlan/optimization/solvers
  copying pyRadPlan/optimization/solvers/_base_solvers.py -> build/lib/pyRadPlan/optimization/solvers
  copying pyRadPlan/optimization/solvers/__init__.py -> build/lib/pyRadPlan/optimization/solvers
  creating build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_voxel_worst_case.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_utils.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_terms.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_smooth_max.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_objective_worst_case.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_none.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_factory.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_expected_value.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_expected_quantity.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_composite_worst_case.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/_base.py -> build/lib/pyRadPlan/optimization/robustness
  copying pyRadPlan/optimization/robustness/__init__.py -> build/lib/pyRadPlan/optimization/robustness
  creating build/lib/pyRadPlan/optimization/problems
  copying pyRadPlan/optimization/problems/_simple_least_squares.py -> build/lib/pyRadPlan/optimization/problems
  copying pyRadPlan/optimization/problems/_optiprob.py -> build/lib/pyRadPlan/optimization/problems
  copying pyRadPlan/optimization/problems/_nonlin_fluence.py -> build/lib/pyRadPlan/optimization/problems
  copying pyRadPlan/optimization/problems/_factory.py -> build/lib/pyRadPlan/optimization/problems
  copying pyRadPlan/optimization/problems/__init__.py -> build/lib/pyRadPlan/optimization/problems
  creating build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_variance.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_std.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_squared_under.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_squared_over.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_squared_mimicking.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_squared_dev.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_objective.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_min_dvh.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_mean.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_max_dvh.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_factory.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/_eud.py -> build/lib/pyRadPlan/optimization/objectives
  copying pyRadPlan/optimization/objectives/__init__.py -> build/lib/pyRadPlan/optimization/objectives
  creating build/lib/pyRadPlan/machines/photons
  copying pyRadPlan/machines/photons/_svd_kernel.py -> build/lib/pyRadPlan/machines/photons
  copying pyRadPlan/machines/photons/_linac.py -> build/lib/pyRadPlan/machines/photons
  copying pyRadPlan/machines/photons/_calculate_machine_scale.py -> build/lib/pyRadPlan/machines/photons
  copying pyRadPlan/machines/photons/_base.py -> build/lib/pyRadPlan/machines/photons
  copying pyRadPlan/machines/photons/__init__.py -> build/lib/pyRadPlan/machines/photons
  creating build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_vhee.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_ions.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_beam_fragment_spectrum.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_beam_focus.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_beam_energy.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_beam_emittance.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_beam_cutoff.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/_base.py -> build/lib/pyRadPlan/machines/particles
  copying pyRadPlan/machines/particles/__init__.py -> build/lib/pyRadPlan/machines/particles
  creating build/lib/pyRadPlan/machines/base
  copying pyRadPlan/machines/base/_internal_beam.py -> build/lib/pyRadPlan/machines/base
  copying pyRadPlan/machines/base/_factory.py -> build/lib/pyRadPlan/machines/base
  copying pyRadPlan/machines/base/_external_beam.py -> build/lib/pyRadPlan/machines/base
  copying pyRadPlan/machines/base/_beam_modifier.py -> build/lib/pyRadPlan/machines/base
  copying pyRadPlan/machines/base/_base.py -> build/lib/pyRadPlan/machines/base
  copying pyRadPlan/machines/base/__init__.py -> build/lib/pyRadPlan/machines/base
  creating build/lib/pyRadPlan/machines/particles/kernel
  copying pyRadPlan/machines/particles/kernel/_vhee.py -> build/lib/pyRadPlan/machines/particles/kernel
  copying pyRadPlan/machines/particles/kernel/_ions.py -> build/lib/pyRadPlan/machines/particles/kernel
  copying pyRadPlan/machines/particles/kernel/_base.py -> build/lib/pyRadPlan/machines/particles/kernel
  copying pyRadPlan/machines/particles/kernel/__init__.py -> build/lib/pyRadPlan/machines/particles/kernel
  creating build/lib/pyRadPlan/io/sitk_based
  copying pyRadPlan/io/sitk_based/_binary_import.py -> build/lib/pyRadPlan/io/sitk_based
  copying pyRadPlan/io/sitk_based/__init__.py -> build/lib/pyRadPlan/io/sitk_based
  creating build/lib/pyRadPlan/io/pickle
  copying pyRadPlan/io/pickle/_importer.py -> build/lib/pyRadPlan/io/pickle
  copying pyRadPlan/io/pickle/_handler.py -> build/lib/pyRadPlan/io/pickle
  copying pyRadPlan/io/pickle/_exporter.py -> build/lib/pyRadPlan/io/pickle
  copying pyRadPlan/io/pickle/__init__.py -> build/lib/pyRadPlan/io/pickle
  creating build/lib/pyRadPlan/io/npz
  copying pyRadPlan/io/npz/_serialize.py -> build/lib/pyRadPlan/io/npz
  copying pyRadPlan/io/npz/_importer.py -> build/lib/pyRadPlan/io/npz
  copying pyRadPlan/io/npz/_handler.py -> build/lib/pyRadPlan/io/npz
  copying pyRadPlan/io/npz/_exporter.py -> build/lib/pyRadPlan/io/npz
  copying pyRadPlan/io/npz/__init__.py -> build/lib/pyRadPlan/io/npz
  creating build/lib/pyRadPlan/io/matlab
  copying pyRadPlan/io/matlab/_matfile.py -> build/lib/pyRadPlan/io/matlab
  copying pyRadPlan/io/matlab/_importer.py -> build/lib/pyRadPlan/io/matlab
  copying pyRadPlan/io/matlab/_handler.py -> build/lib/pyRadPlan/io/matlab
  copying pyRadPlan/io/matlab/_exporter.py -> build/lib/pyRadPlan/io/matlab
  copying pyRadPlan/io/matlab/__init__.py -> build/lib/pyRadPlan/io/matlab
  creating build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_importer.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_import_dose.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_import_ct.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_import_cst.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_helpers.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_handler.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_exporter.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_export_seg.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_export_dose.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_export_ct.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_export_cst.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/_export_common.py -> build/lib/pyRadPlan/io/dicom
  copying pyRadPlan/io/dicom/__init__.py -> build/lib/pyRadPlan/io/dicom
  creating build/lib/pyRadPlan/io/base
  copying pyRadPlan/io/base/_importer.py -> build/lib/pyRadPlan/io/base
  copying pyRadPlan/io/base/_exporter.py -> build/lib/pyRadPlan/io/base
  copying pyRadPlan/io/base/__init__.py -> build/lib/pyRadPlan/io/base
  creating build/lib/pyRadPlan/io/sitk_based/nrrd
  copying pyRadPlan/io/sitk_based/nrrd/__init__.py -> build/lib/pyRadPlan/io/sitk_based/nrrd
  creating build/lib/pyRadPlan/io/sitk_based/nifti
  copying pyRadPlan/io/sitk_based/nifti/__init__.py -> build/lib/pyRadPlan/io/sitk_based/nifti
  creating build/lib/pyRadPlan/io/sitk_based/meta_image
  copying pyRadPlan/io/sitk_based/meta_image/__init__.py -> build/lib/pyRadPlan/io/sitk_based/meta_image
  creating build/lib/pyRadPlan/io/sitk_based/base
  copying pyRadPlan/io/sitk_based/base/_serialize.py -> build/lib/pyRadPlan/io/sitk_based/base
  copying pyRadPlan/io/sitk_based/base/_importer.py -> build/lib/pyRadPlan/io/sitk_based/base
  copying pyRadPlan/io/sitk_based/base/_exporter.py -> build/lib/pyRadPlan/io/sitk_based/base
  copying pyRadPlan/io/sitk_based/base/__init__.py -> build/lib/pyRadPlan/io/sitk_based/base
  creating build/lib/pyRadPlan/gui/windows
  copying pyRadPlan/gui/windows/_splash_win.py -> build/lib/pyRadPlan/gui/windows
  copying pyRadPlan/gui/windows/_result_win.py -> build/lib/pyRadPlan/gui/windows
  copying pyRadPlan/gui/windows/_main_win.py -> build/lib/pyRadPlan/gui/windows
  copying pyRadPlan/gui/windows/_analysis_win.py -> build/lib/pyRadPlan/gui/windows
  creating build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_ui_mode.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_tissue_params.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_result_widget.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_quantity_selector.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_plot_theme.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_logo_widget.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_log_console.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_info_widget.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_display_names.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_config_form.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_base.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/_analysis_widget.py -> build/lib/pyRadPlan/gui/widgets
  copying pyRadPlan/gui/widgets/__init__.py -> build/lib/pyRadPlan/gui/widgets
  creating build/lib/pyRadPlan/gui/menus
  copying pyRadPlan/gui/menus/_view.py -> build/lib/pyRadPlan/gui/menus
  copying pyRadPlan/gui/menus/_settings.py -> build/lib/pyRadPlan/gui/menus
  copying pyRadPlan/gui/menus/_help.py -> build/lib/pyRadPlan/gui/menus
  copying pyRadPlan/gui/menus/_file.py -> build/lib/pyRadPlan/gui/menus
  copying pyRadPlan/gui/menus/_edit.py -> build/lib/pyRadPlan/gui/menus
  copying pyRadPlan/gui/menus/_base_data.py -> build/lib/pyRadPlan/gui/menus
  copying pyRadPlan/gui/menus/__init__.py -> build/lib/pyRadPlan/gui/menus
  creating build/lib/pyRadPlan/gui/assets
  copying pyRadPlan/gui/assets/__init__.py -> build/lib/pyRadPlan/gui/assets
  creating build/lib/pyRadPlan/gui/widgets/workflow
  copying pyRadPlan/gui/widgets/workflow/_workflow_widget.py -> build/lib/pyRadPlan/gui/widgets/workflow
  copying pyRadPlan/gui/widgets/workflow/_image_import_dialog.py -> build/lib/pyRadPlan/gui/widgets/workflow
  copying pyRadPlan/gui/widgets/workflow/_dicom_import_dialog.py -> build/lib/pyRadPlan/gui/widgets/workflow
  copying pyRadPlan/gui/widgets/workflow/_binary_import_dialog.py -> build/lib/pyRadPlan/gui/widgets/workflow
  copying pyRadPlan/gui/widgets/workflow/__init__.py -> build/lib/pyRadPlan/gui/widgets/workflow
  creating build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/vois_widget.py -> build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/visualization_widget.py -> build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/viewer_options_widget.py -> build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/quantity_widget.py -> build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/_save_result_dialog.py -> build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/_orientation_marker.py -> build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/_labels.py -> build/lib/pyRadPlan/gui/widgets/result
  copying pyRadPlan/gui/widgets/result/__init__.py -> build/lib/pyRadPlan/gui/widgets/result
  creating build/lib/pyRadPlan/gui/widgets/plan
  copying pyRadPlan/gui/widgets/plan/_tissue_dialog.py -> build/lib/pyRadPlan/gui/widgets/plan
  copying pyRadPlan/gui/widgets/plan/_plan_widget.py -> build/lib/pyRadPlan/gui/widgets/plan
  copying pyRadPlan/gui/widgets/plan/_optimization_dialog.py -> build/lib/pyRadPlan/gui/widgets/plan
  copying pyRadPlan/gui/widgets/plan/__init__.py -> build/lib/pyRadPlan/gui/widgets/plan
  creating build/lib/pyRadPlan/gui/widgets/optimization
  copying pyRadPlan/gui/widgets/optimization/_status_window.py -> build/lib/pyRadPlan/gui/widgets/optimization
  copying pyRadPlan/gui/widgets/optimization/_optimization_widget.py -> build/lib/pyRadPlan/gui/widgets/optimization
  copying pyRadPlan/gui/widgets/optimization/__init__.py -> build/lib/pyRadPlan/gui/widgets/optimization
  creating build/lib/pyRadPlan/gui/widgets/analysis
  copying pyRadPlan/gui/widgets/analysis/_qi_point_dialog.py -> build/lib/pyRadPlan/gui/widgets/analysis
  copying pyRadPlan/gui/widgets/analysis/_qi.py -> build/lib/pyRadPlan/gui/widgets/analysis
  copying pyRadPlan/gui/widgets/analysis/_gamma.py -> build/lib/pyRadPlan/gui/widgets/analysis
  copying pyRadPlan/gui/widgets/analysis/_dvh.py -> build/lib/pyRadPlan/gui/widgets/analysis
  creating build/lib/pyRadPlan/gui/widgets/ai
  copying pyRadPlan/gui/widgets/ai/_ai_task_dialog.py -> build/lib/pyRadPlan/gui/widgets/ai
  copying pyRadPlan/gui/widgets/ai/__init__.py -> build/lib/pyRadPlan/gui/widgets/ai
  creating build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_topasmc.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_svdpb.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_sparse_output.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_particle_cutoff.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_hongpb.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_fredmc.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_factory.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_base_pencilbeam_particle.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_base_pencilbeam.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_base_montecarlo.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/_base.py -> build/lib/pyRadPlan/dose/engines
  copying pyRadPlan/dose/engines/__init__.py -> build/lib/pyRadPlan/dose/engines
  creating build/lib/pyRadPlan/dose/engines/kernels
  copying pyRadPlan/dose/engines/kernels/_calc_geo_dists_kernel.py -> build/lib/pyRadPlan/dose/engines/kernels
  copying pyRadPlan/dose/engines/kernels/__init__.py -> build/lib/pyRadPlan/dose/engines/kernels
  creating build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/typing.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/jittable.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/helpers.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/compat.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/_reductions.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/_interpolation1d.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/_interpolation.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/_image_interpolation.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/_backends.py -> build/lib/pyRadPlan/core/xp_utils
  copying pyRadPlan/core/xp_utils/__init__.py -> build/lib/pyRadPlan/core/xp_utils
  creating build/lib/pyRadPlan/biology/tissues
  copying pyRadPlan/biology/tissues/_sources.py -> build/lib/pyRadPlan/biology/tissues
  copying pyRadPlan/biology/tissues/_reference.py -> build/lib/pyRadPlan/biology/tissues
  copying pyRadPlan/biology/tissues/_provenance.py -> build/lib/pyRadPlan/biology/tissues
  copying pyRadPlan/biology/tissues/_presets.py -> build/lib/pyRadPlan/biology/tissues
  copying pyRadPlan/biology/tissues/_conversion.py -> build/lib/pyRadPlan/biology/tissues
  copying pyRadPlan/biology/tissues/_base.py -> build/lib/pyRadPlan/biology/tissues
  copying pyRadPlan/biology/tissues/__init__.py -> build/lib/pyRadPlan/biology/tissues
  creating build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_tissue_lookup.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_resolution.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_options.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_kernel_sets.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_influence.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_factory.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_evaluator.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_engine.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/_base.py -> build/lib/pyRadPlan/biology/models
  copying pyRadPlan/biology/models/__init__.py -> build/lib/pyRadPlan/biology/models
  creating build/lib/pyRadPlan/biology/models/_models
  copying pyRadPlan/biology/models/_models/zstar_based_lq_model.py -> build/lib/pyRadPlan/biology/models/_models
  copying pyRadPlan/biology/models/_models/tabulated_rbe_models.py -> build/lib/pyRadPlan/biology/models/_models
  copying pyRadPlan/biology/models/_models/lq_models.py -> build/lib/pyRadPlan/biology/models/_models
  copying pyRadPlan/biology/models/_models/let_based_lq_models.py -> build/lib/pyRadPlan/biology/models/_models
  copying pyRadPlan/biology/models/_models/kernel_based_lq_model.py -> build/lib/pyRadPlan/biology/models/_models
  copying pyRadPlan/biology/models/_models/const_rbe.py -> build/lib/pyRadPlan/biology/models/_models
  creating build/lib/pyRadPlan/ai/modelhub
  copying pyRadPlan/ai/modelhub/_settings.py -> build/lib/pyRadPlan/ai/modelhub
  copying pyRadPlan/ai/modelhub/_resolve.py -> build/lib/pyRadPlan/ai/modelhub
  copying pyRadPlan/ai/modelhub/_registry.py -> build/lib/pyRadPlan/ai/modelhub
  copying pyRadPlan/ai/modelhub/_preprocessor.py -> build/lib/pyRadPlan/ai/modelhub
  copying pyRadPlan/ai/modelhub/_load_model.py -> build/lib/pyRadPlan/ai/modelhub
  copying pyRadPlan/ai/modelhub/__init__.py -> build/lib/pyRadPlan/ai/modelhub
  creating build/lib/pyRadPlan/ai/agents
  copying pyRadPlan/ai/agents/_usage.py -> build/lib/pyRadPlan/ai/agents
  copying pyRadPlan/ai/agents/_settings.py -> build/lib/pyRadPlan/ai/agents
  copying pyRadPlan/ai/agents/_plan_agents.py -> build/lib/pyRadPlan/ai/agents
  copying pyRadPlan/ai/agents/_models.py -> build/lib/pyRadPlan/ai/agents
  copying pyRadPlan/ai/agents/_cst_agents.py -> build/lib/pyRadPlan/ai/agents
  copying pyRadPlan/ai/agents/__init__.py -> build/lib/pyRadPlan/ai/agents
  running egg_info
  writing pyRadPlan.egg-info/PKG-INFO
  writing dependency_links to pyRadPlan.egg-info/dependency_links.txt
  writing entry points to pyRadPlan.egg-info/entry_points.txt
  writing requirements to pyRadPlan.egg-info/requires.txt
  writing top-level names to pyRadPlan.egg-info/top_level.txt
  reading manifest file 'pyRadPlan.egg-info/SOURCES.txt'
  adding license file 'LICENSE'
  writing manifest file 'pyRadPlan.egg-info/SOURCES.txt'
  creating build/lib/pyRadPlan/data/topas/input/template
  copying pyRadPlan/data/topas/input/template/pyRadPlan_run_template.txt -> build/lib/pyRadPlan/data/topas/input/template
  copying pyRadPlan/data/topas/input/template/patient_template.txt -> build/lib/pyRadPlan/data/topas/input/template
  copying pyRadPlan/data/topas/input/template/beamSetup_template.txt -> build/lib/pyRadPlan/data/topas/input/template
  creating build/lib/pyRadPlan/data/topas/input/scorer
  copying pyRadPlan/data/topas/input/scorer/TOPAS_subscorer_LET.txt.in -> build/lib/pyRadPlan/data/topas/input/scorer
  copying pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToWater.txt.in -> build/lib/pyRadPlan/data/topas/input/scorer
  copying pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToMedium.txt.in -> build/lib/pyRadPlan/data/topas/input/scorer
  creating build/lib/pyRadPlan/data/topas/input/material_converter
  copying pyRadPlan/data/topas/input/material_converter/TOPAS_schneider_converter.txt.in -> build/lib/pyRadPlan/data/topas/input/material_converter
  creating build/lib/pyRadPlan/data/topas/input/beam_setup
  copying pyRadPlan/data/topas/input/beam_setup/TOPAS_beam_setup_biGaussian.txt.in -> build/lib/pyRadPlan/data/topas/input/beam_setup
  creating build/lib/pyRadPlan/data/rbe
  copying pyRadPlan/data/rbe/RBEtable_LEMI_Scholz06_AX01_BX005.mat -> build/lib/pyRadPlan/data/rbe
  creating build/lib/pyRadPlan/data/physics/stopping
  copying pyRadPlan/data/physics/stopping/SPtable.mat -> build/lib/pyRadPlan/data/physics/stopping
  creating build/lib/pyRadPlan/data/phantoms
  copying pyRadPlan/data/phantoms/TG119.mat -> build/lib/pyRadPlan/data/phantoms
  creating build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/protons_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/photons_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/oxygen_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/helium_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/carbon_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/VHEE_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/VHEE_Focused.mat -> build/lib/pyRadPlan/data/machines
  creating build/lib/pyRadPlan/data/ai_models
  copying pyRadPlan/data/ai_models/model_config.template.json -> build/lib/pyRadPlan/data/ai_models
  copying pyRadPlan/gui/assets/__init__.py -> build/lib/pyRadPlan/gui/assets
  creating build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/gui/assets/logos/pyradplan_logo_skull_square.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/gui/assets/logos/pyradplan_logo_full_landscape.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/gui/assets/logos/dkfz_logo_white.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/gui/assets/logos/dkfz_logo_blue.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/matRad/.gitkeep -> build/lib/pyRadPlan/matRad
  copying pyRadPlan/io/.gitkeep -> build/lib/pyRadPlan/io
  creating build/lib/pyRadPlan/stf/hlutLibrary
  copying pyRadPlan/stf/hlutLibrary/matRad_default.hlut -> build/lib/pyRadPlan/stf/hlutLibrary
  copying pyRadPlan/gui/assets/logos/dkfz_logo_blue.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/gui/assets/logos/dkfz_logo_white.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/gui/assets/logos/pyradplan_logo_full_landscape.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/gui/assets/logos/pyradplan_logo_skull_square.svg -> build/lib/pyRadPlan/gui/assets/logos
  copying pyRadPlan/data/rbe/RBEtable_LEMI_Scholz06_AX01_BX005.mat -> build/lib/pyRadPlan/data/rbe
  copying pyRadPlan/data/phantoms/TG119.mat -> build/lib/pyRadPlan/data/phantoms
  copying pyRadPlan/data/machines/VHEE_Focused.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/VHEE_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/carbon_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/helium_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/oxygen_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/photons_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/machines/protons_Generic.mat -> build/lib/pyRadPlan/data/machines
  copying pyRadPlan/data/ai_models/model_config.template.json -> build/lib/pyRadPlan/data/ai_models
  copying pyRadPlan/data/topas/input/template/beamSetup_template.txt -> build/lib/pyRadPlan/data/topas/input/template
  copying pyRadPlan/data/topas/input/template/patient_template.txt -> build/lib/pyRadPlan/data/topas/input/template
  copying pyRadPlan/data/topas/input/template/pyRadPlan_run_template.txt -> build/lib/pyRadPlan/data/topas/input/template
  copying pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToMedium.txt.in -> build/lib/pyRadPlan/data/topas/input/scorer
  copying pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToWater.txt.in -> build/lib/pyRadPlan/data/topas/input/scorer
  copying pyRadPlan/data/topas/input/scorer/TOPAS_subscorer_LET.txt.in -> build/lib/pyRadPlan/data/topas/input/scorer
  copying pyRadPlan/data/topas/input/material_converter/TOPAS_schneider_converter.txt.in -> build/lib/pyRadPlan/data/topas/input/material_converter
  copying pyRadPlan/data/topas/input/beam_setup/TOPAS_beam_setup_biGaussian.txt.in -> build/lib/pyRadPlan/data/topas/input/beam_setup
  copying pyRadPlan/data/physics/stopping/SPtable.mat -> build/lib/pyRadPlan/data/physics/stopping
  installing to build/bdist.linux-x86_64/wheel
  running install
  running install_lib
  creating build/bdist.linux-x86_64/wheel
  creating build/bdist.linux-x86_64/wheel/pyRadPlan
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/ai_models
  copying build/lib/pyRadPlan/data/ai_models/model_config.template.json -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/ai_models
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/protons_Generic.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/photons_Generic.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/oxygen_Generic.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/helium_Generic.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/carbon_Generic.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/VHEE_Generic.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/VHEE_Focused.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/machines
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/phantoms
  copying build/lib/pyRadPlan/data/phantoms/TG119.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/phantoms
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/physics
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/physics/stopping
  copying build/lib/pyRadPlan/data/physics/stopping/SPtable.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/physics/stopping
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/rbe
  copying build/lib/pyRadPlan/data/rbe/RBEtable_LEMI_Scholz06_AX01_BX005.mat -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/rbe
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/topas
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/topas/input
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/topas/input/beam_setup
  copying build/lib/pyRadPlan/data/topas/input/beam_setup/TOPAS_beam_setup_biGaussian.txt.in -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/beam_setup
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/topas/input/material_converter
  copying build/lib/pyRadPlan/data/topas/input/material_converter/TOPAS_schneider_converter.txt.in -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/material_converter
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/topas/input/scorer
  copying build/lib/pyRadPlan/data/topas/input/scorer/TOPAS_subscorer_LET.txt.in -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/scorer
  copying build/lib/pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToWater.txt.in -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/scorer
  copying build/lib/pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToMedium.txt.in -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/scorer
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/data/topas/input/template
  copying build/lib/pyRadPlan/data/topas/input/template/pyRadPlan_run_template.txt -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/template
  copying build/lib/pyRadPlan/data/topas/input/template/patient_template.txt -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/template
  copying build/lib/pyRadPlan/data/topas/input/template/beamSetup_template.txt -> build/bdist.linux-x86_64/wheel/./pyRadPlan/data/topas/input/template
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/ai
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_cst_agents.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_models.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_plan_agents.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_settings.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_usage.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/agents
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_load_model.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_preprocessor.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_registry.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_resolve.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_settings.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ai
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_dvh.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_dvh_plot.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_qi.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_units.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/analysis
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/biology
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/biology/models
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/const_rbe.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/kernel_based_lq_model.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/let_based_lq_models.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/lq_models.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/tabulated_rbe_models.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/zstar_based_lq_model.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_engine.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_evaluator.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_influence.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_kernel_sets.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_options.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_resolution.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_tissue_lookup.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/models
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_conversion.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_presets.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_provenance.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_reference.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_sources.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/biology
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/core
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_backends.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_image_interpolation.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_interpolation.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_interpolation1d.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_reductions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/compat.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/helpers.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/jittable.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/typing.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_base_data.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_configurable.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_exceptions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_grids.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_paths.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_progress.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_units.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/datamodel.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/np2sitk.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/resample.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/core
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/cst
  copying build/lib/pyRadPlan/cst/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/cst
  copying build/lib/pyRadPlan/cst/_cst.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/cst
  copying build/lib/pyRadPlan/cst/_voi.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/cst
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/ct
  copying build/lib/pyRadPlan/ct/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ct
  copying build/lib/pyRadPlan/ct/_ct.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ct
  copying build/lib/pyRadPlan/ct/_hlut.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/ct
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/dij
  copying build/lib/pyRadPlan/dij/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dij
  copying build/lib/pyRadPlan/dij/_compose_beam_dijs.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dij
  copying build/lib/pyRadPlan/dij/_dij.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dij
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/dose
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/dose/engines
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/dose/engines/kernels
  copying build/lib/pyRadPlan/dose/engines/kernels/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines/kernels
  copying build/lib/pyRadPlan/dose/engines/kernels/_calc_geo_dists_kernel.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines/kernels
  copying build/lib/pyRadPlan/dose/engines/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base_montecarlo.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base_pencilbeam.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base_pencilbeam_particle.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_fredmc.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_hongpb.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_particle_cutoff.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_sparse_output.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_svdpb.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_topasmc.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose
  copying build/lib/pyRadPlan/dose/_calc_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/dose
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/geometry
  copying build/lib/pyRadPlan/geometry/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/geometry
  copying build/lib/pyRadPlan/geometry/lps.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/geometry
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/assets
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/pyradplan_logo_skull_square.svg -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/pyradplan_logo_full_landscape.svg -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/dkfz_logo_white.svg -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/dkfz_logo_blue.svg -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/assets
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_base_data.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_edit.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_file.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_help.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_settings.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_view.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/menus
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/widgets
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/widgets/ai
  copying build/lib/pyRadPlan/gui/widgets/ai/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/ai
  copying build/lib/pyRadPlan/gui/widgets/ai/_ai_task_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/ai
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_dvh.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_gamma.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_qi.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_qi_point_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/analysis
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/widgets/optimization
  copying build/lib/pyRadPlan/gui/widgets/optimization/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/optimization
  copying build/lib/pyRadPlan/gui/widgets/optimization/_optimization_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/optimization
  copying build/lib/pyRadPlan/gui/widgets/optimization/_status_window.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/optimization
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/_optimization_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/_plan_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/_tissue_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/plan
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/_labels.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/_orientation_marker.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/_save_result_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/quantity_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/viewer_options_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/visualization_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/vois_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/result
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_binary_import_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_dicom_import_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_image_import_dialog.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_workflow_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_analysis_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_config_form.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_display_names.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_info_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_log_console.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_logo_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_plot_theme.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_quantity_selector.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_result_widget.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_tissue_params.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_ui_mode.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/widgets
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_analysis_win.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_main_win.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_result_win.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_splash_win.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui
  copying build/lib/pyRadPlan/gui/apps.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui
  copying build/lib/pyRadPlan/gui/workspace.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/gui
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io
  copying build/lib/pyRadPlan/io/.gitkeep -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/base
  copying build/lib/pyRadPlan/io/base/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/base
  copying build/lib/pyRadPlan/io/base/_exporter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/base
  copying build/lib/pyRadPlan/io/base/_importer.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/base
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_common.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_cst.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_ct.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_seg.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_exporter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_handler.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_helpers.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_import_cst.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_import_ct.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_import_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_importer.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/dicom
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_exporter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_handler.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_importer.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_matfile.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/matlab
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_exporter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_handler.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_importer.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_serialize.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/npz
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/_exporter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/_handler.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/_importer.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/pickle
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/sitk_based
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/_exporter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/_importer.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/_serialize.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based/base
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/sitk_based/meta_image
  copying build/lib/pyRadPlan/io/sitk_based/meta_image/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based/meta_image
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/sitk_based/nifti
  copying build/lib/pyRadPlan/io/sitk_based/nifti/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based/nifti
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/io/sitk_based/nrrd
  copying build/lib/pyRadPlan/io/sitk_based/nrrd/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based/nrrd
  copying build/lib/pyRadPlan/io/sitk_based/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based
  copying build/lib/pyRadPlan/io/sitk_based/_binary_import.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io/sitk_based
  copying build/lib/pyRadPlan/io/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io
  copying build/lib/pyRadPlan/io/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io
  copying build/lib/pyRadPlan/io/_helpers.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io
  copying build/lib/pyRadPlan/io/_load_save.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/io
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/machines
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_beam_modifier.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_external_beam.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_internal_beam.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/base
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/machines/particles
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/_ions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/_vhee.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_cutoff.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_emittance.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_energy.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_focus.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_fragment_spectrum.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_ions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_vhee.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/particles
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/_calculate_machine_scale.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/_linac.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/_svd_kernel.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines
  copying build/lib/pyRadPlan/machines/_load.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines
  copying build/lib/pyRadPlan/machines/_validate.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/machines
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/.gitkeep -> build/bdist.linux-x86_64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/_engine.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/_matlab.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/_octave.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/matRad
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/optimization
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_eud.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_max_dvh.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_mean.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_min_dvh.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_objective.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_dev.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_mimicking.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_over.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_under.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_std.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_variance.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/objectives
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_nonlin_fluence.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_optiprob.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_simple_least_squares.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/problems
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_composite_worst_case.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_expected_quantity.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_expected_value.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_none.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_objective_worst_case.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_smooth_max.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_terms.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_utils.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_voxel_worst_case.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/robustness
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_base_solvers.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_ipax_solver.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_ipopt.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_scipy_solver.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization
  copying build/lib/pyRadPlan/optimization/_fluence_optimization.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/optimization
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/plan
  copying build/lib/pyRadPlan/plan/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/plan
  copying build/lib/pyRadPlan/plan/_plans.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/plan
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_alpha_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_effect.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_let.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_let_x_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_rbe_x_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_resolver.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_sqrt_beta_dose.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/quantities
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_kernels.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_kernels_numba.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_siddon.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/raytracer
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_dimensions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_distributions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_explicit.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_gridded.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_importance.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_legacy.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_nominal.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_random.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_set.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_worst_case.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/scenarios
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/stf
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/stf/hlutLibrary
  copying build/lib/pyRadPlan/stf/hlutLibrary/matRad_default.hlut -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/hlutLibrary
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_base.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_externalbeam.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_factory.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_ions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_photons.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_vhee.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_beam.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_beamlet.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_exceptions.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_generate_stf.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_presets.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_rangeshifter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_ray.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_steeringinformation.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/stf
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/util
  copying build/lib/pyRadPlan/util/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/_jupyter.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/helpers.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/keyboard_listener.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/logging_utils.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/openmp.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/util
  creating build/bdist.linux-x86_64/wheel/pyRadPlan/visualization
  copying build/lib/pyRadPlan/visualization/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/visualization
  copying build/lib/pyRadPlan/visualization/_plot_multiple_slices.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/visualization
  copying build/lib/pyRadPlan/visualization/_plot_slice.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan/visualization
  copying build/lib/pyRadPlan/__init__.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan
  copying build/lib/pyRadPlan/_lazy.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan
  copying build/lib/pyRadPlan/_settings.py -> build/bdist.linux-x86_64/wheel/./pyRadPlan
  running install_egg_info
  Copying pyRadPlan.egg-info to build/bdist.linux-x86_64/wheel/./pyRadPlan-0.8.0-py3.14.egg-info
  running install_scripts
  creating build/bdist.linux-x86_64/wheel/pyradplan-0.8.0.dist-info/WHEEL
  creating '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-wheel-2lc8wx5t/.tmp-aobx5hy0/pyradplan-0.8.0-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it
  adding 'pyRadPlan/__init__.py'
  adding 'pyRadPlan/_lazy.py'
  adding 'pyRadPlan/_settings.py'
  adding 'pyRadPlan/ai/__init__.py'
  adding 'pyRadPlan/ai/agents/__init__.py'
  adding 'pyRadPlan/ai/agents/_cst_agents.py'
  adding 'pyRadPlan/ai/agents/_models.py'
  adding 'pyRadPlan/ai/agents/_plan_agents.py'
  adding 'pyRadPlan/ai/agents/_settings.py'
  adding 'pyRadPlan/ai/agents/_usage.py'
  adding 'pyRadPlan/ai/modelhub/__init__.py'
  adding 'pyRadPlan/ai/modelhub/_load_model.py'
  adding 'pyRadPlan/ai/modelhub/_preprocessor.py'
  adding 'pyRadPlan/ai/modelhub/_registry.py'
  adding 'pyRadPlan/ai/modelhub/_resolve.py'
  adding 'pyRadPlan/ai/modelhub/_settings.py'
  adding 'pyRadPlan/analysis/__init__.py'
  adding 'pyRadPlan/analysis/_dvh.py'
  adding 'pyRadPlan/analysis/_dvh_plot.py'
  adding 'pyRadPlan/analysis/_qi.py'
  adding 'pyRadPlan/analysis/_units.py'
  adding 'pyRadPlan/biology/__init__.py'
  adding 'pyRadPlan/biology/models/__init__.py'
  adding 'pyRadPlan/biology/models/_base.py'
  adding 'pyRadPlan/biology/models/_engine.py'
  adding 'pyRadPlan/biology/models/_evaluator.py'
  adding 'pyRadPlan/biology/models/_factory.py'
  adding 'pyRadPlan/biology/models/_influence.py'
  adding 'pyRadPlan/biology/models/_kernel_sets.py'
  adding 'pyRadPlan/biology/models/_options.py'
  adding 'pyRadPlan/biology/models/_resolution.py'
  adding 'pyRadPlan/biology/models/_tissue_lookup.py'
  adding 'pyRadPlan/biology/models/_models/const_rbe.py'
  adding 'pyRadPlan/biology/models/_models/kernel_based_lq_model.py'
  adding 'pyRadPlan/biology/models/_models/let_based_lq_models.py'
  adding 'pyRadPlan/biology/models/_models/lq_models.py'
  adding 'pyRadPlan/biology/models/_models/tabulated_rbe_models.py'
  adding 'pyRadPlan/biology/models/_models/zstar_based_lq_model.py'
  adding 'pyRadPlan/biology/tissues/__init__.py'
  adding 'pyRadPlan/biology/tissues/_base.py'
  adding 'pyRadPlan/biology/tissues/_conversion.py'
  adding 'pyRadPlan/biology/tissues/_presets.py'
  adding 'pyRadPlan/biology/tissues/_provenance.py'
  adding 'pyRadPlan/biology/tissues/_reference.py'
  adding 'pyRadPlan/biology/tissues/_sources.py'
  adding 'pyRadPlan/core/__init__.py'
  adding 'pyRadPlan/core/_base_data.py'
  adding 'pyRadPlan/core/_configurable.py'
  adding 'pyRadPlan/core/_exceptions.py'
  adding 'pyRadPlan/core/_grids.py'
  adding 'pyRadPlan/core/_paths.py'
  adding 'pyRadPlan/core/_progress.py'
  adding 'pyRadPlan/core/_units.py'
  adding 'pyRadPlan/core/datamodel.py'
  adding 'pyRadPlan/core/np2sitk.py'
  adding 'pyRadPlan/core/resample.py'
  adding 'pyRadPlan/core/xp_utils/__init__.py'
  adding 'pyRadPlan/core/xp_utils/_backends.py'
  adding 'pyRadPlan/core/xp_utils/_image_interpolation.py'
  adding 'pyRadPlan/core/xp_utils/_interpolation.py'
  adding 'pyRadPlan/core/xp_utils/_interpolation1d.py'
  adding 'pyRadPlan/core/xp_utils/_reductions.py'
  adding 'pyRadPlan/core/xp_utils/compat.py'
  adding 'pyRadPlan/core/xp_utils/helpers.py'
  adding 'pyRadPlan/core/xp_utils/jittable.py'
  adding 'pyRadPlan/core/xp_utils/typing.py'
  adding 'pyRadPlan/cst/__init__.py'
  adding 'pyRadPlan/cst/_cst.py'
  adding 'pyRadPlan/cst/_voi.py'
  adding 'pyRadPlan/ct/__init__.py'
  adding 'pyRadPlan/ct/_ct.py'
  adding 'pyRadPlan/ct/_hlut.py'
  adding 'pyRadPlan/data/ai_models/model_config.template.json'
  adding 'pyRadPlan/data/machines/VHEE_Focused.mat'
  adding 'pyRadPlan/data/machines/VHEE_Generic.mat'
  adding 'pyRadPlan/data/machines/carbon_Generic.mat'
  adding 'pyRadPlan/data/machines/helium_Generic.mat'
  adding 'pyRadPlan/data/machines/oxygen_Generic.mat'
  adding 'pyRadPlan/data/machines/photons_Generic.mat'
  adding 'pyRadPlan/data/machines/protons_Generic.mat'
  adding 'pyRadPlan/data/phantoms/TG119.mat'
  adding 'pyRadPlan/data/physics/stopping/SPtable.mat'
  adding 'pyRadPlan/data/rbe/RBEtable_LEMI_Scholz06_AX01_BX005.mat'
  adding 'pyRadPlan/data/topas/input/beam_setup/TOPAS_beam_setup_biGaussian.txt.in'
  adding 'pyRadPlan/data/topas/input/material_converter/TOPAS_schneider_converter.txt.in'
  adding 'pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToMedium.txt.in'
  adding 'pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToWater.txt.in'
  adding 'pyRadPlan/data/topas/input/scorer/TOPAS_subscorer_LET.txt.in'
  adding 'pyRadPlan/data/topas/input/template/beamSetup_template.txt'
  adding 'pyRadPlan/data/topas/input/template/patient_template.txt'
  adding 'pyRadPlan/data/topas/input/template/pyRadPlan_run_template.txt'
  adding 'pyRadPlan/dij/__init__.py'
  adding 'pyRadPlan/dij/_compose_beam_dijs.py'
  adding 'pyRadPlan/dij/_dij.py'
  adding 'pyRadPlan/dose/__init__.py'
  adding 'pyRadPlan/dose/_calc_dose.py'
  adding 'pyRadPlan/dose/engines/__init__.py'
  adding 'pyRadPlan/dose/engines/_base.py'
  adding 'pyRadPlan/dose/engines/_base_montecarlo.py'
  adding 'pyRadPlan/dose/engines/_base_pencilbeam.py'
  adding 'pyRadPlan/dose/engines/_base_pencilbeam_particle.py'
  adding 'pyRadPlan/dose/engines/_factory.py'
  adding 'pyRadPlan/dose/engines/_fredmc.py'
  adding 'pyRadPlan/dose/engines/_hongpb.py'
  adding 'pyRadPlan/dose/engines/_particle_cutoff.py'
  adding 'pyRadPlan/dose/engines/_sparse_output.py'
  adding 'pyRadPlan/dose/engines/_svdpb.py'
  adding 'pyRadPlan/dose/engines/_topasmc.py'
  adding 'pyRadPlan/dose/engines/kernels/__init__.py'
  adding 'pyRadPlan/dose/engines/kernels/_calc_geo_dists_kernel.py'
  adding 'pyRadPlan/geometry/__init__.py'
  adding 'pyRadPlan/geometry/lps.py'
  adding 'pyRadPlan/gui/__init__.py'
  adding 'pyRadPlan/gui/apps.py'
  adding 'pyRadPlan/gui/workspace.py'
  adding 'pyRadPlan/gui/assets/__init__.py'
  adding 'pyRadPlan/gui/assets/logos/dkfz_logo_blue.svg'
  adding 'pyRadPlan/gui/assets/logos/dkfz_logo_white.svg'
  adding 'pyRadPlan/gui/assets/logos/pyradplan_logo_full_landscape.svg'
  adding 'pyRadPlan/gui/assets/logos/pyradplan_logo_skull_square.svg'
  adding 'pyRadPlan/gui/menus/__init__.py'
  adding 'pyRadPlan/gui/menus/_base_data.py'
  adding 'pyRadPlan/gui/menus/_edit.py'
  adding 'pyRadPlan/gui/menus/_file.py'
  adding 'pyRadPlan/gui/menus/_help.py'
  adding 'pyRadPlan/gui/menus/_settings.py'
  adding 'pyRadPlan/gui/menus/_view.py'
  adding 'pyRadPlan/gui/widgets/__init__.py'
  adding 'pyRadPlan/gui/widgets/_analysis_widget.py'
  adding 'pyRadPlan/gui/widgets/_base.py'
  adding 'pyRadPlan/gui/widgets/_config_form.py'
  adding 'pyRadPlan/gui/widgets/_display_names.py'
  adding 'pyRadPlan/gui/widgets/_info_widget.py'
  adding 'pyRadPlan/gui/widgets/_log_console.py'
  adding 'pyRadPlan/gui/widgets/_logo_widget.py'
  adding 'pyRadPlan/gui/widgets/_plot_theme.py'
  adding 'pyRadPlan/gui/widgets/_quantity_selector.py'
  adding 'pyRadPlan/gui/widgets/_result_widget.py'
  adding 'pyRadPlan/gui/widgets/_tissue_params.py'
  adding 'pyRadPlan/gui/widgets/_ui_mode.py'
  adding 'pyRadPlan/gui/widgets/ai/__init__.py'
  adding 'pyRadPlan/gui/widgets/ai/_ai_task_dialog.py'
  adding 'pyRadPlan/gui/widgets/analysis/_dvh.py'
  adding 'pyRadPlan/gui/widgets/analysis/_gamma.py'
  adding 'pyRadPlan/gui/widgets/analysis/_qi.py'
  adding 'pyRadPlan/gui/widgets/analysis/_qi_point_dialog.py'
  adding 'pyRadPlan/gui/widgets/optimization/__init__.py'
  adding 'pyRadPlan/gui/widgets/optimization/_optimization_widget.py'
  adding 'pyRadPlan/gui/widgets/optimization/_status_window.py'
  adding 'pyRadPlan/gui/widgets/plan/__init__.py'
  adding 'pyRadPlan/gui/widgets/plan/_optimization_dialog.py'
  adding 'pyRadPlan/gui/widgets/plan/_plan_widget.py'
  adding 'pyRadPlan/gui/widgets/plan/_tissue_dialog.py'
  adding 'pyRadPlan/gui/widgets/result/__init__.py'
  adding 'pyRadPlan/gui/widgets/result/_labels.py'
  adding 'pyRadPlan/gui/widgets/result/_orientation_marker.py'
  adding 'pyRadPlan/gui/widgets/result/_save_result_dialog.py'
  adding 'pyRadPlan/gui/widgets/result/quantity_widget.py'
  adding 'pyRadPlan/gui/widgets/result/viewer_options_widget.py'
  adding 'pyRadPlan/gui/widgets/result/visualization_widget.py'
  adding 'pyRadPlan/gui/widgets/result/vois_widget.py'
  adding 'pyRadPlan/gui/widgets/workflow/__init__.py'
  adding 'pyRadPlan/gui/widgets/workflow/_binary_import_dialog.py'
  adding 'pyRadPlan/gui/widgets/workflow/_dicom_import_dialog.py'
  adding 'pyRadPlan/gui/widgets/workflow/_image_import_dialog.py'
  adding 'pyRadPlan/gui/widgets/workflow/_workflow_widget.py'
  adding 'pyRadPlan/gui/windows/_analysis_win.py'
  adding 'pyRadPlan/gui/windows/_main_win.py'
  adding 'pyRadPlan/gui/windows/_result_win.py'
  adding 'pyRadPlan/gui/windows/_splash_win.py'
  adding 'pyRadPlan/io/.gitkeep'
  adding 'pyRadPlan/io/__init__.py'
  adding 'pyRadPlan/io/_factory.py'
  adding 'pyRadPlan/io/_helpers.py'
  adding 'pyRadPlan/io/_load_save.py'
  adding 'pyRadPlan/io/base/__init__.py'
  adding 'pyRadPlan/io/base/_exporter.py'
  adding 'pyRadPlan/io/base/_importer.py'
  adding 'pyRadPlan/io/dicom/__init__.py'
  adding 'pyRadPlan/io/dicom/_export_common.py'
  adding 'pyRadPlan/io/dicom/_export_cst.py'
  adding 'pyRadPlan/io/dicom/_export_ct.py'
  adding 'pyRadPlan/io/dicom/_export_dose.py'
  adding 'pyRadPlan/io/dicom/_export_seg.py'
  adding 'pyRadPlan/io/dicom/_exporter.py'
  adding 'pyRadPlan/io/dicom/_handler.py'
  adding 'pyRadPlan/io/dicom/_helpers.py'
  adding 'pyRadPlan/io/dicom/_import_cst.py'
  adding 'pyRadPlan/io/dicom/_import_ct.py'
  adding 'pyRadPlan/io/dicom/_import_dose.py'
  adding 'pyRadPlan/io/dicom/_importer.py'
  adding 'pyRadPlan/io/matlab/__init__.py'
  adding 'pyRadPlan/io/matlab/_exporter.py'
  adding 'pyRadPlan/io/matlab/_handler.py'
  adding 'pyRadPlan/io/matlab/_importer.py'
  adding 'pyRadPlan/io/matlab/_matfile.py'
  adding 'pyRadPlan/io/npz/__init__.py'
  adding 'pyRadPlan/io/npz/_exporter.py'
  adding 'pyRadPlan/io/npz/_handler.py'
  adding 'pyRadPlan/io/npz/_importer.py'
  adding 'pyRadPlan/io/npz/_serialize.py'
  adding 'pyRadPlan/io/pickle/__init__.py'
  adding 'pyRadPlan/io/pickle/_exporter.py'
  adding 'pyRadPlan/io/pickle/_handler.py'
  adding 'pyRadPlan/io/pickle/_importer.py'
  adding 'pyRadPlan/io/sitk_based/__init__.py'
  adding 'pyRadPlan/io/sitk_based/_binary_import.py'
  adding 'pyRadPlan/io/sitk_based/base/__init__.py'
  adding 'pyRadPlan/io/sitk_based/base/_exporter.py'
  adding 'pyRadPlan/io/sitk_based/base/_importer.py'
  adding 'pyRadPlan/io/sitk_based/base/_serialize.py'
  adding 'pyRadPlan/io/sitk_based/meta_image/__init__.py'
  adding 'pyRadPlan/io/sitk_based/nifti/__init__.py'
  adding 'pyRadPlan/io/sitk_based/nrrd/__init__.py'
  adding 'pyRadPlan/machines/__init__.py'
  adding 'pyRadPlan/machines/_load.py'
  adding 'pyRadPlan/machines/_validate.py'
  adding 'pyRadPlan/machines/base/__init__.py'
  adding 'pyRadPlan/machines/base/_base.py'
  adding 'pyRadPlan/machines/base/_beam_modifier.py'
  adding 'pyRadPlan/machines/base/_external_beam.py'
  adding 'pyRadPlan/machines/base/_factory.py'
  adding 'pyRadPlan/machines/base/_internal_beam.py'
  adding 'pyRadPlan/machines/particles/__init__.py'
  adding 'pyRadPlan/machines/particles/_base.py'
  adding 'pyRadPlan/machines/particles/_beam_cutoff.py'
  adding 'pyRadPlan/machines/particles/_beam_emittance.py'
  adding 'pyRadPlan/machines/particles/_beam_energy.py'
  adding 'pyRadPlan/machines/particles/_beam_focus.py'
  adding 'pyRadPlan/machines/particles/_beam_fragment_spectrum.py'
  adding 'pyRadPlan/machines/particles/_ions.py'
  adding 'pyRadPlan/machines/particles/_vhee.py'
  adding 'pyRadPlan/machines/particles/kernel/__init__.py'
  adding 'pyRadPlan/machines/particles/kernel/_base.py'
  adding 'pyRadPlan/machines/particles/kernel/_ions.py'
  adding 'pyRadPlan/machines/particles/kernel/_vhee.py'
  adding 'pyRadPlan/machines/photons/__init__.py'
  adding 'pyRadPlan/machines/photons/_base.py'
  adding 'pyRadPlan/machines/photons/_calculate_machine_scale.py'
  adding 'pyRadPlan/machines/photons/_linac.py'
  adding 'pyRadPlan/machines/photons/_svd_kernel.py'
  adding 'pyRadPlan/matRad/.gitkeep'
  adding 'pyRadPlan/matRad/__init__.py'
  adding 'pyRadPlan/matRad/_engine.py'
  adding 'pyRadPlan/matRad/_matlab.py'
  adding 'pyRadPlan/matRad/_octave.py'
  adding 'pyRadPlan/optimization/__init__.py'
  adding 'pyRadPlan/optimization/_fluence_optimization.py'
  adding 'pyRadPlan/optimization/objectives/__init__.py'
  adding 'pyRadPlan/optimization/objectives/_eud.py'
  adding 'pyRadPlan/optimization/objectives/_factory.py'
  adding 'pyRadPlan/optimization/objectives/_max_dvh.py'
  adding 'pyRadPlan/optimization/objectives/_mean.py'
  adding 'pyRadPlan/optimization/objectives/_min_dvh.py'
  adding 'pyRadPlan/optimization/objectives/_objective.py'
  adding 'pyRadPlan/optimization/objectives/_squared_dev.py'
  adding 'pyRadPlan/optimization/objectives/_squared_mimicking.py'
  adding 'pyRadPlan/optimization/objectives/_squared_over.py'
  adding 'pyRadPlan/optimization/objectives/_squared_under.py'
  adding 'pyRadPlan/optimization/objectives/_std.py'
  adding 'pyRadPlan/optimization/objectives/_variance.py'
  adding 'pyRadPlan/optimization/problems/__init__.py'
  adding 'pyRadPlan/optimization/problems/_factory.py'
  adding 'pyRadPlan/optimization/problems/_nonlin_fluence.py'
  adding 'pyRadPlan/optimization/problems/_optiprob.py'
  adding 'pyRadPlan/optimization/problems/_simple_least_squares.py'
  adding 'pyRadPlan/optimization/robustness/__init__.py'
  adding 'pyRadPlan/optimization/robustness/_base.py'
  adding 'pyRadPlan/optimization/robustness/_composite_worst_case.py'
  adding 'pyRadPlan/optimization/robustness/_expected_quantity.py'
  adding 'pyRadPlan/optimization/robustness/_expected_value.py'
  adding 'pyRadPlan/optimization/robustness/_factory.py'
  adding 'pyRadPlan/optimization/robustness/_none.py'
  adding 'pyRadPlan/optimization/robustness/_objective_worst_case.py'
  adding 'pyRadPlan/optimization/robustness/_smooth_max.py'
  adding 'pyRadPlan/optimization/robustness/_terms.py'
  adding 'pyRadPlan/optimization/robustness/_utils.py'
  adding 'pyRadPlan/optimization/robustness/_voxel_worst_case.py'
  adding 'pyRadPlan/optimization/solvers/__init__.py'
  adding 'pyRadPlan/optimization/solvers/_base_solvers.py'
  adding 'pyRadPlan/optimization/solvers/_factory.py'
  adding 'pyRadPlan/optimization/solvers/_ipax_solver.py'
  adding 'pyRadPlan/optimization/solvers/_ipopt.py'
  adding 'pyRadPlan/optimization/solvers/_scipy_solver.py'
  adding 'pyRadPlan/plan/__init__.py'
  adding 'pyRadPlan/plan/_plans.py'
  adding 'pyRadPlan/quantities/__init__.py'
  adding 'pyRadPlan/quantities/_alpha_dose.py'
  adding 'pyRadPlan/quantities/_base.py'
  adding 'pyRadPlan/quantities/_dose.py'
  adding 'pyRadPlan/quantities/_effect.py'
  adding 'pyRadPlan/quantities/_let.py'
  adding 'pyRadPlan/quantities/_let_x_dose.py'
  adding 'pyRadPlan/quantities/_rbe_x_dose.py'
  adding 'pyRadPlan/quantities/_resolver.py'
  adding 'pyRadPlan/quantities/_sqrt_beta_dose.py'
  adding 'pyRadPlan/raytracer/__init__.py'
  adding 'pyRadPlan/raytracer/_base.py'
  adding 'pyRadPlan/raytracer/_kernels.py'
  adding 'pyRadPlan/raytracer/_kernels_numba.py'
  adding 'pyRadPlan/raytracer/_siddon.py'
  adding 'pyRadPlan/scenarios/__init__.py'
  adding 'pyRadPlan/scenarios/_base.py'
  adding 'pyRadPlan/scenarios/_dimensions.py'
  adding 'pyRadPlan/scenarios/_distributions.py'
  adding 'pyRadPlan/scenarios/_explicit.py'
  adding 'pyRadPlan/scenarios/_gridded.py'
  adding 'pyRadPlan/scenarios/_importance.py'
  adding 'pyRadPlan/scenarios/_legacy.py'
  adding 'pyRadPlan/scenarios/_nominal.py'
  adding 'pyRadPlan/scenarios/_random.py'
  adding 'pyRadPlan/scenarios/_set.py'
  adding 'pyRadPlan/scenarios/_worst_case.py'
  adding 'pyRadPlan/stf/__init__.py'
  adding 'pyRadPlan/stf/_beam.py'
  adding 'pyRadPlan/stf/_beamlet.py'
  adding 'pyRadPlan/stf/_exceptions.py'
  adding 'pyRadPlan/stf/_generate_stf.py'
  adding 'pyRadPlan/stf/_presets.py'
  adding 'pyRadPlan/stf/_rangeshifter.py'
  adding 'pyRadPlan/stf/_ray.py'
  adding 'pyRadPlan/stf/_steeringinformation.py'
  adding 'pyRadPlan/stf/generators/__init__.py'
  adding 'pyRadPlan/stf/generators/_base.py'
  adding 'pyRadPlan/stf/generators/_externalbeam.py'
  adding 'pyRadPlan/stf/generators/_factory.py'
  adding 'pyRadPlan/stf/generators/_ions.py'
  adding 'pyRadPlan/stf/generators/_photons.py'
  adding 'pyRadPlan/stf/generators/_vhee.py'
  adding 'pyRadPlan/stf/hlutLibrary/matRad_default.hlut'
  adding 'pyRadPlan/util/__init__.py'
  adding 'pyRadPlan/util/_jupyter.py'
  adding 'pyRadPlan/util/helpers.py'
  adding 'pyRadPlan/util/keyboard_listener.py'
  adding 'pyRadPlan/util/logging_utils.py'
  adding 'pyRadPlan/util/openmp.py'
  adding 'pyRadPlan/visualization/__init__.py'
  adding 'pyRadPlan/visualization/_plot_multiple_slices.py'
  adding 'pyRadPlan/visualization/_plot_slice.py'
  adding 'pyradplan-0.8.0.dist-info/licenses/LICENSE'
  adding 'pyradplan-0.8.0.dist-info/METADATA'
  adding 'pyradplan-0.8.0.dist-info/WHEEL'
  adding 'pyradplan-0.8.0.dist-info/entry_points.txt'
  adding 'pyradplan-0.8.0.dist-info/top_level.txt'
  adding 'pyradplan-0.8.0.dist-info/RECORD'
  removing build/bdist.linux-x86_64/wheel
  Building wheel for pyRadPlan (pyproject.toml): finished with status 'done'
  Created wheel for pyRadPlan: filename=pyradplan-0.8.0-py3-none-any.whl size=32327555 sha256=94b5f21105c16a95ea17ed6b4e568dbf5b738c17e03b217665043a1bdcf39dd9
  Stored in directory: /builddir/.cache/pip/wheels/bf/c6/d9/519a10de0a2de160c5e3070733236db255354cd2b9dc1b2561
Successfully built pyRadPlan
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%install): /bin/sh -e /var/tmp/rpm-tmp.zOeOed
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ '[' /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT '!=' / ']'
+ rm -rf /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT
++ dirname /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT
+ mkdir -p /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ mkdir /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd pyradplan-0.8.0
++ xargs basename --multiple
++ ls /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir/pyradplan-0.8.0-py3-none-any.whl
++ sed -E 's/([^-]+)-([^-]+)-.+\.whl/\1==\2/'
+ specifier=pyradplan==0.8.0
+ '[' -z pyradplan==0.8.0 ']'
+ PIP_CONFIG_FILE=/dev/null
+ TMPDIR=/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir
+ /usr/bin/python3 -m pip install --root /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT --prefix /usr --no-deps --disable-pip-version-check --progress-bar off --verbose --ignore-installed --no-warn-script-location --no-index --no-cache-dir --find-links /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir pyradplan==0.8.0
Using pip 25.1.1 from /usr/lib/python3.14/site-packages/pip (python 3.14)
Looking in links: /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir
Processing /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir/pyradplan-0.8.0-py3-none-any.whl
Installing collected packages: pyradplan
  Creating /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin
  changing mode of /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin/pyRadPlanGUI to 755
Successfully installed pyradplan-0.8.0
+ '[' -d /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin ']'
+ '[' -z sP ']'
+ shebang_flags=-kasP
+ /usr/bin/python3 -B /usr/lib/rpm/redhat/pathfix.py -pni /usr/bin/python3 -kasP /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin/pyRadPlanGUI
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin/pyRadPlanGUI: updating
+ rm -rfv /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin/__pycache__
+ rm -f /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-ghost-distinfo
+ site_dirs=()
+ '[' -d /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages ']'
+ site_dirs+=("/usr/lib/python3.14/site-packages")
+ '[' /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib64/python3.14/site-packages '!=' /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages ']'
+ '[' -d /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib64/python3.14/site-packages ']'
+ for site_dir in ${site_dirs[@]}
+ for distinfo in /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT$site_dir/*.dist-info
+ echo '%ghost %dir /usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info'
+++ basename /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info
++ PYTHONPATH=/usr/lib/rpm/redhat
++ /usr/bin/python3 -sBc 'import sys; from pyproject_save_files import canonical_name_from_distinfo; print(canonical_name_from_distinfo(sys.argv[1]))' pyradplan-0.8.0.dist-info
+ _normalized=pyradplan
+ echo '%ghost %dir /usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info'
+ sed -i s/pip/rpm/ /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info/INSTALLER
+ PYTHONPATH=/usr/lib/rpm/redhat
+ /usr/bin/python3 -B /usr/lib/rpm/redhat/pyproject_preprocess_record.py --buildroot /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT --record /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info/RECORD --output /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-record
+ rm -fv /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info/RECORD
removed '/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info/RECORD'
+ rm -fv /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info/REQUESTED
removed '/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyradplan-0.8.0.dist-info/REQUESTED'
+ '[' -f /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-dep-overrides ']'
++ wc -l /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-ghost-distinfo
++ cut -f1 '-d '
+ lines=1
+ '[' 1 -ne 1 ']'
+ RPM_FILES_ESCAPE=4.19
+ /usr/bin/python3 /usr/lib/rpm/redhat/pyproject_save_files.py --output-files /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-files --output-modules /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-modules --buildroot /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT --sitelib /usr/lib/python3.14/site-packages --sitearch /usr/lib64/python3.14/site-packages --python-version 3.14 --pyproject-record /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-record --prefix /usr pyRadPlan
+ /usr/lib/rpm/check-buildroot
+ /usr/lib/rpm/redhat/brp-ldconfig
+ /usr/lib/rpm/brp-compress
+ /usr/lib/rpm/brp-strip /usr/bin/strip
+ /usr/lib/rpm/brp-strip-comment-note /usr/bin/strip /usr/bin/objdump
+ /usr/lib/rpm/redhat/brp-strip-lto /usr/bin/strip
+ /usr/lib/rpm/check-rpaths
+ /usr/lib/rpm/redhat/brp-mangle-shebangs
+ /usr/lib/rpm/brp-remove-la-files
+ /usr/lib/rpm/redhat/brp-python-rpm-in-distinfo
+ env /usr/lib/rpm/redhat/brp-python-bytecompile '' 1 0 -j4
Bytecompiling .py files below /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14 using python3.14
+ /usr/lib/rpm/redhat/brp-python-hardlink
+ /usr/bin/add-determinism --brp -j4 /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/__pycache__/_lazy.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/visualization/__pycache__/_plot_multiple_slices.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/visualization/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/__pycache__/_settings.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/util/__pycache__/openmp.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/util/__pycache__/logging_utils.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/util/__pycache__/_jupyter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/util/__pycache__/keyboard_listener.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/util/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/util/__pycache__/helpers.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_ray.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/visualization/__pycache__/_plot_slice.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_rangeshifter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_steeringinformation.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_generate_stf.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_beamlet.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_exceptions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_presets.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/__pycache__/_beam.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/__pycache__/_photons.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/__pycache__/_vhee.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/__pycache__/_ions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/__pycache__/_externalbeam.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_dimensions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_worst_case.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_importance.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_gridded.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_random.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_nominal.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_explicit.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/__pycache__/_kernels_numba.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/__pycache__/_kernels.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_legacy.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_distributions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_let.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/__pycache__/_siddon.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_effect.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_sqrt_beta_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_alpha_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_resolver.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_let_x_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_rbe_x_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/plan/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/__pycache__/_fluence_optimization.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/__pycache__/_set.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/__pycache__/_scipy_solver.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/plan/__pycache__/_plans.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/__pycache__/_base_solvers.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/__pycache__/_ipopt.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/__pycache__/_ipax_solver.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_smooth_max.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_utils.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_voxel_worst_case.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_objective_worst_case.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_terms.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_none.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_expected_value.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_expected_quantity.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/__pycache__/_composite_worst_case.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_squared_dev.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/__pycache__/_simple_least_squares.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_std.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_max_dvh.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/__pycache__/_nonlin_fluence.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_squared_under.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_variance.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_squared_over.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_squared_mimicking.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_min_dvh.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_eud.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_mean.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/__pycache__/_objective.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/matRad/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/matRad/__pycache__/_engine.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/matRad/__pycache__/_octave.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/matRad/__pycache__/_matlab.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/__pycache__/_validate.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/__pycache__/_load.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/__pycache__/_linac.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/__pycache__/_calculate_machine_scale.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/__pycache__/_svd_kernel.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_vhee.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/__pycache__/_optiprob.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_ions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_beam_emittance.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_beam_cutoff.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_beam_energy.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/__pycache__/_vhee.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_beam_focus.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/__pycache__/_ions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_beam_fragment_spectrum.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/__pycache__/_internal_beam.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/__pycache__/_beam_modifier.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/__pycache__/_helpers.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/__pycache__/_load_save.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/nrrd/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/nifti/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/__pycache__/_external_beam.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/__pycache__/_binary_import.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/meta_image/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/base/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/pickle/__pycache__/_handler.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/base/__pycache__/_exporter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/base/__pycache__/_importer.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/pickle/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/pickle/__pycache__/_exporter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/npz/__pycache__/_handler.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/pickle/__pycache__/_importer.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/npz/__pycache__/_exporter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/sitk_based/base/__pycache__/_serialize.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/npz/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/matlab/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/npz/__pycache__/_serialize.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/matlab/__pycache__/_handler.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/matlab/__pycache__/_matfile.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/matlab/__pycache__/_exporter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/npz/__pycache__/_importer.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/matlab/__pycache__/_importer.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_import_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_import_ct.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_export_seg.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_export_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_helpers.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_export_ct.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_importer.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_handler.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_import_cst.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_export_common.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_export_cst.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/dicom/__pycache__/_exporter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/base/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/base/__pycache__/_exporter.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/base/__pycache__/_importer.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/windows/__pycache__/_result_win.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/__pycache__/workspace.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/__pycache__/apps.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_ui_mode.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/windows/__pycache__/_analysis_win.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/windows/__pycache__/_main_win.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_info_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/windows/__pycache__/_splash_win.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_display_names.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_quantity_selector.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_result_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_plot_theme.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_logo_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_analysis_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_log_console.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_tissue_params.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_config_form.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/workflow/__pycache__/_dicom_import_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/workflow/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/workflow/__pycache__/_image_import_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/workflow/__pycache__/_binary_import_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/visualization_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/viewer_options_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/_save_result_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/_orientation_marker.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/_labels.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/vois_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/plan/__pycache__/_tissue_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/workflow/__pycache__/_workflow_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/plan/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/plan/__pycache__/_optimization_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/optimization/__pycache__/_status_window.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/optimization/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/result/__pycache__/quantity_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/analysis/__pycache__/_gamma.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/analysis/__pycache__/_qi.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/analysis/__pycache__/_qi_point_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/ai/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/ai/__pycache__/_ai_task_dialog.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/optimization/__pycache__/_optimization_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/menus/__pycache__/_file.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/menus/__pycache__/_edit.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/menus/__pycache__/_help.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/analysis/__pycache__/_dvh.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/widgets/plan/__pycache__/_plan_widget.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/menus/__pycache__/_view.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/menus/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/assets/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/geometry/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/geometry/__pycache__/lps.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/__pycache__/_calc_dose.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_hongpb.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/menus/__pycache__/_settings.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/gui/menus/__pycache__/_base_data.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_svdpb.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_topasmc.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_sparse_output.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_particle_cutoff.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_fredmc.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_base_montecarlo.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_base_pencilbeam.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/kernels/__pycache__/_calc_geo_dists_kernel.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_base_pencilbeam_particle.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/kernels/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/__pycache__/_compose_beam_dijs.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ct/__pycache__/_hlut.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_base_pencilbeam_particle.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ct/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ct/__pycache__/_ct.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/cst/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/resample.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/cst/__pycache__/_voi.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/cst/__pycache__/_cst.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/__pycache__/_dij.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/np2sitk.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/_paths.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/datamodel.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/_progress.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/_units.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/datamodel.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/_exceptions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/_configurable.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/_grids.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/__pycache__/_base_data.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/compat.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/_interpolation.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/typing.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/_image_interpolation.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/_reductions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/compat.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/_interpolation1d.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/_backends.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/jittable.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/tissues/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/tissues/__pycache__/_reference.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/tissues/__pycache__/_provenance.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/__pycache__/helpers.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/tissues/__pycache__/_sources.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/tissues/__pycache__/_conversion.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/tissues/__pycache__/_presets.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_tissue_lookup.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_options.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_influence.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_factory.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/tissues/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_resolution.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_kernel_sets.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_engine.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/__pycache__/zstar_based_lq_model.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/__pycache__/let_based_lq_models.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_evaluator.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/__pycache__/const_rbe.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/__pycache__/lq_models.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/__pycache__/kernel_based_lq_model.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/__pycache__/_base.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/__pycache__/_units.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/__pycache__/_dvh_plot.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/modelhub/__pycache__/_settings.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/modelhub/__pycache__/_resolve.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/__pycache__/tabulated_rbe_models.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/__pycache__/_dvh.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/modelhub/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/modelhub/__pycache__/_registry.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/modelhub/__pycache__/_load_model.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/modelhub/__pycache__/_preprocessor.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/agents/__pycache__/_models.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/agents/__pycache__/_plan_agents.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/agents/__pycache__/_settings.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/agents/__pycache__/_usage.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/agents/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/ai/agents/__pycache__/_cst_agents.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/__pycache__/_qi.cpython-314.opt-1.pyc: rewriting with normalized contents
Scanned 141 directories and 972 files,
               processed 316 inodes,
               316 modified (6 replaced + 310 rewritten),
               0 unsupported format, 0 errors
Executing(%check): /bin/sh -e /var/tmp/rpm-tmp.dDG380
+ umask 022
+ cd /builddir/build/BUILD/python-pyradplan-0.8.0-build
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd pyradplan-0.8.0
+ '[' '!' -f /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-modules ']'
+ PATH=/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin
+ PYTHONPATH=/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib64/python3.14/site-packages:/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages
+ _PYTHONSITE=/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib64/python3.14/site-packages:/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages
+ PYTHONDONTWRITEBYTECODE=1
+ /usr/bin/python3 -sP /usr/lib/rpm/redhat/import_all_modules.py -f /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc43.x86_64-pyproject-modules -e 'pyRadPlan.ai.*' -e 'pyRadPlan.gui*' -e 'pyRadPlan.matRad*' -e pyRadPlan.optimization.solvers._ipopt
Check import: pyRadPlan
Check import: pyRadPlan.ai
Check import: pyRadPlan.analysis
Check import: pyRadPlan.biology
Check import: pyRadPlan.biology.models
Check import: pyRadPlan.biology.tissues
Check import: pyRadPlan.core
Check import: pyRadPlan.core.datamodel
Check import: pyRadPlan.core.np2sitk
Check import: pyRadPlan.core.resample
Check import: pyRadPlan.core.xp_utils
Check import: pyRadPlan.core.xp_utils.compat
Check import: pyRadPlan.core.xp_utils.helpers
Check import: pyRadPlan.core.xp_utils.jittable
Check import: pyRadPlan.core.xp_utils.typing
Check import: pyRadPlan.cst
Check import: pyRadPlan.ct
Check import: pyRadPlan.dij
Check import: pyRadPlan.dose
Check import: pyRadPlan.dose.engines
Check import: pyRadPlan.dose.engines.kernels
Check import: pyRadPlan.geometry
Check import: pyRadPlan.geometry.lps
Check import: pyRadPlan.io
Check import: pyRadPlan.io.base
Check import: pyRadPlan.io.dicom
Check import: pyRadPlan.io.matlab
Check import: pyRadPlan.io.npz
Check import: pyRadPlan.io.pickle
Check import: pyRadPlan.io.sitk_based
Check import: pyRadPlan.io.sitk_based.base
Check import: pyRadPlan.io.sitk_based.meta_image
Check import: pyRadPlan.io.sitk_based.nifti
Check import: pyRadPlan.io.sitk_based.nrrd
Check import: pyRadPlan.machines
Check import: pyRadPlan.machines.base
Check import: pyRadPlan.machines.particles
Check import: pyRadPlan.machines.particles.kernel
Check import: pyRadPlan.machines.photons
Check import: pyRadPlan.optimization
Check import: pyRadPlan.optimization.objectives
Check import: pyRadPlan.optimization.problems
Check import: pyRadPlan.optimization.robustness
Check import: pyRadPlan.optimization.solvers
Check import: pyRadPlan.plan
Check import: pyRadPlan.quantities
Check import: pyRadPlan.raytracer
Check import: pyRadPlan.scenarios
Check import: pyRadPlan.stf
Check import: pyRadPlan.stf.generators
Check import: pyRadPlan.util
Check import: pyRadPlan.util.helpers
Check import: pyRadPlan.util.keyboard_listener
Check import: pyRadPlan.util.logging_utils
Check import: pyRadPlan.util.openmp
Check import: pyRadPlan.visualization
+ export ARRAY_API_STRICT_API_VERSION=2023.12
+ ARRAY_API_STRICT_API_VERSION=2023.12
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ PATH=/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin
+ PYTHONPATH=/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib64/python3.14/site-packages:/builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages
+ PYTHONDONTWRITEBYTECODE=1
+ PYTEST_ADDOPTS=' --ignore=/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir'
+ PYTEST_XDIST_AUTO_NUM_WORKERS=4
+ /usr/bin/pytest -p no:cacheprovider test --ignore=test/ai --ignore=test/gui --ignore=test/packaging --ignore=test/test_matlabEngine.py --ignore=test/test_matrad_octave.py --ignore=test/test_octaveEngine.py
============================= test session starts ==============================
platform linux -- Python 3.14.8, pytest-8.3.5, pluggy-1.6.0
rootdir: /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0
configfile: pyproject.toml
plugins: anyio-4.13.0
collected 3418 items

test/analysis/test_dvh.py ...........                                    [  0%]
test/analysis/test_dvh_plot.py .......                                   [  0%]
test/analysis/test_qi.py ............................                    [  1%]
test/analysis/test_units.py ...........                                  [  1%]
test/biology/models/test_bio_model_factory.py ...................        [  2%]
test/biology/models/test_check_tissues.py .............................. [  3%]
...                                                                      [  3%]
test/biology/models/test_const_rbe_bio_model.py ...                      [  3%]
test/biology/models/test_custom_model_extension.py ...F....              [  3%]
test/biology/models/test_empty_bio_model.py ..                           [  3%]
test/biology/models/test_engine_capability.py .......................... [  4%]
.                                                                        [  4%]
test/biology/models/test_evaluator.py ........                           [  4%]
test/biology/models/test_formalism_resolution.py ....................... [  5%]
..                                                                       [  5%]
test/biology/models/test_fragment_species_selection.py .............F    [  5%]
test/biology/models/test_gpu_backend.py sssss                            [  5%]
test/biology/models/test_influence.py .                                  [  5%]
test/biology/models/test_kernel_bio_model.py ...F.......F..F             [  6%]
test/biology/models/test_kernel_let_model.py .........F.                 [  6%]
test/biology/models/test_reference_parameter_domain.py .........F....... [  7%]
.........................................................                [  8%]
test/biology/models/test_tabulated_kernel_model.py .........F            [  9%]
test/biology/models/test_tissue_lookup.py ........FF.F....F..FF          [  9%]
test/biology/models/test_tissue_quick_fixes.py .........                 [ 10%]
test/biology/models/test_zstar_bio_model.py ..FFF.........               [ 10%]
test/biology/tissues/test_tissue_properties.py ......................... [ 11%]
........................................................................ [ 13%]
..                                                                       [ 13%]
test/biology/tissues/test_tissue_reference_parameters.py ............... [ 13%]
..............................                                           [ 14%]
test/core/test_array_resample.py ................sssssssssssssssssssssss [ 15%]
s..............sssssssssssssssssssss..sss..sss....ssssss....ssssss       [ 17%]
test/core/test_base_data.py ........................                     [ 18%]
test/core/test_configurable.py ..................                        [ 18%]
test/core/test_coordinates.py .                                          [ 18%]
test/core/test_datamodel.py ..                                           [ 19%]
test/core/test_np2sitk.py ..........                                     [ 19%]
test/core/test_paths.py ......                                           [ 19%]
test/core/test_progress.py ................                              [ 19%]
test/core/test_resample.py ....                                          [ 20%]
test/core/test_resample_array_api.py ..................FF..FF..FF..FFsss [ 21%]
sssssssssssssssssssssssssssssssssssssssssssss..................sssssssss [ 23%]
ssssssssssssssssss..sss.....Fsss..sss..........sssssssssssssss           [ 25%]
test/core/test_unit_registry.py ..                                       [ 25%]
test/core/xp_utils/compat/test_backend_consistency.py .Fsss.Fsss.Fsss.Fs [ 25%]
ss..sss..sss                                                             [ 25%]
test/core/xp_utils/compat/test_interp1d_torch.py ssssssss                [ 26%]
test/core/xp_utils/compat/test_interpnd.py FFF....sss                    [ 26%]
test/core/xp_utils/compat/test_math_interp1d.py FFFFFFFFFFFFFFFF.        [ 26%]
test/core/xp_utils/compat/test_math_quantile.py .....ss                  [ 27%]
test/core/xp_utils/helpers/test_device_parsing.py ......FsF.ssF.s        [ 27%]
test/core/xp_utils/helpers/test_namespace_conversion.py ...sssssss.sssss [ 28%]
sssssssssssssssssss                                                      [ 28%]
test/core/xp_utils/helpers/test_openblas_warning.py ..........           [ 28%]
test/core/xp_utils/helpers/test_scatter.py ...s                          [ 29%]
test/core/xp_utils/test_backends.py ..s.......                           [ 29%]
test/core/xp_utils/test_initialization.py .....ss..ssss.s....            [ 29%]
test/core/xp_utils/test_interpolation.py ........ssssssssFFFFFFFFsssssss [ 30%]
sssssssss..ssFFssss....ssss....ssssssss....ss..ssss.s...s..........sssss [ 32%]
sssFFFFFFFFssssssssssssssss..                                            [ 33%]
test/core/xp_utils/test_interpolation1d.py ............................. [ 34%]
...............................sssssssssssssssssssssssssssssssssssssssss [ 36%]
sssssssssssssssssssFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF [ 38%]
FFFFFFF...sssFFF.s...ss..                                                [ 39%]
test/core/xp_utils/test_reductions.py ......sssssss.....ssssssssssss.s.s [ 40%]
s.s.ss.s.ss.s.ss.s.sss                                                   [ 41%]
test/cst/test_VOI.py ................................................... [ 42%]
..................................................                       [ 44%]
test/cst/test_cst.py ................................................... [ 45%]
......                                                                   [ 45%]
test/cst/test_cst_4d_ct.py ......                                        [ 45%]
test/ct/test_ct.py ..............................                        [ 46%]
test/ct/test_hlut.py .                                                   [ 46%]
test/data/test_validate_test_data.py .....                               [ 47%]
test/dose/kernels/test_calc_geo_dists.py ssssssssssssss                  [ 47%]
test/dose/test_base_pencilbeam.py ..............                         [ 47%]
test/dose/test_bio_tissue_check.py F.FFF.F.FF.F..FFFFFFF.FFFF            [ 48%]
test/dose/test_default_bio_models.py FFFFFFFFFF..FFF.F                   [ 49%]
test/dose/test_depth_preparation.py .s.s.s.s.s.s.s.s                     [ 49%]
test/dose/test_dose_engine_base.py ...........                           [ 49%]
test/dose/test_engine_reuse.py FFFFFF                                    [ 50%]
test/dose/test_factory.py ....                                           [ 50%]
test/dose/test_fred.py ....s.ssssssss.................                   [ 51%]
test/dose/test_fred_bio.py .................                             [ 51%]
test/dose/test_hongpb.py .FFFFFFFFFFFFFFFFFFFFFFFF.F.FF                  [ 52%]
test/dose/test_hongpb_bio.py .............FFFFFFF.FF.FF.F......F         [ 53%]
test/dose/test_particle_cutoff.py ....................ssssssssssssssssss [ 54%]
ssFFFFFFFFFFFFFFFFFFFF.s...ss......ss.....sss..........sssssss.......... [ 56%]
..F.s...s.....s.                                                         [ 57%]
test/dose/test_particle_indexing.py ........................ssssssssssss [ 58%]
ssssssssssss................FFFFFFFFEEEEEEEEEEEEEEEEEEEEEEEE             [ 60%]
test/dose/test_particle_interpolators.py .sF.sF..FF..                    [ 60%]
test/dose/test_particle_lateral_cutoff.py F                              [ 60%]
test/dose/test_pb_geodist.py ....                                        [ 60%]
test/dose/test_ray_geometry_preparation.py ........ssssssss........sssss [ 61%]
sss....ssss....ssss...ssFFss.s.s..ss..ss.s.s.s.s                         [ 62%]
test/dose/test_scenarios_pencilbeam.py FFFFFFFFFFFFFF.s.sFFFF...FFFFFFFF [ 63%]
FFFFFFFFFFFFFFF.s.s                                                      [ 64%]
test/dose/test_sparse_output.py .........s..s...                         [ 64%]
test/dose/test_svdpb.py FFF                                              [ 64%]
test/dose/test_svdpb_bio.py FFF                                          [ 64%]
test/dose/test_svdpb_bixel.py ............ssssssssssss............ssssss [ 66%]
ssssss.s.s.s.s                                                           [ 66%]
test/dose/test_svdpb_interpolators.py ..ssFF                             [ 66%]
test/dose/test_svdpb_sampling.py .s..s..s..s.....ssss....ssss            [ 67%]
test/dose/test_topas.py ...............                                  [ 67%]
test/dose/test_voxel_preparation.py ...............                      [ 68%]
test/geometry/test_lps.py ............ss                                 [ 68%]
test/io/test_io_binary_import.py ...............                         [ 69%]
test/io/test_io_dicom.py ........FF.....F..FFF.......................... [ 70%]
..                                                                       [ 70%]
test/io/test_io_factory.py .......                                       [ 70%]
test/io/test_io_frontend.py .....................                        [ 71%]
test/io/test_io_matlab.py ..............                                 [ 71%]
test/io/test_io_npz.py ........                                          [ 72%]
test/io/test_io_pickle.py ......                                         [ 72%]
test/io/test_io_sitk.py .............................                    [ 73%]
test/machines/test_beam_focus.py .....                                   [ 73%]
test/machines/test_beam_modifiers.py ...........                         [ 73%]
test/machines/test_bundled_base_data.py ............                     [ 74%]
test/machines/test_energy_spectrum.py ......                             [ 74%]
test/machines/test_kernel_sets.py .................                      [ 74%]
test/machines/test_particle_kernel.py .........                          [ 74%]
test/machines/test_user_machines.py .............                        [ 75%]
test/optimization/robustness/test_robustness_contract.py .....F.....F... [ 75%]
                                                                         [ 75%]
test/optimization/robustness/test_robustness_operators.py F............. [ 76%]
.......FF.F..                                                            [ 76%]
test/optimization/robustness/test_smooth_max.py .......                  [ 76%]
test/optimization/robustness/test_variance_objective.py FF.F.            [ 76%]
test/optimization/test_dose_convention.py .............................. [ 77%]
                                                                         [ 77%]
test/optimization/test_fluence_optiprob.py .......FFF...FFFFs            [ 78%]
test/optimization/test_ipax_solver.py .........sFss.                     [ 78%]
test/optimization/test_ipopt_output.py ssssssss                          [ 78%]
test/optimization/test_ipopt_solver.py ssssssss                          [ 79%]
test/optimization/test_obj_history.py ..F.F..                            [ 79%]
test/optimization/test_objectives.py ............................F.ss... [ 80%]
..........FF.FFsssssssss                                                 [ 81%]
test/optimization/test_opt_info.py .E.E.E.E.....                         [ 81%]
test/optimization/test_problem_factory.py ........                       [ 81%]
test/optimization/test_robust_fluence_optiprob.py ..F.FF...FFF.F         [ 82%]
test/optimization/test_scipy_solver.py .FFFFF..........s                 [ 82%]
test/optimization/test_solver_factory.py ............                    [ 83%]
test/quantities/test_alpha_dose_quantity.py ...                          [ 83%]
test/quantities/test_dose_quantity.py ...                                [ 83%]
test/quantities/test_effect_quantity.py ...                              [ 83%]
test/quantities/test_let_dose_quantity.py ...                            [ 83%]
test/quantities/test_let_quantity.py .....                               [ 83%]
test/quantities/test_multi_scenario_quantities.py ...........F..F..F..F. [ 84%]
....F                                                                    [ 84%]
test/quantities/test_rbe_x_dose_quantity.py .FF......FFFF..F             [ 84%]
test/quantities/test_resolver.py .............                           [ 85%]
test/quantities/test_sqrt_beta_dose_quantity.py ....                     [ 85%]
test/scenarios/test_scenario_dimensions.py ..................            [ 85%]
test/scenarios/test_scenario_gridded.py ................................ [ 86%]
.........                                                                [ 86%]
test/scenarios/test_scenario_models.py .......................           [ 87%]
test/scenarios/test_scenario_random.py .........                         [ 87%]
test/scenarios/test_scenario_set.py .................................... [ 88%]
                                                                         [ 88%]
test/stf/test_beam_angle_presets.py .....                                [ 89%]
test/stf/test_beamlet.py ..........                                      [ 89%]
test/stf/test_stf.py ...FF....                                           [ 89%]
test/stf/test_stf_gen.py ........FF..FF.FF.....................          [ 90%]
test/test_dij.py ....................s                                   [ 91%]
test/test_dij_scenarios.py ......................s.................      [ 92%]
test/test_grid.py ....................                                   [ 93%]
test/test_helpers.py ............                                        [ 93%]
test/test_keyboard_listener.py ..........                                [ 93%]
test/test_lazy_imports.py .s......                                       [ 94%]
test/test_logging_utils.py ............                                  [ 94%]
test/test_machine_model.py ..................                            [ 94%]
test/test_openmp.py .................................s.......            [ 96%]
test/test_plan.py ...........................................            [ 97%]
test/test_raytracer_kernels.py ssss.s                                    [ 97%]
test/test_raytracersiddon.py .FF.FFsFs.....sFs                           [ 98%]
test/test_scenarios.py .........                                         [ 98%]
test/test_settings.py ....................                               [ 98%]
test/visualization/test_plot_multiple_slices.py ......................   [ 99%]
test/visualization/test_plot_slice.py ................                   [100%]

==================================== ERRORS ====================================
_ ERROR at setup of test_particle_support_selection[strict2025-full-False-empty] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-False-empty]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-full-False-none] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-False-none]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-full-False-all] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-False-all]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-full-False-mixed] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-False-mixed]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-full-True-empty] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-True-empty]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-full-True-none] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-True-none]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-full-True-all] __

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-True-all]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-full-True-mixed] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-full-True-mixed]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-False-empty] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-False-empty]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-False-none] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-False-none]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-False-all] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-False-all]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-False-mixed] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-False-mixed]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-True-empty] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-True-empty]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-True-none] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-True-none]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-True-all] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-True-all]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-constant-True-mixed] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-constant-True-mixed]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-False-empty] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-False-empty]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-False-none] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-False-none]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-False-all] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-False-all]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-False-mixed] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-False-mixed]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-True-empty] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-True-empty]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-True-none] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-True-none]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-True-all] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-True-all]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_ ERROR at setup of test_particle_support_selection[strict2025-table-True-mixed] _

request = <SubRequest 'xp' for <Function test_particle_support_selection[strict2025-table-True-mixed]>>

    @pytest.fixture(params=["numpy", "cupy", "strict2023", "strict2025"])
    def xp(request):
        """Exercise native arrays and strict versions without boolean indexing."""
        if request.param.startswith("strict"):
            strict = pytest.importorskip("array_api_strict")
            # Table-cutoff interpolation already requires clip, introduced in 2023.12.
            version = request.param.removeprefix("strict") + ".12"
>           with strict.ArrayAPIStrictFlags(api_version=version, boolean_indexing=False):

test/dose/test_particle_indexing.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: in __enter__
    set_array_api_strict_flags(**self.kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    def set_array_api_strict_flags(
        *,
        api_version=None,
        boolean_indexing=None,
        data_dependent_shapes=None,
        enabled_extensions=None,
    ):
        """
        Set the array-api-strict flags to the specified values.
    
        Flags are global variables that enable or disable array-api-strict
        behaviors.
    
        The flags can also be changed by setting :ref:`environment variables <environment-variables>`.
    
        .. note::
    
           This function is **not** part of the array API standard. It only exists
           in array-api-strict.
    
        Parameters
        ----------
        api_version : str, optional
            The version of the standard to use. Supported versions are:
            ``{supported_versions}``. The default version number is
            ``{default_version!r}``.
    
            Note that 2021.12 is supported, but currently gives the same thing as
            2022.12 (except that the fft extension will be disabled).
    
            2023.12 support is experimental. Some features in 2023.12 may still be
            missing, and it hasn't been fully tested. A future version of
            array-api-strict will change the default version to 2023.12.
    
        boolean_indexing : bool, optional
            Whether indexing by a boolean array is supported. This flag is enabled
            by default. Note that although boolean array indexing does result in
            data-dependent shapes, this flag is independent of the
            `data_dependent_shapes` flag (see below).
    
        data_dependent_shapes : bool, optional
            Whether data-dependent shapes are enabled in array-api-strict.
            This flag is enabled by default. Array libraries that use computation
            graphs may not be able to support functions whose output shapes depend
            on the input data.
    
            The functions that make use of data-dependent shapes, and are therefore
            disabled by setting this flag to False are
    
            - `unique_all()`, `unique_counts()`, `unique_inverse()`, and `unique_values()`.
            - `nonzero()`
            - `repeat()` when the `repeats` argument is an array (requires the
              2023.12 version of the standard)
    
            Note that while boolean indexing is also data-dependent, it is
            controlled by a separate `boolean_indexing` flag (see above).
    
            See
            https://data-apis.org/array-api/latest/design_topics/data_dependent_output_shapes.html
            for more details.
    
        enabled_extensions : list of str, optional
            A list of extensions that are enabled in array-api-strict. The default
            is ``{default_extensions}``. Note that some extensions require a
            minimum version of the standard.
    
    
        Examples
        --------
    
        >>> from array_api_strict import set_array_api_strict_flags
    
        >>> # Set the standard version to 2021.12
        >>> set_array_api_strict_flags(api_version="2021.12")
    
        >>> # Disable data-dependent shapes and boolean indexing
        >>> set_array_api_strict_flags(data_dependent_shapes=False, boolean_indexing=False)
    
        >>> # Enable only the linalg extension (disable the fft extension)
        >>> set_array_api_strict_flags(enabled_extensions=["linalg"])
    
        See Also
        --------
    
        get_array_api_strict_flags: Get the current values of flags.
        reset_array_api_strict_flags: Reset the flags to their default values.
        ArrayAPIStrictFlags: A context manager to temporarily set the flags.
    
        """
        global API_VERSION, BOOLEAN_INDEXING, DATA_DEPENDENT_SHAPES, ENABLED_EXTENSIONS
    
        if api_version is not None:
            if api_version not in supported_versions:
>               raise ValueError(f"Unsupported standard version {api_version!r}")
E               ValueError: Unsupported standard version '2025.12'

/usr/lib/python3.14/site-packages/array_api_strict/_flags.py:142: ValueError
_____________ ERROR at setup of test_opt_info_is_populated[scipy] ______________

request = <SubRequest 'optimized' for <Function test_opt_info_is_populated[scipy]>>
small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    @pytest.fixture
    def optimized(request, small_proton_case):
        """Run a full optimization with ``opt_info`` for the solver named by the test parameter."""
        pln, ct, cst, stf, dij = small_proton_case
        pln.prop_opt = {"solver": request.param, "display": False, "max_iter": 25}
    
        opt_info = {}
>       w = fluence_optimization(ct, cst, stf, dij, pln, opt_info=opt_info)

test/optimization/test_opt_info.py:21: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/_fluence_optimization.py:95: in fluence_optimization
    x, result_info = planning_prob.solve(_ct, _cst, _stf, _dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log setup ------------------------------
WARNING  pyRadPlan.optimization.problems._factory:_factory.py:138 No PlanningProblem specified in Plan. Using first available problem nonlin_fluence.
_____________ ERROR at setup of test_result_info_is_a_dict[scipy] ______________

request = <SubRequest 'optimized' for <Function test_result_info_is_a_dict[scipy]>>
small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    @pytest.fixture
    def optimized(request, small_proton_case):
        """Run a full optimization with ``opt_info`` for the solver named by the test parameter."""
        pln, ct, cst, stf, dij = small_proton_case
        pln.prop_opt = {"solver": request.param, "display": False, "max_iter": 25}
    
        opt_info = {}
>       w = fluence_optimization(ct, cst, stf, dij, pln, opt_info=opt_info)

test/optimization/test_opt_info.py:21: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/_fluence_optimization.py:95: in fluence_optimization
    x, result_info = planning_prob.solve(_ct, _cst, _stf, _dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log setup ------------------------------
WARNING  pyRadPlan.optimization.problems._factory:_factory.py:138 No PlanningProblem specified in Plan. Using first available problem nonlin_fluence.
_________ ERROR at setup of test_num_iter_comes_from_the_solver[scipy] _________

request = <SubRequest 'optimized' for <Function test_num_iter_comes_from_the_solver[scipy]>>
small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    @pytest.fixture
    def optimized(request, small_proton_case):
        """Run a full optimization with ``opt_info`` for the solver named by the test parameter."""
        pln, ct, cst, stf, dij = small_proton_case
        pln.prop_opt = {"solver": request.param, "display": False, "max_iter": 25}
    
        opt_info = {}
>       w = fluence_optimization(ct, cst, stf, dij, pln, opt_info=opt_info)

test/optimization/test_opt_info.py:21: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/_fluence_optimization.py:95: in fluence_optimization
    x, result_info = planning_prob.solve(_ct, _cst, _stf, _dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log setup ------------------------------
WARNING  pyRadPlan.optimization.problems._factory:_factory.py:138 No PlanningProblem specified in Plan. Using first available problem nonlin_fluence.
______ ERROR at setup of test_obj_history_records_every_evaluation[scipy] ______

request = <SubRequest 'optimized' for <Function test_obj_history_records_every_evaluation[scipy]>>
small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    @pytest.fixture
    def optimized(request, small_proton_case):
        """Run a full optimization with ``opt_info`` for the solver named by the test parameter."""
        pln, ct, cst, stf, dij = small_proton_case
        pln.prop_opt = {"solver": request.param, "display": False, "max_iter": 25}
    
        opt_info = {}
>       w = fluence_optimization(ct, cst, stf, dij, pln, opt_info=opt_info)

test/optimization/test_opt_info.py:21: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/_fluence_optimization.py:95: in fluence_optimization
    x, result_info = planning_prob.solve(_ct, _cst, _stf, _dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log setup ------------------------------
WARNING  pyRadPlan.optimization.problems._factory:_factory.py:138 No PlanningProblem specified in Plan. Using first available problem nonlin_fluence.
=================================== FAILURES ===================================
__________________ test_custom_model_runs_in_particle_engine ___________________

registered_custom_model = <class 'test_custom_model_extension._LinearLETModel'>
test_data_protons_raw = {'__globals__': [], '__header__': b'MATLAB 5.0 MAT-file, Platform: GLNXA64, Created on: Fri Apr 11 14:50:38 2025', '__... 'alphaX': 0.1, ...}, {'className': 'DoseObjectives.matRad_SquaredOverdosing', 'parameters': 0, 'penalty': 400}]], ...}

    def test_custom_model_runs_in_particle_engine(registered_custom_model, test_data_protons_raw):
        """The public model/evaluator contract integrates with the particle dose engine."""
        pln = validate_pln(test_data_protons_raw["pln"])
        ct = validate_ct(test_data_protons_raw["ct"])
        cst = validate_cst(test_data_protons_raw["cst"], ct=ct)
        stf = validate_stf(test_data_protons_raw["stf"])
        pln.bio_model = {"model": registered_custom_model.model, "slope": 0.03}
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/biology/models/test_custom_model_extension.py:142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
______________ test_spectrum_tables_are_cached_per_logical_device ______________

    def test_spectrum_tables_are_cached_per_logical_device():
        """Every array_api_strict device shares one DLPack tuple, so it cannot be the cache key."""
        import array_api_compat
        import array_api_strict as xps
    
        class _Machine:
            energies = [100.0]
            pb_kernels = {100.0: _kernel([(1, 1.0), (6, np.nan)])}
    
        machine = _Machine()
        evaluator = TabulatedAlphaBetaModel().evaluator(
            machine,
            {
                "alpha_x": np.asarray([[0.1]]),
                "beta_x": np.asarray([[0.05]]),
                "tissue_class": np.asarray([TissueProperties(alpha_x=0.1, beta_x=0.05)]),
                "tissue_class_ix": np.asarray([[0]]),
            },
        )
    
        # built by hand: xp_utils.to_namespace cannot express array_api_strict's logical devices
        depths = np.asarray(machine.pb_kernels[100.0].depths)
>       devices = [xps.Device("CPU_DEVICE"), xps.Device("device1")]

test/biology/models/test_fragment_species_selection.py:220: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
____________ test_KernelBasedLQModel_evaluate_mixed_tissue_classes _____________

machine = <test_kernel_bio_model._Machine object at 0x7f51adc10550>
voxel_params = {'alpha_x': array([[0.1],
       [0.5],
       [0. ]]), 'beta_x': array([[0.05],
       [0.05],
       [0.  ]]), 'tiss...(cell_line=None, model='Generic', alpha_x=0.5, beta_x=0.05)],
      dtype=object), 'tissue_class_ix': array([0, 1, 0])}

    def test_KernelBasedLQModel_evaluate_mixed_tissue_classes(machine, voxel_params):
        evaluator = KernelBasedLQModel().evaluator(machine, voxel_params)
        # interpolated kernels: (n_tissue_classes, n_voxels)
        kernels = {
            "alpha": xp.asarray([[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 2.0]]),
            "beta": xp.asarray([[4.0, 4.0, 4.0, 4.0], [5.0, 5.0, 5.0, 5.0]]),
        }
>       result = evaluator.evaluate(
            BioEvaluationContext(
                {
                    "alpha_x": xp.asarray([0.1, 0.5, 0.1, 0.0]),
                    "beta_x": xp.asarray([0.05, 0.05, 0.05, 0.0]),
                    "tissue_class_ix": xp.asarray([0, 1, 0, 0]),
                    **kernels,
                }
            )
        )

test/biology/models/test_kernel_bio_model.py:97: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:322: in _evaluate
    alpha, beta = self.model.alpha_beta_from_kernel_rows(self._gather_rows(context))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:317: in _gather_rows
    rows = self.lookup.gather(context, self.kernel_fields)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 1., 1., 1.],
       [2., 2., 2., 2.]], dtype=array_api_strict.float64)
key = (Array([0, 1, 0, 0], dtype=array_api_strict.int64), Array([0, 1, 2, 3], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______ test_declared_tissue_classes_are_mapped_onto_kernel_rows_by_value _______

voxel_params = {'alpha_x': array([[0.1],
       [0.5],
       [0. ]]), 'beta_x': array([[0.05],
       [0.05],
       [0.  ]]), 'tiss...(cell_line=None, model='Generic', alpha_x=0.5, beta_x=0.05)],
      dtype=object), 'tissue_class_ix': array([0, 1, 0])}

    def test_declared_tissue_classes_are_mapped_onto_kernel_rows_by_value(voxel_params):
        """A tissueParameters order differing from the kernel order still selects the own row."""
        machine = _Machine([0.1, 0.5], [0.05, 0.05])
        machine.tissue_properties = list(reversed(machine.tissue_properties))
        model = KernelBasedLQModel()
    
        classes = model.kernel_tissue_classes(machine)
        assert [tissue.alpha_x for tissue in classes] == [0.1, 0.5]
    
        # CST tissue 0 has alpha_x = 0.1 (kernel row 0), tissue 1 has alpha_x = 0.5 (row 1)
        evaluator = model.evaluator(machine, voxel_params)
>       assert _evaluate_alpha(evaluator, [0, 1]) == [1.0, 2.0]

test/biology/models/test_kernel_bio_model.py:225: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/biology/models/test_kernel_bio_model.py:200: in _evaluate_alpha
    result = evaluator.evaluate(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:322: in _evaluate
    alpha, beta = self.model.alpha_beta_from_kernel_rows(self._gather_rows(context))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:317: in _gather_rows
    rows = self.lookup.gather(context, self.kernel_fields)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 1.],
       [2., 2.]], dtype=array_api_strict.float64)
key = (Array([0, 1], dtype=array_api_strict.int64), Array([0, 1], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
________ test_machine_without_tissue_parameters_uses_the_kernel_classes ________

voxel_params = {'alpha_x': array([[0.1],
       [0.5],
       [0. ]]), 'beta_x': array([[0.05],
       [0.05],
       [0.  ]]), 'tiss...(cell_line=None, model='Generic', alpha_x=0.5, beta_x=0.05)],
      dtype=object), 'tissue_class_ix': array([0, 1, 0])}

    def test_machine_without_tissue_parameters_uses_the_kernel_classes(voxel_params):
        """Without tissueParameters meta data the classes come from the kernels themselves."""
        carbon = load_from_name("carbon", "Generic")
        data = {
            name: getattr(carbon, name)
            for name in carbon.model_fields_set
            if name != "tissue_properties"
        }
        machine = IonAccelerator.model_validate(data)
        assert "tissue_properties" not in machine.model_fields_set
        assert len(machine.tissue_properties) == 1
    
        model = KernelBasedLQModel()
        classes = model.kernel_tissue_classes(machine)
        assert [(t.alpha_x, t.beta_x) for t in classes] == [(0.1, 0.05), (0.5, 0.05)]
    
        # an alpha_x = 0.5 structure is served by the second kernel row
        evaluator = model.evaluator(machine, voxel_params)
>       assert _evaluate_alpha(evaluator, [0, 1]) == [1.0, 2.0]

test/biology/models/test_kernel_bio_model.py:265: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/biology/models/test_kernel_bio_model.py:200: in _evaluate_alpha
    result = evaluator.evaluate(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:322: in _evaluate
    alpha, beta = self.model.alpha_beta_from_kernel_rows(self._gather_rows(context))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:317: in _gather_rows
    rows = self.lookup.gather(context, self.kernel_fields)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 1.],
       [2., 2.]], dtype=array_api_strict.float64)
key = (Array([0, 1], dtype=array_api_strict.int64), Array([0, 1], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
________________________ test_LinearScaling_alpha_beta _________________________

sample_parameters = (Array([0.1, 0.1, 0.1], dtype=array_api_strict.float64), Array([0.05, 0.05, 0.05], dtype=array_api_strict.float64))

    def test_LinearScaling_alpha_beta(sample_parameters):
        alpha_x, beta_x = sample_parameters
        lam, corr, upper, lower = 0.008, 0.5, 30.0, 0.3
        # below, inside and above the LET thresholds
        kernels = {"let": xp.asarray([0.1, 5.0, 40.0])}
        let_clipped = np.asarray([lower, 5.0, upper])
        alpha_0 = np.asarray(alpha_x) - lam * corr
        expected_alpha = alpha_0 + lam * let_clipped
>       alpha, beta = _evaluate(LinearScaling(), alpha_x, beta_x, kernels)

test/biology/models/test_kernel_let_model.py:119: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/biology/models/test_kernel_let_model.py:29: in _evaluate
    result = evaluator.evaluate(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:297: in _evaluate
    alpha, beta = self.model.alpha_beta(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/let_based_lq_models.py:75: in alpha_beta
    rbe_min, rbe_max = self.rbe_min_max(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/let_based_lq_models.py:256: in rbe_min_max
    rbe_max = xp.where(alpha_x > 0, alpha / xp.where(alpha_x > 0, alpha_x, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([0.1, 0.1, 0.1], dtype=array_api_strict.float64), x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
___ test_outside_structure_voxels_evaluate_to_zero_array_api[LinearScaling] ____

model_cls = <class 'pyRadPlan.biology.models._models.let_based_lq_models.LinearScaling'>

    @pytest.mark.parametrize("model_cls", ALL_MODELS)
    def test_outside_structure_voxels_evaluate_to_zero_array_api(model_cls):
        """The masking uses only array-API operations, so it works on any backend."""
>       influence = _influence(model_cls(), [0.0, 0.1], [0.0, 0.05], [1.0, 1.0], [3.0, 3.0], xp=xps)

test/biology/models/test_reference_parameter_domain.py:87: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/biology/models/test_reference_parameter_domain.py:33: in _influence
    return evaluator.evaluate_influence(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:235: in evaluate_influence
    self._evaluate_influence(context),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:274: in _evaluate_influence
    result = self.evaluate(context)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:297: in _evaluate
    alpha, beta = self.model.alpha_beta(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/let_based_lq_models.py:75: in alpha_beta
    rbe_min, rbe_max = self.rbe_min_max(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_models/let_based_lq_models.py:256: in rbe_min_max
    rbe_max = xp.where(alpha_x > 0, alpha / xp.where(alpha_x > 0, alpha_x, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True], dtype=array_api_strict.bool)
x1 = Array([1. , 0.1], dtype=array_api_strict.float64), x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
____________________ test_TabulatedAlphaBetaModel_evaluate _____________________

machine = <test_tabulated_kernel_model._Machine object at 0x7f51adb9b130>
voxel_params = {'alpha_x': array([[0.1],
       [0. ]]), 'beta_x': array([[0.05],
       [0.  ]]), 'tissue_class': array([TissueProperties(cell_line=None, model='Generic', alpha_x=0.1, beta_x=0.05)],
      dtype=object), 'tissue_class_ix': array([0, 0])}

    def test_TabulatedAlphaBetaModel_evaluate(machine, voxel_params):
        evaluator = TabulatedAlphaBetaModel().evaluator(machine, voxel_params)
        # interpolated kernels: (n_tissue_classes, n_voxels)
        kernels = {
            "alpha": xp.asarray([[1.0, 2.0, 3.0, 4.0]]),
            "sqrt_beta": xp.asarray([[1.0, 2.0, 1.0, 2.0]]),
        }
>       result = evaluator.evaluate(
            BioEvaluationContext(
                {
                    "alpha_x": xp.asarray([0.1, 0.1, 0.0, 0.1]),
                    "beta_x": xp.asarray([0.05, 0.05, 0.0, 0.05]),
                    "tissue_class_ix": xp.asarray([0, 0, 0, 0]),
                    **kernels,
                }
            )
        )

test/biology/models/test_tabulated_kernel_model.py:245: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:322: in _evaluate
    alpha, beta = self.model.alpha_beta_from_kernel_rows(self._gather_rows(context))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:317: in _gather_rows
    rows = self.lookup.gather(context, self.kernel_fields)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 2., 3., 4.]], dtype=array_api_strict.float64)
key = (Array([0, 0, 0, 0], dtype=array_api_strict.int64), Array([0, 1, 2, 3], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______________________________ test_exact_gather _______________________________

lookup = <pyRadPlan.biology.models._tissue_lookup.ExactClassLookup object at 0x7f51b0182a50>

    def test_exact_gather(lookup):
        context = {
            "alpha_x": xp.asarray([0.1, 0.5, 0.1, 0.0]),
            "beta_x": xp.asarray([0.05, 0.05, 0.05, 0.0]),
            "tissue_class_ix": xp.asarray([0, 1, 0, 0]),
            # interpolated kernels: (n_classes, n_voxels)
            "alpha": xp.asarray([[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 2.0]]),
            "beta": xp.asarray([[4.0, 4.0, 4.0, 4.0], [5.0, 5.0, 5.0, 5.0]]),
            "let": xp.asarray([9.0, 9.0, 9.0, 9.0]),  # not requested, must be ignored
        }
>       rows = lookup.gather(context, ["alpha", "beta"])

test/biology/models/test_tissue_lookup.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 1., 1., 1.],
       [2., 2., 2., 2.]], dtype=array_api_strict.float64)
key = (Array([0, 1, 0, 0], dtype=array_api_strict.int64), Array([0, 1, 2, 3], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__________ test_exact_gather_takes_one_dimensional_kernels_per_voxel ___________

lookup = <pyRadPlan.biology.models._tissue_lookup.ExactClassLookup object at 0x7f51adcf7bd0>

    def test_exact_gather_takes_one_dimensional_kernels_per_voxel(lookup):
        """A kernel that is not tabulated per tissue class is used as-is."""
        context = {
            "alpha_x": xp.asarray([0.1, 0.5]),
            "tissue_class_ix": xp.asarray([0, 1]),
            "zs": xp.asarray([7.0, 8.0]),
        }
>       rows = lookup.gather(context, ["zs"])

test/biology/models/test_tissue_lookup.py:111: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:231: in gather
    name: context[name][voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([7., 8.], dtype=array_api_strict.float64)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________ test_exact_gather_skips_the_check_once_the_grid_is_validated _________

lookup = <pyRadPlan.biology.models._tissue_lookup.ExactClassLookup object at 0x7f51a322d450>

    def test_exact_gather_skips_the_check_once_the_grid_is_validated(lookup):
        """Per-bixel indices are a subset of the validated grid, so the check is redundant."""
        lookup.validate({"tissue_class_ix": np.asarray([[1], [0]])})
>       rows = lookup.gather(
            {
                "alpha_x": xp.asarray([0.5, 0.1]),
                "tissue_class_ix": xp.asarray([1, 0]),
                "alpha": xp.asarray([[1.0, 1.0], [2.0, 2.0]]),
            },
            ["alpha"],
        )

test/biology/models/test_tissue_lookup.py:131: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 1.],
       [2., 2.]], dtype=array_api_strict.float64)
key = (Array([1, 0], dtype=array_api_strict.int64), Array([0, 1], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
________ test_gather_tissue_parameters_matches_the_per_bixel_conversion ________

    def test_gather_tissue_parameters_matches_the_per_bixel_conversion():
        """The cached class parameters give identical results on every device."""
        classes = [
            MKMTissueProperties(r_nucleus=3.9, r_domain=0.32, alpha_0=0.1, beta_0=0.05),
            MKMTissueProperties(r_nucleus=3.9, r_domain=0.32, alpha_0=0.2, beta_0=0.06),
        ]
        lookup = ExactClassLookup(classes, [0, 1])
>       for device in (None, xp.Device("device1")):

test/biology/models/test_tissue_lookup.py:250: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
______________ test_reference_cache_distinguishes_logical_devices ______________

lookup = <pyRadPlan.biology.models._tissue_lookup.ExactClassLookup object at 0x7f51ae8b7c50>

    def test_reference_cache_distinguishes_logical_devices(lookup):
        """Every array_api_strict device shares the DLPack tuple (1, 0), so it cannot be the key."""
>       devices = [xp.Device("CPU_DEVICE"), xp.Device("device1")]

test/biology/models/test_tissue_lookup.py:288: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
______________ test_gather_works_on_a_non_default_logical_device _______________

lookup = <pyRadPlan.biology.models._tissue_lookup.ExactClassLookup object at 0x7f51ae8b4450>

    def test_gather_works_on_a_non_default_logical_device(lookup):
        """A cached reference from another device must not leak into this one."""
        lookup.class_index(xp.asarray([0]))  # warm the CPU_DEVICE entry
    
>       device = xp.Device("device1")

test/biology/models/test_tissue_lookup.py:302: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_______ test_MKM_evaluate_combines_kernel_rows_with_the_class_parameters _______

machine = <test_zstar_bio_model.machine.<locals>._Machine object at 0x7f51adc042f0>
voxel_params = {'alpha_x': array([[0.1],
       [0.2]]), 'beta_x': array([[0.05],
       [0.06]]), 'tissue_class': array([MKMTissuePr...0.2, beta_0=0.06, reference_radiation='xray_200kV')],
      dtype=object), 'tissue_class_ix': array([[0],
       [1]])}

    def test_MKM_evaluate_combines_kernel_rows_with_the_class_parameters(machine, voxel_params):
        """alpha = alpha_0 + beta_0 * zs and beta = beta_0, both of the voxel's own class."""
        evaluator = MKM().evaluator(machine, voxel_params)
    
>       result = evaluator.evaluate(
            BioEvaluationContext(
                {
                    "alpha_x": xp.asarray([0.1, 0.2, 0.2]),
                    "beta_x": xp.asarray([0.05, 0.06, 0.06]),
                    "tissue_class_ix": xp.asarray([0, 1, 1]),
                    # interpolated zs kernels: (n_tissue_classes, n_voxels)
                    "zs": xp.asarray([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]),
                }
            )
        )

test/biology/models/test_zstar_bio_model.py:74: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:322: in _evaluate
    alpha, beta = self.model.alpha_beta_from_kernel_rows(self._gather_rows(context))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:317: in _gather_rows
    rows = self.lookup.gather(context, self.kernel_fields)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 1., 1.],
       [2., 2., 2.]], dtype=array_api_strict.float64)
key = (Array([0, 1, 1], dtype=array_api_strict.int64), Array([0, 1, 2], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
____________________ test_mkm_copies_no_host_data_per_bixel ____________________

machine = <test_zstar_bio_model.machine.<locals>._Machine object at 0x7f51adc04c20>
voxel_params = {'alpha_x': array([[0.1],
       [0.2]]), 'beta_x': array([[0.05],
       [0.06]]), 'tissue_class': array([MKMTissuePr...0.2, beta_0=0.06, reference_radiation='xray_200kV')],
      dtype=object), 'tissue_class_ix': array([[0],
       [1]])}
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51a3234160>

    def test_mkm_copies_no_host_data_per_bixel(machine, voxel_params, monkeypatch):
        """Evaluating further bixels only indexes arrays already on the device."""
        evaluator = MKM().evaluator(machine, voxel_params)
        rng = np.random.default_rng(1)
        bixels = [
            BioEvaluationContext(
                {
                    "alpha_x": xp.asarray([0.1, 0.2, 0.2]),
                    "beta_x": xp.asarray([0.05, 0.06, 0.06]),
                    "tissue_class_ix": xp.asarray(rng.integers(0, 2, 3)),
                    "physical_dose": xp.asarray(rng.random(3)),
                    "zs": xp.asarray(rng.random((2, 3))),
                }
            )
            for _ in range(10)
        ]
        host_conversions = []
        asarray = xp.asarray
    
        def counting(obj, *args, **kwargs):
            if isinstance(obj, (list, tuple, np.ndarray)):
                host_conversions.append(obj)
            return asarray(obj, *args, **kwargs)
    
        monkeypatch.setattr(xp, "asarray", counting)
>       evaluator.evaluate_influence(bixels[0])

test/biology/models/test_zstar_bio_model.py:117: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:235: in evaluate_influence
    self._evaluate_influence(context),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:274: in _evaluate_influence
    result = self.evaluate(context)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:322: in _evaluate
    alpha, beta = self.model.alpha_beta_from_kernel_rows(self._gather_rows(context))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:317: in _gather_rows
    rows = self.lookup.gather(context, self.kernel_fields)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[0.42332645, 0.82770259,
        0.40919914],
       [0.54959369, 0.02755911,
        0.75351311]], dtype=array_api_strict.float64)
key = (Array([0, 1, 1], dtype=array_api_strict.int64), Array([0, 1, 2], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
____ test_MKM_evaluate_influence_weights_the_class_parameters_with_the_dose ____

machine = <test_zstar_bio_model.machine.<locals>._Machine object at 0x7f51adc042f0>
voxel_params = {'alpha_x': array([[0.1],
       [0.2]]), 'beta_x': array([[0.05],
       [0.06]]), 'tissue_class': array([MKMTissuePr...0.2, beta_0=0.06, reference_radiation='xray_200kV')],
      dtype=object), 'tissue_class_ix': array([[0],
       [1]])}

    def test_MKM_evaluate_influence_weights_the_class_parameters_with_the_dose(machine, voxel_params):
        evaluator = MKM().evaluator(machine, voxel_params)
        physical_dose = xp.asarray([2.0, 4.0])
    
>       result = evaluator.evaluate_influence(
            BioEvaluationContext(
                {
                    "alpha_x": xp.asarray([0.1, 0.2]),
                    "beta_x": xp.asarray([0.05, 0.06]),
                    "tissue_class_ix": xp.asarray([0, 1]),
                    "physical_dose": physical_dose,
                    "zs": xp.asarray([[1.0, 1.0], [2.0, 2.0]]),
                }
            )
        )

test/biology/models/test_zstar_bio_model.py:130: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:235: in evaluate_influence
    self._evaluate_influence(context),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:274: in _evaluate_influence
    result = self.evaluate(context)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:223: in evaluate
    self._evaluate(context), self.model.output_quantities, "output quantities"
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:322: in _evaluate
    alpha, beta = self.model.alpha_beta_from_kernel_rows(self._gather_rows(context))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_evaluator.py:317: in _gather_rows
    rows = self.lookup.gather(context, self.kernel_fields)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/biology/models/_tissue_lookup.py:233: in gather
    else context[name][row_ix, voxel_ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([[1., 1.],
       [2., 2.]], dtype=array_api_strict.float64)
key = (Array([0, 1], dtype=array_api_strict.int64), Array([0, 1], dtype=array_api_strict.int64))

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______ test_enums_extrapolation_and_backend[array_api_strict-nearest-23] _______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 23, extrapolate = 'nearest'

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_______ test_enums_extrapolation_and_backend[array_api_strict-nearest-4] _______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 4, extrapolate = 'nearest'

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_______ test_enums_extrapolation_and_backend[array_api_strict--3.25-23] ________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 23, extrapolate = -3.25

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_enums_extrapolation_and_backend[array_api_strict--3.25-4] ________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 4, extrapolate = -3.25

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_______ test_enums_extrapolation_and_backend[array_api_strict-False-23] ________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 23, extrapolate = False

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_enums_extrapolation_and_backend[array_api_strict-False-4] ________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 4, extrapolate = False

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_enums_extrapolation_and_backend[array_api_strict-True-23] ________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 23, extrapolate = True

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_enums_extrapolation_and_backend[array_api_strict-True-4] _________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
method = 4, extrapolate = True

    @pytest.mark.parametrize(
        "method", [sitk.sitkLinear, sitk.sitkNearestNeighbor, sitk.sitkBSpline, sitk.sitkGaussian]
    )
    @pytest.mark.parametrize("extrapolate", ["nearest", -3.25, False, True])
    def test_enums_extrapolation_and_backend(xp, method, extrapolate):
        """All enum requests retain native semantics, including host fallback modes."""
        values, image, target = images()
        expected = sitk.GetArrayFromImage(
            resample_image(image, interpolator=method, target_grid=target, extrapolate=extrapolate)
        )
        input_array = xp.asarray(values)
>       actual = resample_array(
            input_array, image, interpolator=method, target_grid=target, extrapolate=extrapolate
        )

test/core/test_resample_array_api.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
__________ test_small_bspline_fallback_is_preserved[array_api_strict] __________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>

    def test_small_bspline_fallback_is_preserved(xp):
        """The existing native small-image fallback still works on device inputs."""
        values = np.arange(27, dtype=np.float32).reshape(3, 3, 3)
        image = sitk.GetImageFromArray(values)
        grid = Grid.from_sitk_image(image).resample(dict(x=0.8, y=0.7, z=0.9))
>       actual = resample_array(xp.asarray(values), image, target_grid=grid)

test/core/test_resample_array_api.py:152: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/resample.py:187: in resample_array
    return to_namespace(xp, result, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________________ test_interp1d_2d_boundaries[array_api_strict] _________________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>

    @pytest.mark.parametrize("xp", BACKENDS)
    def test_interp1d_2d_boundaries(xp):
        """Use the same 2D boundary semantics on every backend."""
        x = xp.asarray([0.0, 1.0, 2.0], dtype=xp.float32)
        y = xp.asarray([[0.0, 1.0, 2.0], [10.0, 11.0, 12.0]], dtype=xp.float32)
        xq = xp.asarray([-1.0, 0.5, 3.0], dtype=xp.float32)
    
>       default_result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_backend_consistency.py:60: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_____________ test_interp1d_container_boundaries[array_api_strict] _____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>

    @pytest.mark.parametrize("xp", BACKENDS)
    def test_interp1d_container_boundaries(xp):
        """Allow scalar but reject row-wise boundaries for y containers."""
        x = xp.asarray([0.0, 1.0, 2.0], dtype=xp.float32)
        xq = xp.asarray([-1.0, 0.5, 3.0], dtype=xp.float32)
        y1 = xp.asarray([0.0, 1.0, 2.0], dtype=xp.float32)
        y2 = xp.asarray([10.0, 11.0, 12.0], dtype=xp.float32)
        row_left = xp.asarray([-5.0, -6.0], dtype=xp.float32)
    
        containers = ([y1, y2], (y1, y2), {"first": y1, "second": y2})
        for y in containers:
>           result = interp1d(xq, x, y, left=-5.0, right=99.0)

test/core/xp_utils/compat/test_backend_consistency.py:86: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:408: in interp1d
    res = [_interpolation(xq, x, y_arr, left, right) for y_arr in y]
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________ test_interp1d_multidimensional_queries[array_api_strict] ___________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>

    @pytest.mark.parametrize("xp", BACKENDS)
    def test_interp1d_multidimensional_queries(xp):
        """Preserve multidimensional query shapes for 2D y arrays."""
        x = xp.asarray([0.0, 1.0, 2.0], dtype=xp.float32)
        y = xp.asarray([[0.0, 1.0, 2.0], [10.0, 11.0, 12.0]], dtype=xp.float32)
        xq = xp.asarray([[-1.0, 0.5], [1.5, 3.0]], dtype=xp.float32)
        left = xp.asarray([-5.0, -6.0], dtype=xp.float32)
        right = xp.asarray([98.0, 99.0], dtype=xp.float32)
    
>       result = interp1d(xq, x, y, left=left, right=right)

test/core/xp_utils/compat/test_backend_consistency.py:108: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________ test_interpnd_bounds_and_multiple_queries[array_api_strict] __________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>

    @pytest.mark.parametrize("xp", BACKENDS)
    def test_interpnd_bounds_and_multiple_queries(xp):
        """Interpolate multiple points and apply the requested bounds behavior."""
        gx = xp.asarray([0.0, 1.0, 3.0], dtype=xp.float32)
        gy = xp.asarray([0.0, 2.0, 3.0], dtype=xp.float32)
        y = xp.asarray(
            [[0.0, 20.0, 30.0], [1.0, 21.0, 31.0], [3.0, 23.0, 33.0]],
            dtype=xp.float32,
        )
        xq = xp.asarray([[0.5, 1.0], [-1.0, 1.0], [2.0, 4.0]], dtype=xp.float32)
    
>       result = interpnd(xq, (gx, gy), y)

test/core/xp_utils/compat/test_backend_consistency.py:125: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:230: in interpnd
    return ArrayBackendGridInterpolator(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 3.], dtype=array_api_strict.float32)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______________________ test_interpnd_generic_returns[2d] _______________________

xq = Array([[0.25, 0.5 ]], dtype=array_api_strict.float64)
x = (Array([0., 1.], dtype=array_api_strict.float64), Array([0., 1.], dtype=array_api_strict.float64))
y = Array([[ 0., 10.],
       [ 1., 11.]], dtype=array_api_strict.float64)
expected = Array([5.25], dtype=array_api_strict.float64)

    @pytest.mark.parametrize(
        "xq, x, y, expected",
        [_interp_input_2d(strictxp), _interp_input_3d(strictxp)],
        ids=["2d", "3d"],
    )
    def test_interpnd_generic_returns(xq, x, y, expected):
>       result = interpnd(xq, x, y)

test/core/xp_utils/compat/test_interpnd.py:113: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:230: in interpnd
    return ArrayBackendGridInterpolator(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1.], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______________________ test_interpnd_generic_returns[3d] _______________________

xq = Array([[0.25, 0.5 , 0.75]], dtype=array_api_strict.float64)
x = (Array([0., 1.], dtype=array_api_strict.float64), Array([0., 1.], dtype=array_api_strict.float64), Array([0., 1.], dtype=array_api_strict.float64))
y = Array([[[  0., 100.],
        [ 10., 110.]],

       [[  1., 101.],
        [ 11., 111.]]], dtype=array_api_strict.float64)
expected = Array([80.25], dtype=array_api_strict.float64)

    @pytest.mark.parametrize(
        "xq, x, y, expected",
        [_interp_input_2d(strictxp), _interp_input_3d(strictxp)],
        ids=["2d", "3d"],
    )
    def test_interpnd_generic_returns(xq, x, y, expected):
>       result = interpnd(xq, x, y)

test/core/xp_utils/compat/test_interpnd.py:113: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:230: in interpnd
    return ArrayBackendGridInterpolator(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1.], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________ test_interpnd_generic_2d_handles_descending_axes _______________

    def test_interpnd_generic_2d_handles_descending_axes():
        xq, _, __, expected = _interp_input_2d(strictxp)
        gx = strictxp.asarray([1.0, 0.0], dtype=strictxp.float64)
        gy = strictxp.asarray([1.0, 0.0], dtype=strictxp.float64)
        x = (gx, gy)
        y = strictxp.asarray(
            [
                [11.0, 1.0],  # x = 1
                [10.0, 0.0],  # x = 0
            ],
            dtype=strictxp.float64,
        )
    
>       result = interpnd(xq, x, y)

test/core/xp_utils/compat/test_interpnd.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:230: in interpnd
    return ArrayBackendGridInterpolator(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1.], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________________________ test_interp1d_basic[dtype0] __________________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_basic(dtype):
        x = xp.asarray([0.0, 1.0, 2.0, 3.0], dtype=dtype)
        y = xp.asarray([0.0, 1.0, 4.0, 9.0], dtype=dtype)
        xq = xp.asarray([0.5, 1.5, 2.5], dtype=dtype)
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2., 3.], dtype=array_api_strict.float64)
key = Array([0, 1, 2], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 2] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________________________ test_interp1d_basic[dtype1] __________________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_basic(dtype):
        x = xp.asarray([0.0, 1.0, 2.0, 3.0], dtype=dtype)
        y = xp.asarray([0.0, 1.0, 4.0, 9.0], dtype=dtype)
        xq = xp.asarray([0.5, 1.5, 2.5], dtype=dtype)
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2., 3.], dtype=array_api_strict.float32)
key = Array([0, 1, 2], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 2] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________________ test_interp1d_on_points[dtype0] ________________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_on_points(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y = xp.asarray([10.0, 20.0, 30.0], dtype=dtype)
        xq = x
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:26: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________________ test_interp1d_on_points[dtype1] ________________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_on_points(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y = xp.asarray([10.0, 20.0, 30.0], dtype=dtype)
        xq = x
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:26: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
________________________ test_interp1d_2darray[dtype0] _________________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_2darray(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y = xp.asarray([[0.0, 1.0, 4.0], [0.0, 1.0, 4.0]], dtype=dtype)
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:36: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float64)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
________________________ test_interp1d_2darray[dtype1] _________________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_2darray(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y = xp.asarray([[0.0, 1.0, 4.0], [0.0, 1.0, 4.0]], dtype=dtype)
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:36: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________ test_interp1d_multiple_y_stack_sequence[dtype0] ________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_multiple_y_stack_sequence(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y1 = xp.asarray([1.0, 2.0, 3.0], dtype=dtype)
        y2 = xp.asarray([10.0, 20.0, 30.0], dtype=dtype)
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, [y1, y2], stack=True)

test/core/xp_utils/compat/test_math_interp1d.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float64)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________ test_interp1d_multiple_y_stack_sequence[dtype1] ________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_multiple_y_stack_sequence(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y1 = xp.asarray([1.0, 2.0, 3.0], dtype=dtype)
        y2 = xp.asarray([10.0, 20.0, 30.0], dtype=dtype)
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, [y1, y2], stack=True)

test/core/xp_utils/compat/test_math_interp1d.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________________ test_interp1_multiple_y_sequence[dtype0] ___________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1_multiple_y_sequence(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y1 = xp.asarray([1.0, 2.0, 3.0], dtype=dtype)
        y2 = xp.asarray([10.0, 20.0, 30.0], dtype=dtype)
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, [y1, y2], stack=False)

test/core/xp_utils/compat/test_math_interp1d.py:76: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:408: in interp1d
    res = [_interpolation(xq, x, y_arr, left, right) for y_arr in y]
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float64)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________________ test_interp1_multiple_y_sequence[dtype1] ___________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1_multiple_y_sequence(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y1 = xp.asarray([1.0, 2.0, 3.0], dtype=dtype)
        y2 = xp.asarray([10.0, 20.0, 30.0], dtype=dtype)
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, [y1, y2], stack=False)

test/core/xp_utils/compat/test_math_interp1d.py:76: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:408: in interp1d
    res = [_interpolation(xq, x, y_arr, left, right) for y_arr in y]
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__________________ test_interp1d_mixed_1d_2d_sequence[dtype0] __________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_mixed_1d_2d_sequence(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y1 = xp.asarray([1.0, 2.0, 3.0], dtype=dtype)  # 1D
        y2 = xp.asarray([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], dtype=dtype)  # 2D
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
        # stack=False: should return a list, with shapes matching y1 and y2
>       result = interp1d(xq, x, [y1, y2], stack=False)

test/core/xp_utils/compat/test_math_interp1d.py:101: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:408: in interp1d
    res = [_interpolation(xq, x, y_arr, left, right) for y_arr in y]
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float64)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__________________ test_interp1d_mixed_1d_2d_sequence[dtype1] __________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_mixed_1d_2d_sequence(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y1 = xp.asarray([1.0, 2.0, 3.0], dtype=dtype)  # 1D
        y2 = xp.asarray([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], dtype=dtype)  # 2D
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
        # stack=False: should return a list, with shapes matching y1 and y2
>       result = interp1d(xq, x, [y1, y2], stack=False)

test/core/xp_utils/compat/test_math_interp1d.py:101: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:408: in interp1d
    res = [_interpolation(xq, x, y_arr, left, right) for y_arr in y]
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________________ test_interp1d_dict_input[dtype0] _______________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_dict_input(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y_dict = {
            "a": xp.asarray([1.0, 2.0, 3.0], dtype=dtype),
            "b": xp.asarray([10.0, 20.0, 30.0], dtype=dtype),
        }
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, y_dict)

test/core/xp_utils/compat/test_math_interp1d.py:130: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float64)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________________ test_interp1d_dict_input[dtype1] _______________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_dict_input(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y_dict = {
            "a": xp.asarray([1.0, 2.0, 3.0], dtype=dtype),
            "b": xp.asarray([10.0, 20.0, 30.0], dtype=dtype),
        }
        xq = xp.asarray([0.5, 1.5], dtype=dtype)
    
>       result = interp1d(xq, x, y_dict)

test/core/xp_utils/compat/test_math_interp1d.py:130: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_____________________ test_interp1d_out_of_bounds[dtype0] ______________________

dtype = array_api_strict.float64

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_out_of_bounds(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y = xp.asarray([0.0, 1.0, 4.0], dtype=dtype)
        xq = xp.asarray([-1.0, 0.0, 2.0, 3.0], dtype=dtype)
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:154: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_____________________ test_interp1d_out_of_bounds[dtype1] ______________________

dtype = array_api_strict.float32

    @pytest.mark.parametrize("dtype", [xp.float64, xp.float32])
    def test_interp1d_out_of_bounds(dtype):
        x = xp.asarray([0.0, 1.0, 2.0], dtype=dtype)
        y = xp.asarray([0.0, 1.0, 4.0], dtype=dtype)
        xq = xp.asarray([-1.0, 0.0, 2.0, 3.0], dtype=dtype)
    
>       result = interp1d(xq, x, y)

test/core/xp_utils/compat/test_math_interp1d.py:154: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 2.], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__________________ test_parse_array_api_strict_default_device __________________

    def test_parse_array_api_strict_default_device():
        """The strict Device object must map to CPU (matched by type, not by repr)."""
        device = xps.asarray([1.0]).device
>       assert _parse_device_to_dlpack(device) == (DLPACK_CPU, 0)

test/core/xp_utils/helpers/test_device_parsing.py:44: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
___________ test_device_repr_containing_cpu_is_not_classified_as_cpu ___________

    def test_device_repr_containing_cpu_is_not_classified_as_cpu():
        """A device whose repr merely mentions "cpu" must not be treated as a CPU device."""
    
        class SneakyDevice:
            def __repr__(self):
                return "cuda:1 (spilled from cpu)"
    
        with pytest.raises(ValueError, match="Invalid device specification"):
>           _parse_device_to_dlpack(SneakyDevice())

test/core/xp_utils/helpers/test_device_parsing.py:66: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
___________ test_device_cache_key_prefers_the_backend_device_object ____________

    def test_device_cache_key_prefers_the_backend_device_object():
        """Hashable device objects identify a device exactly; the DLPack tuple does not."""
>       cpu = xps.asarray([1.0], device=xps.Device("CPU_DEVICE"))

test/core/xp_utils/helpers/test_device_parsing.py:100: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
____________ test_repeated_queries[array_api_strict-float32-2-True] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 2, dtype_name = 'float32'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float32)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________ test_repeated_queries[array_api_strict-float32-2-False] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 2, dtype_name = 'float32'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float32)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
____________ test_repeated_queries[array_api_strict-float32-3-True] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 3, dtype_name = 'float32'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float32)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________ test_repeated_queries[array_api_strict-float32-3-False] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 3, dtype_name = 'float32'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float32)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
____________ test_repeated_queries[array_api_strict-float64-2-True] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 2, dtype_name = 'float64'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________ test_repeated_queries[array_api_strict-float64-2-False] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 2, dtype_name = 'float64'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
____________ test_repeated_queries[array_api_strict-float64-3-True] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 3, dtype_name = 'float64'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________ test_repeated_queries[array_api_strict-float64-3-False] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 3, dtype_name = 'float64'

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_repeated_queries(xp, prefer_native, ndim, dtype_name):
        """Nonuniform, descending grids reproduce a multilinear field and clip queries."""
        dtype = getattr(xp, dtype_name)
        points = (np.array([3.0, 1.0, -2.0]),) + (np.array([-1.0, 0.5, 2.0]),) * (ndim - 1)
        grids = np.meshgrid(*points, indexing="ij")
        values = sum((i + 1) * g for i, g in enumerate(grids)) + np.prod(grids, axis=0)
        coords = np.array([[0.25] * ndim, [3.0] * ndim, [-2.0] * ndim])
        clipped = np.column_stack(
            [np.clip(coords[:, i], p.min(), p.max()) for i, p in enumerate(points)]
        )
        expected = sum((i + 1) * clipped[:, i] for i in range(ndim)) + np.prod(clipped, axis=1)
        points = tuple(xp.asarray(p, dtype=dtype) for p in points)
        values = xp.asarray(values, dtype=dtype)
        queries = xp.asarray(coords, dtype=dtype)
        interpolator = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
        if not prefer_native:
            assert interpolator.implementation == "array_api"
        for _ in range(2):
>           result = interpolator(queries)

test/core/xp_utils/test_interpolation.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 1, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________ test_bounds_and_query_validation[array_api_strict-True] ____________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True

    @pytest.mark.parametrize("prefer_native", [True, False])
    def test_bounds_and_query_validation(xp, prefer_native):
        """Boundary points are accepted, out-of-grid coordinates and bad shapes rejected."""
        p = xp.asarray([0.0, 1.0])
        values = xp.asarray([[0.0, 1.0], [1.0, 2.0]])
        interpolator = ArrayBackendGridInterpolator(
            (p, p), values, bounds_error=True, prefer_native=prefer_native
        )
        np.testing.assert_allclose(
>           to_numpy(interpolator(xp.asarray([[0.0, 0.0], [1.0, 1.0]]))), [0, 2]
        )

test/core/xp_utils/test_interpolation.py:73: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1.], dtype=array_api_strict.float64)
key = Array([0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___________ test_bounds_and_query_validation[array_api_strict-False] ___________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False

    @pytest.mark.parametrize("prefer_native", [True, False])
    def test_bounds_and_query_validation(xp, prefer_native):
        """Boundary points are accepted, out-of-grid coordinates and bad shapes rejected."""
        p = xp.asarray([0.0, 1.0])
        values = xp.asarray([[0.0, 1.0], [1.0, 2.0]])
        interpolator = ArrayBackendGridInterpolator(
            (p, p), values, bounds_error=True, prefer_native=prefer_native
        )
        np.testing.assert_allclose(
>           to_numpy(interpolator(xp.asarray([[0.0, 0.0], [1.0, 1.0]]))), [0, 2]
        )

test/core/xp_utils/test_interpolation.py:73: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1.], dtype=array_api_strict.float64)
key = Array([0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______ test_shared_value_dimensions[array_api_strict-value_shape0-2-True] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 2, value_shape = (3,)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_____ test_shared_value_dimensions[array_api_strict-value_shape0-2-False] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 2, value_shape = (3,)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______ test_shared_value_dimensions[array_api_strict-value_shape0-3-True] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 3, value_shape = (3,)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_____ test_shared_value_dimensions[array_api_strict-value_shape0-3-False] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 3, value_shape = (3,)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______ test_shared_value_dimensions[array_api_strict-value_shape1-2-True] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 2, value_shape = (2, 3)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_____ test_shared_value_dimensions[array_api_strict-value_shape1-2-False] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 2, value_shape = (2, 3)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______ test_shared_value_dimensions[array_api_strict-value_shape1-3-True] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True, ndim = 3, value_shape = (2, 3)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_____ test_shared_value_dimensions[array_api_strict-value_shape1-3-False] ______

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False, ndim = 3, value_shape = (2, 3)

    @pytest.mark.parametrize("prefer_native", [True, False])
    @pytest.mark.parametrize("ndim", [2, 3])
    @pytest.mark.parametrize("value_shape", [(3,), (2, 3)])
    def test_shared_value_dimensions(xp, prefer_native, ndim, value_shape):
        """Shared grid searches preserve independent scalar fields and trailing shape."""
        points = (xp.asarray([3.0, 1.0, -2.0]),) * ndim
        shape = (3,) * ndim + value_shape
        values = xp.asarray(np.random.default_rng(6).normal(size=shape), dtype=xp.float32)
        queries = xp.asarray([[0.5] * ndim, [-2.0] * ndim, [4.0] * ndim], dtype=xp.float32)
        shared = ArrayBackendGridInterpolator(points, values, prefer_native=prefer_native)
>       result = shared(queries)

test/core/xp_utils/test_interpolation.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:303: in _interp3d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2.,  1.,  3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None-None-array-float32] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'array', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None-None-array-float64] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'array', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None-None-rows-float32] ____

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'rows', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None-None-rows-float64] ____

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'rows', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-None-None-fields-float32] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'fields', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-None-None-fields-float64] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'fields', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None-None-mixed_rows-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_rows', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None-None-mixed_rows-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_rows', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None-None-mixed_dtype-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_dtype', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None-None-mixed_dtype-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_dtype', boundary = None, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None--3.5-array-float32] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'array', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None--3.5-array-float64] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'array', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None--3.5-rows-float32] ____

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'rows', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_prepared_matches_one_shot[array_api_strict-None--3.5-rows-float64] ____

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'rows', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-None--3.5-fields-float32] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'fields', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-None--3.5-fields-float64] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'fields', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_rows-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_rows', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_rows-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_rows', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_dtype-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_dtype', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_dtype-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_dtype', boundary = -3.5, offset = None

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0. , 0.3, 2.4, 5. ], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-array-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'array', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-array-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'array', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict--0.1723-None-rows-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'rows', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict--0.1723-None-rows-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'rows', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-fields-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'fields', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-fields-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'fields', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_rows-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_rows', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_rows-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_rows', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_dtype-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_dtype', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_dtype-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_dtype', boundary = None, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-array-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'array', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-array-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'array', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-rows-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'rows', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-rows-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'rows', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-fields-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'fields', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-fields-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'fields', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_rows-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_rows', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_rows-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_rows', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_dtype-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_dtype', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723    ,  0.12770002,
        2.2277    ,  4.8277    ], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_dtype-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_dtype', boundary = -3.5, offset = -0.1723

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-0.1723,  0.1277,  2.2277,  4.8277], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 1, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 1 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234-None-array-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'array', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234-None-array-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'array', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234-None-rows-float32] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'rows', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234-None-rows-float64] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'rows', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234-None-fields-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'fields', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234-None-fields-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'fields', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_rows-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_rows', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_rows-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_rows', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_dtype-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_dtype', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_dtype-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_dtype', boundary = None, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-array-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'array', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-array-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'array', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-rows-float32] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'rows', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-rows-float64] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'rows', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-fields-float32] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'fields', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-fields-float64] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'fields', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_rows-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_rows', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_rows-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_rows', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_dtype-float32] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32', kind = 'mixed_dtype', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234   , 13.534   , 15.634001,
       18.234001], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_dtype-float64] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64', kind = 'mixed_dtype', boundary = -3.5, offset = 13.234

    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    @pytest.mark.parametrize("kind", ["array", "rows", "fields", "mixed_rows", "mixed_dtype"])
    @pytest.mark.parametrize("boundary", [None, -3.5])
    @pytest.mark.parametrize("offset", [None, -0.1723, 13.234])
    def test_prepared_matches_one_shot(xp, dtype, kind, boundary, offset):
        x = xp.asarray([0.0, 0.3, 2.4, 5.0], dtype=getattr(xp, dtype))
        y = xp.asarray([1.0, -2.0, 4.0, 3.0], dtype=getattr(xp, dtype))
        if kind == "rows":
            y = xp.stack([y, y * 2], axis=0)
        elif kind != "array":
            y = {"one": y, "two": y * 2}
            if kind == "mixed_rows":
                y["two"] = xp.stack([y["one"] * 2, y["one"] * 3], axis=0)
            if kind == "mixed_dtype":
                y["two"] = xp.astype(y["two"], xp.float64 if dtype == "float32" else xp.float32)
        obj = ArrayBackend1DInterpolator(x, y, left=boundary, right=boundary)
        grid = x if offset is None else x + offset
        q = xp.concat(
            [grid, xp.asarray([-9.0, 0.11, 2.2, 40.0, np.nan, np.inf, -np.inf], dtype=x.dtype)]
        )
        for queries in (q, q[:0], xp.flip(q, axis=0)):
>           actual = obj(queries, grid_offset=offset)

test/core/xp_utils/test_interpolation1d.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([13.234, 13.534, 15.634, 18.234], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 0, 0, 2, 2, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 0 0 2 2 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________ test_dispatch_preference[array_api_strict-None] ________________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = None

    @pytest.mark.parametrize("prefer_native", [None, True, False])
    def test_dispatch_preference(xp, prefer_native):
        x = xp.asarray([0.0, 1.0, 3.0], dtype=xp.float64)
        y = {"a": x * 2, "b": xp.stack([x * 3, x * 4])}
        q = xp.asarray([-1.0, 0.5, 2.0, 4.0], dtype=xp.float64)
>       actual = ArrayBackend1DInterpolator(x, y, prefer_native=prefer_native)(q)

test/core/xp_utils/test_interpolation1d.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________ test_dispatch_preference[array_api_strict-True] ________________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = True

    @pytest.mark.parametrize("prefer_native", [None, True, False])
    def test_dispatch_preference(xp, prefer_native):
        x = xp.asarray([0.0, 1.0, 3.0], dtype=xp.float64)
        y = {"a": x * 2, "b": xp.stack([x * 3, x * 4])}
        q = xp.asarray([-1.0, 0.5, 2.0, 4.0], dtype=xp.float64)
>       actual = ArrayBackend1DInterpolator(x, y, prefer_native=prefer_native)(q)

test/core/xp_utils/test_interpolation1d.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________ test_dispatch_preference[array_api_strict-False] _______________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
prefer_native = False

    @pytest.mark.parametrize("prefer_native", [None, True, False])
    def test_dispatch_preference(xp, prefer_native):
        x = xp.asarray([0.0, 1.0, 3.0], dtype=xp.float64)
        y = {"a": x * 2, "b": xp.stack([x * 3, x * 4])}
        q = xp.asarray([-1.0, 0.5, 2.0, 4.0], dtype=xp.float64)
>       actual = ArrayBackend1DInterpolator(x, y, prefer_native=prefer_native)(q)

test/core/xp_utils/test_interpolation1d.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation1d.py:134: in __call__
    interpolated = self._interpolate(xq, x, values, self._left, self._right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0., 1., 3.], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue0-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue0-False]>>
mode = 'carbon', model = 'alpha_beta_kernels'
tissue = TissueProperties(cell_line=None, model='Generic', alpha_x=0.1, beta_x=0.05)
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue2-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue2-False]>>
mode = 'carbon', model = 'alpha_beta_kernels'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan)
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue3-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue3-False]>>
mode = 'carbon', model = 'alpha_beta_kernels'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=30.0, r_nucleus=5.0)
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue4-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue4-False]>>
mode = 'carbon', model = 'alpha_beta_kernels'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
WARNING  pyRadPlan.dose.engines._base:_base.py:983 Biological model 'alpha_beta_kernels': Target: The tissue is labelled LEM I; the base data is LEM IV (α/β kernels · LEM IV, kernel set 'alpha_beta', tissue class Generic_LEMax05). Evaluated with the base data's model. Relabel it as LEM IV to silence this warning (tissue.adopt_provenance("LEM_IV")).
WARNING  pyRadPlan.dose.engines._base:_base.py:983 Biological model 'alpha_beta_kernels': Body: The tissue is labelled LEM I; the base data is LEM IV (α/β kernels · LEM IV, kernel set 'alpha_beta', tissue class Generic_LEMax05). Evaluated with the base data's model. Relabel it as LEM IV to silence this warning (tissue.adopt_provenance("LEM_IV")).
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-alpha_beta_kernels-tissue6-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-alpha_beta_kernels-tissue6-False]>>
mode = 'oxygen', model = 'alpha_beta_kernels'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-zstar_kernels-tissue8-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-zstar_kernels-tissue8-False]>>
mode = 'oxygen', model = 'zstar_kernels'
tissue = MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-zstar_kernels-tissue9-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-zstar_kernels-tissue9-False]>>
mode = 'oxygen', model = 'zstar_kernels'
tissue = MKMTissueProperties(cell_line=None, model='MKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
WARNING  pyRadPlan.dose.engines._base:_base.py:983 Biological model 'zstar_kernels': Target: The tissue is labelled MKM; the base data is mMKM (z* kernels · mMKM, z1D_saturation_corrected, kernel set 'zs', tissue class Generic_MKM). Evaluated with the base data's model. Relabel it as mMKM to silence this warning (tissue.adopt_provenance("mMKM")).
WARNING  pyRadPlan.dose.engines._base:_base.py:983 Biological model 'zstar_kernels': Body: The tissue is labelled MKM; the base data is mMKM (z* kernels · mMKM, z1D_saturation_corrected, kernel set 'zs', tissue class Generic_MKM). Evaluated with the base data's model. Relabel it as mMKM to silence this warning (tissue.adopt_provenance("mMKM")).
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-LEM-tissue11-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-LEM-tissue11-False]>>
mode = 'oxygen', model = 'LEM'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='II', d_t=30.0, r_nucleus=5.0)
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
WARNING  pyRadPlan.dose.engines._base:_base.py:983 Biological model 'alpha_beta_kernels': Target: The tissue is labelled LEM II; the base data is LEM I (α/β kernels · LEM I, kernel set 'alpha_beta', tissue class Generic_LEMax05). Evaluated with the base data's model. Relabel it as LEM I to silence this warning (tissue.adopt_provenance("LEM_I")).
WARNING  pyRadPlan.dose.engines._base:_base.py:983 Biological model 'alpha_beta_kernels': Body: The tissue is labelled LEM II; the base data is LEM I (α/β kernels · LEM I, kernel set 'alpha_beta', tissue class Generic_LEMax05). Evaluated with the base data's model. Relabel it as LEM I to silence this warning (tissue.adopt_provenance("LEM_I")).
_ test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[protons-WED-tissue14-False] _

request = <FixtureRequest for <Function test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[protons-WED-tissue14-False]>>
mode = 'protons', model = 'WED'
tissue = TissueProperties(cell_line=None, model='Generic', alpha_x=0.1, beta_x=0.05)
has_errors = False

    @pytest.mark.parametrize(("mode", "model", "tissue", "has_errors"), CASES)
    def test_requested_bio_dose_raises_exactly_when_the_check_reports_errors(
        request, mode, model, tissue, has_errors
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = True
        _set_tissue(cst, tissue)
    
        machine = DoseEngineBase.load_machine(pln.radiation_mode, pln.machine)
        issues = create_bio_model(model).check_tissues(machine, cst)
        assert bool([issue for issue in issues if issue.is_error]) is has_errors
    
        if has_errors:
            with pytest.raises(BioModelUnavailableError) as info:
                calc_dose_influence(ct, cst, stf, pln)
            for issue in issues:
                assert issue.message in str(info.value)
        else:
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[carbon-alpha_beta_kernels-tissue0] _

request = <FixtureRequest for <Function test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[carbon-alpha_beta_kernels-tissue0]>>
mode = 'carbon', model = 'alpha_beta_kernels'
tissue = TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05)

    @pytest.mark.parametrize(
        ("mode", "model", "tissue"),
        [(mode, model, tissue) for mode, model, tissue, has_errors in CASES if has_errors],
    )
    def test_automatic_bio_dose_falls_back_to_physical_dose_on_errors(request, mode, model, tissue):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        _set_tissue(cst, tissue)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:95: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'alpha_beta_kernels' cannot be evaluated for this machine and these structures: Target: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM IV (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan).; Body: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM IV (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan). Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[carbon-zstar_kernels-tissue1] _

request = <FixtureRequest for <Function test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[carbon-zstar_kernels-tissue1]>>
mode = 'carbon', model = 'zstar_kernels'
tissue = MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')

    @pytest.mark.parametrize(
        ("mode", "model", "tissue"),
        [(mode, model, tissue) for mode, model, tissue, has_errors in CASES if has_errors],
    )
    def test_automatic_bio_dose_falls_back_to_physical_dose_on_errors(request, mode, model, tissue):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        _set_tissue(cst, tissue)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:95: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'zstar_kernels' cannot be evaluated for this machine and these structures: Biological model 'zstar_kernels' not available: Required quantities not provided by Dose Engine or selected Machine Dataset: zs. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-alpha_beta_kernels-tissue2] _

request = <FixtureRequest for <Function test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-alpha_beta_kernels-tissue2]>>
mode = 'oxygen', model = 'alpha_beta_kernels'
tissue = MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')

    @pytest.mark.parametrize(
        ("mode", "model", "tissue"),
        [(mode, model, tissue) for mode, model, tissue, has_errors in CASES if has_errors],
    )
    def test_automatic_bio_dose_falls_back_to_physical_dose_on_errors(request, mode, model, tissue):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        _set_tissue(cst, tissue)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:95: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'alpha_beta_kernels' cannot be evaluated for this machine and these structures: Target: The tissue parameters are mMKM (MKMTissueProperties), but α/β kernels · LEM I (kernel set 'alpha_beta') is tabulated for LEM I tissue classes, which share no identifying parameter with them.; Body: The tissue parameters are mMKM (MKMTissueProperties), but α/β kernels · LEM I (kernel set 'alpha_beta') is tabulated for LEM I tissue classes, which share no identifying parameter with them. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-zstar_kernels-tissue3] _

request = <FixtureRequest for <Function test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-zstar_kernels-tissue3]>>
mode = 'oxygen', model = 'zstar_kernels'
tissue = MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=nan, reference_radiation='xray_200kV')

    @pytest.mark.parametrize(
        ("mode", "model", "tissue"),
        [(mode, model, tissue) for mode, model, tissue, has_errors in CASES if has_errors],
    )
    def test_automatic_bio_dose_falls_back_to_physical_dose_on_errors(request, mode, model, tissue):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        _set_tissue(cst, tissue)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:95: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'zstar_kernels' cannot be evaluated for this machine and these structures: Target: Biological model 'zstar_kernels' reads the tissue parameter(s) beta_0 of every structure, but MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=nan, reference_radiation='xray_200kV') lacks beta_0.; Body: Biological model 'zstar_kernels' reads the tissue parameter(s) beta_0 of every structure, but MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=nan, reference_radiation='xray_200kV') lacks beta_0. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-zstar_kernels-tissue4] _

request = <FixtureRequest for <Function test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-zstar_kernels-tissue4]>>
mode = 'oxygen', model = 'zstar_kernels'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)

    @pytest.mark.parametrize(
        ("mode", "model", "tissue"),
        [(mode, model, tissue) for mode, model, tissue, has_errors in CASES if has_errors],
    )
    def test_automatic_bio_dose_falls_back_to_physical_dose_on_errors(request, mode, model, tissue):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        _set_tissue(cst, tissue)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:95: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'zstar_kernels' cannot be evaluated for this machine and these structures: Target: The tissue parameters are LEM I (LEMTissueProperties), but z* kernels · mMKM (kernel set 'zs') is tabulated for mMKM tissue classes, which share no identifying parameter with them.; Body: The tissue parameters are LEM I (LEMTissueProperties), but z* kernels · mMKM (kernel set 'zs') is tabulated for mMKM tissue classes, which share no identifying parameter with them. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[protons-MCN-tissue5] _

request = <FixtureRequest for <Function test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[protons-MCN-tissue5]>>
mode = 'protons', model = 'MCN'
tissue = TissueProperties(cell_line=None, model='Generic', alpha_x=0.1, beta_x=0.0)

    @pytest.mark.parametrize(
        ("mode", "model", "tissue"),
        [(mode, model, tissue) for mode, model, tissue, has_errors in CASES if has_errors],
    )
    def test_automatic_bio_dose_falls_back_to_physical_dose_on_errors(request, mode, model, tissue):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        _set_tissue(cst, tissue)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:95: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'MCN' cannot be evaluated for this machine and these structures: Target: Biological model 'MCN' is only defined for beta_x > 0, but the structure has beta_x = 0.0.; Body: Biological model 'MCN' is only defined for beta_x > 0, but the structure has beta_x = 0.0. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_differing_provenance_label_warns_and_evaluates_the_model[oxygen-zstar_kernels-tissue0-relabelled0-auto] _

request = <FixtureRequest for <Function test_differing_provenance_label_warns_and_evaluates_the_model[oxygen-zstar_kernels-tissue0-relabelled0-auto]>>
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51adc11a90>
mode = 'oxygen', model = 'zstar_kernels'
tissue = MKMTissueProperties(cell_line=None, model='MKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')
relabelled = MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')
calc_bio_dose = 'auto'

    @pytest.mark.parametrize("calc_bio_dose", ["auto", True])
    @pytest.mark.parametrize(("mode", "model", "tissue", "relabelled"), WARNING_CASES)
    def test_differing_provenance_label_warns_and_evaluates_the_model(
        request, caplog, mode, model, tissue, relabelled, calc_bio_dose
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = calc_bio_dose
    
        _set_tissue(cst, relabelled)
>       reference = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:134: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_differing_provenance_label_warns_and_evaluates_the_model[oxygen-zstar_kernels-tissue0-relabelled0-True] _

request = <FixtureRequest for <Function test_differing_provenance_label_warns_and_evaluates_the_model[oxygen-zstar_kernels-tissue0-relabelled0-True]>>
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a1748180>
mode = 'oxygen', model = 'zstar_kernels'
tissue = MKMTissueProperties(cell_line=None, model='MKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')
relabelled = MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.30051126563660663, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615, reference_radiation='xray_200kV')
calc_bio_dose = True

    @pytest.mark.parametrize("calc_bio_dose", ["auto", True])
    @pytest.mark.parametrize(("mode", "model", "tissue", "relabelled"), WARNING_CASES)
    def test_differing_provenance_label_warns_and_evaluates_the_model(
        request, caplog, mode, model, tissue, relabelled, calc_bio_dose
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = calc_bio_dose
    
        _set_tissue(cst, relabelled)
>       reference = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:134: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_differing_provenance_label_warns_and_evaluates_the_model[carbon-alpha_beta_kernels-tissue1-relabelled1-auto] _

request = <FixtureRequest for <Function test_differing_provenance_label_warns_and_evaluates_the_model[carbon-alpha_beta_kernels-tissue1-relabelled1-auto]>>
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a17488a0>
mode = 'carbon', model = 'alpha_beta_kernels'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)
relabelled = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=30.0, r_nucleus=5.0)
calc_bio_dose = 'auto'

    @pytest.mark.parametrize("calc_bio_dose", ["auto", True])
    @pytest.mark.parametrize(("mode", "model", "tissue", "relabelled"), WARNING_CASES)
    def test_differing_provenance_label_warns_and_evaluates_the_model(
        request, caplog, mode, model, tissue, relabelled, calc_bio_dose
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = calc_bio_dose
    
        _set_tissue(cst, relabelled)
>       reference = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:134: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_ test_differing_provenance_label_warns_and_evaluates_the_model[carbon-alpha_beta_kernels-tissue1-relabelled1-True] _

request = <FixtureRequest for <Function test_differing_provenance_label_warns_and_evaluates_the_model[carbon-alpha_beta_kernels-tissue1-relabelled1-True]>>
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a2716d50>
mode = 'carbon', model = 'alpha_beta_kernels'
tissue = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)
relabelled = LEMTissueProperties(cell_line=None, model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=30.0, r_nucleus=5.0)
calc_bio_dose = True

    @pytest.mark.parametrize("calc_bio_dose", ["auto", True])
    @pytest.mark.parametrize(("mode", "model", "tissue", "relabelled"), WARNING_CASES)
    def test_differing_provenance_label_warns_and_evaluates_the_model(
        request, caplog, mode, model, tissue, relabelled, calc_bio_dose
    ):
        pln, ct, cst, stf, _dij, _result = request.getfixturevalue(f"test_data_{mode}")
        pln.bio_model = model
        pln.prop_dose_calc["calc_bio_dose"] = calc_bio_dose
    
        _set_tissue(cst, relabelled)
>       reference = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_bio_tissue_check.py:134: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
_____________ test_default_bio_model_with_matching_tissue[protons] _____________

request = <FixtureRequest for <Function test_default_bio_model_with_matching_tissue[protons]>>
mode = 'protons'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a3237240>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_matching_tissue(request, mode, caplog):
        """Physical-dose objectives are optimized on RBExDose, which the dij provides."""
        pln, ct, cst, stf = _case(request, mode)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_____________ test_default_bio_model_with_matching_tissue[helium] ______________

request = <FixtureRequest for <Function test_default_bio_model_with_matching_tissue[helium]>>
mode = 'helium'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a32357b0>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_matching_tissue(request, mode, caplog):
        """Physical-dose objectives are optimized on RBExDose, which the dij provides."""
        pln, ct, cst, stf = _case(request, mode)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
_____________ test_default_bio_model_with_matching_tissue[carbon] ______________

request = <FixtureRequest for <Function test_default_bio_model_with_matching_tissue[carbon]>>
mode = 'carbon'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a32b9b50>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_matching_tissue(request, mode, caplog):
        """Physical-dose objectives are optimized on RBExDose, which the dij provides."""
        pln, ct, cst, stf = _case(request, mode)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_____________ test_default_bio_model_with_matching_tissue[oxygen] ______________

request = <FixtureRequest for <Function test_default_bio_model_with_matching_tissue[oxygen]>>
mode = 'oxygen'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a1616250>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_matching_tissue(request, mode, caplog):
        """Physical-dose objectives are optimized on RBExDose, which the dij provides."""
        pln, ct, cst, stf = _case(request, mode)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_ test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[protons] _

request = <FixtureRequest for <Function test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[protons]>>
mode = 'protons'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a1636030>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow(request, mode, caplog):
        """Tissue without base data only drops the kernel-based biology, with a warning."""
        pln, ct, cst, stf = _case(request, mode)
        _set_tissue(cst, UNMATCHED_TISSUE)
    
        with caplog.at_level(logging.WARNING):
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:82: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_ test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[helium] _

request = <FixtureRequest for <Function test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[helium]>>
mode = 'helium'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a1635c70>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow(request, mode, caplog):
        """Tissue without base data only drops the kernel-based biology, with a warning."""
        pln, ct, cst, stf = _case(request, mode)
        _set_tissue(cst, UNMATCHED_TISSUE)
    
        with caplog.at_level(logging.WARNING):
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:82: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
_ test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[carbon] _

request = <FixtureRequest for <Function test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[carbon]>>
mode = 'carbon'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a132e970>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow(request, mode, caplog):
        """Tissue without base data only drops the kernel-based biology, with a warning."""
        pln, ct, cst, stf = _case(request, mode)
        _set_tissue(cst, UNMATCHED_TISSUE)
    
        with caplog.at_level(logging.WARNING):
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:82: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'alpha_beta_kernels' cannot be evaluated for this machine and these structures: Target: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM IV (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan).; Body: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM IV (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan). Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[oxygen] _

request = <FixtureRequest for <Function test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[oxygen]>>
mode = 'oxygen'
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a1903bd0>

    @pytest.mark.parametrize("mode", ION_MODES)
    def test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow(request, mode, caplog):
        """Tissue without base data only drops the kernel-based biology, with a warning."""
        pln, ct, cst, stf = _case(request, mode)
        _set_tissue(cst, UNMATCHED_TISSUE)
    
        with caplog.at_level(logging.WARNING):
>           dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:82: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'alpha_beta_kernels' cannot be evaluated for this machine and these structures: Target: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM I (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0).; Body: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM I (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0). Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
____________ test_unmatched_tissue_with_physical_dose_only[carbon] _____________

request = <FixtureRequest for <Function test_unmatched_tissue_with_physical_dose_only[carbon]>>
mode = 'carbon'

    @pytest.mark.parametrize("mode", KERNEL_MODES)
    def test_unmatched_tissue_with_physical_dose_only(request, mode):
        """The physical-dose influence of an unmatched tissue works as before (calc_bio_dose=False)."""
        pln, ct, cst, stf = _case(request, mode)
        pln.prop_dose_calc["calc_bio_dose"] = False
        _set_tissue(cst, UNMATCHED_TISSUE)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:107: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'alpha_beta_kernels' cannot be evaluated for this machine and these structures: Target: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM IV (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan).; Body: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM IV (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='IV', d_t=nan, r_nucleus=nan). Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
____________ test_unmatched_tissue_with_physical_dose_only[oxygen] _____________

request = <FixtureRequest for <Function test_unmatched_tissue_with_physical_dose_only[oxygen]>>
mode = 'oxygen'

    @pytest.mark.parametrize("mode", KERNEL_MODES)
    def test_unmatched_tissue_with_physical_dose_only(request, mode):
        """The physical-dose influence of an unmatched tissue works as before (calc_bio_dose=False)."""
        pln, ct, cst, stf = _case(request, mode)
        pln.prop_dose_calc["calc_bio_dose"] = False
        _set_tissue(cst, UNMATCHED_TISSUE)
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:107: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'alpha_beta_kernels' cannot be evaluated for this machine and these structures: Target: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM I (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0).; Body: No matching tissue class in the machine base data for TissueProperties(cell_line=None, model='Generic', alpha_x=0.2, beta_x=0.05) in α/β kernels · LEM I (kernel set 'alpha_beta'). Available classes: [LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0), LEMTissueProperties(cell_line='Generic_LEMax05', model='LEM', alpha_x=0.5, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0)]. Closest class: LEMTissueProperties(cell_line='Generic_LEMax01', model='LEM', alpha_x=0.1, beta_x=0.05, version='I', d_t=30.0, r_nucleus=5.0). Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
____________________ test_oxygen_lem_dose_calculation[0.1] _____________________

test_data_oxygen = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'iso_c...0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), ...})
alpha_x = 0.1

    @pytest.mark.parametrize("alpha_x", [0.1, 0.5])
    def test_oxygen_lem_dose_calculation(test_data_oxygen, alpha_x):
        """Both LEM classes of the generic oxygen machine yield finite biological results."""
        pln, ct, cst, stf, _dij, _result = test_data_oxygen
        pln.bio_model = "LEM"
        _set_tissue(cst, LEMTissueProperties(alpha_x=alpha_x, beta_x=0.05, d_t=30.0, r_nucleus=5.0))
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:131: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_oxygen_lem_dose_calculation[0.5] _____________________

test_data_oxygen = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'iso_c...0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), ...})
alpha_x = 0.5

    @pytest.mark.parametrize("alpha_x", [0.1, 0.5])
    def test_oxygen_lem_dose_calculation(test_data_oxygen, alpha_x):
        """Both LEM classes of the generic oxygen machine yield finite biological results."""
        pln, ct, cst, stf, _dij, _result = test_data_oxygen
        pln.bio_model = "LEM"
        _set_tissue(cst, LEMTissueProperties(alpha_x=alpha_x, beta_x=0.05, d_t=30.0, r_nucleus=5.0))
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:131: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________________ test_oxygen_mkm_dose_calculation _______________________

test_data_oxygen = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'iso_c...0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), ...})

    def test_oxygen_mkm_dose_calculation(test_data_oxygen):
        """z* kernels: alpha/beta from the oxygen machine's mMKM z* and the tissue's alpha_0."""
        pln, ct, cst, stf, _dij, _result = test_data_oxygen
        pln.bio_model = "MKM"
        _set_tissue(
            cst,
            MKMTissueProperties(
                model="mMKM", r_nucleus=3.9, r_domain=0.32, alpha_0=0.172, beta_0=0.0615
            ),
        )
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_________________ test_oxygen_mkm_rejects_unset_alpha_0[auto] __________________

test_data_oxygen = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'iso_c...0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), ...})
calc_bio_dose = 'auto'

    @pytest.mark.parametrize("calc_bio_dose", [True, "auto"])
    def test_oxygen_mkm_rejects_unset_alpha_0(test_data_oxygen, calc_bio_dose):
        """A tissue without alpha_0 must not yield NaN biological quantities."""
        pln, ct, cst, stf, _dij, _result = test_data_oxygen
        pln.bio_model = "MKM"
        pln.prop_dose_calc["calc_bio_dose"] = calc_bio_dose
        _set_tissue(
            cst, MKMTissueProperties(model="mMKM", r_nucleus=3.9, r_domain=0.32, beta_0=0.0615)
        )
    
        if calc_bio_dose is True:
            with pytest.raises(BioModelUnavailableError, match="lacks alpha_0"):
                calc_dose_influence(ct, cst, stf, pln)
            return
    
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_default_bio_models.py:179: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'zstar_kernels' cannot be evaluated for this machine and these structures: Target: Biological model 'zstar_kernels' reads the tissue parameter(s) alpha_0 of every structure, but MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.1, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=nan, beta_0=0.0615, reference_radiation='xray_200kV') lacks alpha_0.; Body: Biological model 'zstar_kernels' reads the tissue parameter(s) alpha_0 of every structure, but MKMTissueProperties(cell_line=None, model='mMKM', alpha_x=0.1, beta_x=0.0615, r_nucleus=3.9, r_domain=0.32, alpha_0=nan, beta_0=0.0615, reference_radiation='xray_200kV') lacks alpha_0. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
___________________ test_reused_engine_repeats_forward_dose ____________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_reused_engine_repeats_forward_dose(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        w = np.ones(stf.total_number_of_bixels)
        engine = get_engine(pln)
    
>       first = _dose(engine.calc_dose_forward(ct, cst, stf, w))

test/dose/test_engine_reuse.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_____________ test_reused_engine_uses_the_structures_of_each_call ______________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_reused_engine_uses_the_structures_of_each_call(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        w = np.ones(stf.total_number_of_bixels)
        targets = cst.model_copy(update={"vois": [v for v in cst.vois if v.voi_type == "TARGET"]})
    
        engine = get_engine(pln)
>       engine.calc_dose_forward(ct, targets, stf, w)

test/dose/test_engine_reuse.py:33: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.8866995,  1.0837438],
        [ 0.7856742, -0.7856742]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.8866995,  1.0837438],
        [-0.7856742,  0.7856742]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
________________ test_forward_dose_leaves_the_stf_weights_alone ________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_forward_dose_leaves_the_stf_weights_alone(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        before = [b.weight for beam in stf.beams for ray in beam.rays for b in ray.beamlets]
        w = np.linspace(0.1, 1.0, stf.total_number_of_bixels)
    
>       get_engine(pln).calc_dose_forward(ct, cst, stf, w)

test/dose/test_engine_reuse.py:47: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
__________________________ test_keep_rad_depth_cubes ___________________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_keep_rad_depth_cubes(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        pln.prop_dose_calc["keep_rad_depth_cubes"] = True
        engine = get_engine(pln)
    
        for _ in range(2):
>           dij = engine.calc_dose_influence(ct, cst, stf)

test/dose/test_engine_reuse.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: keep_rad_depth_cubes
__________ test_reused_engine_resolves_the_bio_model_per_calculation ___________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_reused_engine_resolves_the_bio_model_per_calculation(test_data_protons):
        pln, ct, cst, stf, _, _ = test_data_protons
        pln.bio_model = "MCN"
        no_beta = cst.model_copy(deep=True)
        for voi in no_beta.vois:
            voi.beta_x = 0.0
    
        # McNamara needs beta_x > 0, so the first calculation drops the model ...
        engine = get_engine(pln)
>       assert engine.calc_dose_influence(ct, no_beta, stf).bio_model is None

test/dose/test_engine_reuse.py:75: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'MCN' cannot be evaluated for this machine and these structures: Target: Biological model 'MCN' is only defined for beta_x > 0, but the structure has beta_x = 0.0.; Body: Biological model 'MCN' is only defined for beta_x > 0, but the structure has beta_x = 0.0. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
________ test_reused_engine_resolves_the_lateral_model_per_calculation _________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_reused_engine_resolves_the_lateral_model_per_calculation(test_data_protons):
        # "auto" resolves to the most accurate model the machine supports, for each calculation
        pln, ct, cst, stf, _, _ = test_data_protons
        pln.prop_dose_calc["lateral_model"] = "auto"
        engine = get_engine(pln)
    
>       engine.calc_dose_influence(ct, cst, stf)

test/dose/test_engine_reuse.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________________ test_protons_cd_forward ____________________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:27: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_protons_cd_forward_bio_model_none ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward_bio_model_none(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "none"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_protons_cd_forward_bio_model_WED _____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward_bio_model_WED(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "WED"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:47: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_protons_cd_forward_bio_model_MCN _____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward_bio_model_MCN(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "MCN"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:57: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_protons_cd_forward_bio_model_CAR _____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward_bio_model_CAR(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "CAR"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:67: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_protons_cd_forward_bio_model_LSM _____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward_bio_model_LSM(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "LSM"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:77: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_protons_cd_forward_bio_model_const ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward_bio_model_const(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "constant_rbe"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:87: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________________ test_helium_cd_forward ____________________________

test_data_helium = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_helium_cd_forward(test_data_helium):
        pln, ct, cst, stf, dij, result = test_data_helium
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:96: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
____________________ test_helium_cd_forward_bio_model_none _____________________

test_data_helium = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_helium_cd_forward_bio_model_none(test_data_helium):
        pln, ct, cst, stf, dij, result = test_data_helium
        pln.bio_model = "none"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
_____________________ test_helium_cd_forward_bio_model_LSM _____________________

test_data_helium = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_helium_cd_forward_bio_model_LSM(test_data_helium):
        pln, ct, cst, stf, dij, result = test_data_helium
        pln.bio_model = "LSM"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:116: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
____________________________ test_carbon_cd_forward ____________________________

test_data_carbon = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_carbon_cd_forward(test_data_carbon):
        pln, ct, cst, stf, dij, result = test_data_carbon
>       result_py = calc_dose_forward(ct, cst, stf, pln)

test/dose/test_hongpb.py:125: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_carbon_cd_forward_bio_model_none _____________________

test_data_carbon = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_carbon_cd_forward_bio_model_none(test_data_carbon):
        pln, ct, cst, stf, dij, result = test_data_carbon
        pln.bio_model = "none"
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_hongpb.py:135: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________________ test_oxygen_cd_forward ____________________________

test_data_oxygen = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'iso_c...0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), ...})

    def test_oxygen_cd_forward(test_data_oxygen):
        pln, ct, cst, stf, dij, result = test_data_oxygen
>       result_py = calc_dose_forward(ct, cst, stf, pln)

test/dose/test_hongpb.py:144: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
__________________________ test_protons_cd_influence ___________________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_influence(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:153: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_protons_cd_influence_bio_model_none ___________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_influence_bio_model_none(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "none"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:163: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_protons_cd_influence_bio_model_WED ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_influence_bio_model_WED(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "WED"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_protons_cd_influence_bio_model_MCN ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_influence_bio_model_MCN(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "MCN"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_protons_cd_influence_bio_model_CAR ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_influence_bio_model_CAR(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "CAR"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:193: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_protons_cd_influence_bio_model_LSM ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_influence_bio_model_LSM(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "LSM"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:203: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________________ test_helium_cd_influence ___________________________

test_data_helium = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_helium_cd_influence(test_data_helium):
        pln, ct, cst, stf, dij, result = test_data_helium
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
___________________ test_helium_cd_influence_bio_model_none ____________________

test_data_helium = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_helium_cd_influence_bio_model_none(test_data_helium):
        pln, ct, cst, stf, dij, result = test_data_helium
        pln.bio_model = "none"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
____________________ test_helium_cd_influence_bio_model_LSM ____________________

test_data_helium = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_helium_cd_influence_bio_model_LSM(test_data_helium):
        pln, ct, cst, stf, dij, result = test_data_helium
        pln.bio_model = "LSM"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
___________________ test_carbon_cd_influence_bio_model_none ____________________

test_data_carbon = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_carbon_cd_influence_bio_model_none(test_data_carbon):
        pln, ct, cst, stf, dij, result = test_data_carbon
        pln.bio_model = "none"
        pln
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________________ test_oxygen_cd_influence ___________________________

test_data_oxygen = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'iso_c...0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), ...})

    def test_oxygen_cd_influence(test_data_oxygen):
        pln, ct, cst, stf, dij, result = test_data_oxygen
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:252: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
______ test_carbon_cd_influence_bio_model_unavailable_keeps_physical_dose ______

test_data_carbon = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51a1a7c7a0>

    def test_carbon_cd_influence_bio_model_unavailable_keeps_physical_dose(test_data_carbon, caplog):
        """Without calc_bio_dose=True, an unavailable model leaves a physical-dose dij."""
        pln, ct, cst, stf, dij, result = test_data_carbon
        pln.bio_model = "LSM"
    
        with caplog.at_level("WARNING"):
>           dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb.py:275: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'LSM' cannot be evaluated for this machine and these structures: Biological model 'LSM' not available: Required quantities not provided by Dose Engine or selected Machine Dataset: let. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_________________ test_carbon_multi_gaussian_reduces_to_single _________________

test_data_carbon = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51a1773620>

    def test_carbon_multi_gaussian_reduces_to_single(test_data_carbon, monkeypatch):
        """A multi-Gaussian kernel with all extra weights zero equals the single-Gaussian model."""
        from pathlib import Path
    
        import pyRadPlan
        from pyRadPlan.dose.engines._base import DoseEngineBase
        from pyRadPlan.machines import load_machine_from_mat, validate_machine
    
        pln, ct, cst, stf, dij, result = test_data_carbon
        machine_file = Path(pyRadPlan.__file__).parent / "data" / "machines" / "carbon_Generic.mat"
        machine = validate_machine(load_machine_from_mat(machine_file))
        for kernel in machine.pb_kernels.values():
            kernel.sigma_multi = np.stack([kernel.sigma, 2 * kernel.sigma, 3 * kernel.sigma])
            kernel.weight_multi = np.zeros((2, kernel.depths.shape[0]))
        assert machine.has_multi_gaussian_kernel
        monkeypatch.setattr(DoseEngineBase, "load_machine", staticmethod(lambda *_: machine))
    
        pln.prop_dose_calc["lateral_model"] = "single"
>       dose_single = calc_dose_influence(ct, cst, stf, pln).physical_dose.flat[0].toarray()

test/dose/test_hongpb.py:312: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________ test_protons_cd_forward_uses_loaded_plan_machine _______________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_cd_forward_uses_loaded_plan_machine(test_data_protons):
        from pyRadPlan.machines import load_from_name
    
        pln, ct, cst, stf, dij, result = test_data_protons
>       reference = sitk.GetArrayFromImage(calc_dose_forward(ct, cst, stf, pln)["physical_dose"])

test/dose/test_hongpb.py:323: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_____________________ test_protons_WED_alpha_beta_matrices _____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_WED_alpha_beta_matrices(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "WED"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:229: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_____________________ test_protons_MCN_alpha_beta_matrices _____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_MCN_alpha_beta_matrices(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "MCN"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:242: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_____________________ test_helium_HEL_alpha_beta_matrices ______________________

test_data_helium = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_helium_HEL_alpha_beta_matrices(test_data_helium):
        pln, ct, cst, stf, dij, result = test_data_helium
        pln.bio_model = "HEL"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:255: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base_pencilbeam_particle:_base_pencilbeam_particle.py:124 Chosen Machine does not support a single Gaussian Pencil-Beam model!
__________________________ test_protons_constant_rbe ___________________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_constant_rbe(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "constant_rbe"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:267: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
________________ test_protons_constant_rbe_parameter_from_plan _________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_constant_rbe_parameter_from_plan(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = {"model": "constant_rbe", "rbe": 1.0}
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:283: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________ test_calc_bio_dose_off_keeps_let_for_on_the_fly_models ____________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_calc_bio_dose_off_keeps_let_for_on_the_fly_models(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "WED"
        pln.prop_dose_calc = {"calc_bio_dose": False}
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:291: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.6793786 , -1.8561553 ],
        [ 0.98141724,  1.0012388 ],
        [ 0.8396893 , -0.92807764]],

       [... 1.8561553 ],
        [ 0.98141724,  1.0012388 ],
        [-0.8396893 ,  0.92807764]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_bio_dose
________________ test_calc_let_off_still_feeds_let_based_model _________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_calc_let_off_still_feeds_let_based_model(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "WED"
        pln.prop_dose_calc = {"calc_let": False}
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:301: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.6793786 , -1.8561553 ],
        [ 0.98141724,  1.0012388 ],
        [ 0.8396893 , -0.92807764]],

       [... 1.8561553 ],
        [ 0.98141724,  1.0012388 ],
        [-0.8396893 ,  0.92807764]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: calc_let
_______________ test_protons_bio_model_none_has_no_bio_matrices ________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_protons_bio_model_none_has_no_bio_matrices(test_data_protons):
        pln, ct, cst, stf, dij, result = test_data_protons
        pln.bio_model = "none"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:318: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________ test_carbon_kernel_based_lq_alpha_beta_matrices ________________

test_data_carbon = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})

    def test_carbon_kernel_based_lq_alpha_beta_matrices(test_data_carbon):
        pln, ct, cst, stf, dij, result = test_data_carbon
        pln.bio_model = "kernel_based_lq"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:328: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
__________________ test_carbon_tabulated_alpha_beta_matrices ___________________

test_data_carbon = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
      dtype=uint8), ...})
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51a17af700>

    def test_carbon_tabulated_alpha_beta_matrices(test_data_carbon, monkeypatch):
        pln, ct, cst, stf, dij, result = test_data_carbon
        machine_file = Path(pyRadPlan.__file__).parent / "data" / "machines" / "carbon_Generic.mat"
        machine = validate_machine(load_machine_from_mat(machine_file))
        _attach_synthetic_spectra(machine)
        assert "fluence" in machine.provided_quantities()
        monkeypatch.setattr(DoseEngineBase, "load_machine", staticmethod(lambda *_: machine))
    
        pln.bio_model = "dose_average_alpha_beta"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:408: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
________________ test_bio_tissue_arrays_use_the_selected_device ________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_bio_tissue_arrays_use_the_selected_device(test_data_protons):
        """Tissue inputs are created on the engine's device, not the namespace default one."""
        import array_api_compat
        import array_api_strict
    
        pln, ct, cst, stf, _dij, _result = test_data_protons
        pln.bio_model = "WED"
        pln.prop_dose_calc["calc_bio_dose"] = False
    
>       device = array_api_strict.Device("device1")

test/dose/test_hongpb_bio.py:445: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_ test_out_of_domain_reference_parameters_without_bio_request_keep_physical_dose _

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_out_of_domain_reference_parameters_without_bio_request_keep_physical_dose(
        test_data_protons,
    ):
        """Outside the model's domain, only a requested biological dose is an error."""
        pln, ct, cst, stf, _dij, _result = test_data_protons
        pln.bio_model = "MCN"
        _with_reference_params(cst, beta_x=0.0)
    
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:492: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'MCN' cannot be evaluated for this machine and these structures: Target: Biological model 'MCN' is only defined for beta_x > 0, but the structure has beta_x = 0.0. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
____________________ test_bio_influence_matrices_are_finite ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_bio_influence_matrices_are_finite(test_data_protons):
        """No NaN/Inf ever reaches the stored biological influence matrices."""
        pln, ct, cst, stf, _dij, _result = test_data_protons
        pln.bio_model = "MCN"
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_hongpb_bio.py:527: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___ test_cutoff_matches_scalar_reference[array_api_strict-1-integral-single] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'single', method = 'integral', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_cutoff_matches_scalar_reference[array_api_strict-1-integral-double] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'double', method = 'integral', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_cutoff_matches_scalar_reference[array_api_strict-1-integral-multi] ____

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi', method = 'integral', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_cutoff_matches_scalar_reference[array_api_strict-1-integral-multi_1d] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi_1d', method = 'integral', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_cutoff_matches_scalar_reference[array_api_strict-1-integral-singleXY] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'singleXY', method = 'integral', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_cutoff_matches_scalar_reference[array_api_strict-1-relative-single] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'single', method = 'relative', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_cutoff_matches_scalar_reference[array_api_strict-1-relative-double] ___

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'double', method = 'relative', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
___ test_cutoff_matches_scalar_reference[array_api_strict-1-relative-multi] ____

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi', method = 'relative', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_cutoff_matches_scalar_reference[array_api_strict-1-relative-multi_1d] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi_1d', method = 'relative', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__ test_cutoff_matches_scalar_reference[array_api_strict-1-relative-singleXY] __

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'singleXY', method = 'relative', max_samples = 1

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-single] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'single', method = 'integral', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-double] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'double', method = 'integral', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-multi] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi', method = 'integral', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-multi_1d] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi_1d', method = 'integral', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-singleXY] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'singleXY', method = 'integral', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-single] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'single', method = 'relative', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-double] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'double', method = 'relative', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-multi] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi', method = 'relative', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-multi_1d] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'multi_1d', method = 'relative', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-singleXY] _

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
model = 'singleXY', method = 'relative', max_samples = 100000

    @pytest.mark.parametrize("model", ["single", "double", "multi", "multi_1d", "singleXY"])
    @pytest.mark.parametrize("method", ["integral", "relative"])
    @pytest.mark.parametrize("max_samples", [1, 100_000])
    def test_cutoff_matches_scalar_reference(xp, model, method, max_samples):
        """Batching preserves radii, compensation and numerical integration error."""
        engine, kernel, host_grid = _inputs(model)
        depths = np.array([0.0, 1.2, 4.5, 8.0])
        idd = 1.6021766208e-02 * np.interp(depths, kernel["depths"], kernel["idd"])
        cutoff = 0.95
        expected = []
        for depth, reference_idd in zip(depths, idd):
            dose = _scalar_reference(engine, kernel, host_grid, depth, 0.4)
            area = np.cumsum(host_grid.radii * dose * host_grid.dr * host_grid.angular_weights)
            condition = (
                area >= reference_idd * cutoff
                if method == "integral"
                else dose <= (1 - cutoff) * dose.max()
            )
            ix = condition.argmax()
            comp = cutoff**-1 if method == "integral" else (area[ix] / area[-1]) ** -1
            expected.append([host_grid.radii[ix], comp, abs(area[-1] / reference_idd - 1) * 100])
        backend_kernel = {
            key: to_namespace(xp, value) if isinstance(value, np.ndarray) else value
            for key, value in kernel.items()
        }
        grid = host_grid.to_namespace(xp)
        batches = []
    
        def profile(batch, quadrature):
            batches.append(batch.shape[0])
            return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
    
>       actual = evaluate_cutoff(
            xp.asarray(depths),
            xp.asarray(idd),
            grid,
            profile,
            cutoff=cutoff,
            method=method,
            max_samples=max_samples,
        )

test/dose/test_particle_cutoff.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_particle_cutoff.py:207: in evaluate_cutoff
    dose = dose_profile(depths[start:stop], quadrature)
test/dose/test_particle_cutoff.py:135: in profile
    return engine._cutoff_dose_profile(batch, backend_kernel, 0.4, quadrature)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_hongpb.py:112: in _cutoff_dose_profile
    kernels = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.7, 3.7, 8.7], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 1], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 1] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
____________ test_quadrature_respects_explicit_and_inferred_device _____________

    def test_quadrature_respects_explicit_and_inferred_device():
        """Native creation and custom boundaries stay on a non-default logical device."""
        xp = pytest.importorskip("array_api_strict")
>       device = xp.Device("device1")

test/dose/test_particle_cutoff.py:281: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_particle_support_selection[strict2023-table-False-empty] _________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'empty', optional_fields = False, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = empty((0,), dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________ test_particle_support_selection[strict2023-table-False-none] _________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'none', optional_fields = False, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = Array([0, 0, 0, 0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 0 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________ test_particle_support_selection[strict2023-table-False-all] __________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'all', optional_fields = False, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = Array([0, 0, 0, 0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 0 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
________ test_particle_support_selection[strict2023-table-False-mixed] _________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'mixed', optional_fields = False, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = Array([0, 0, 0, 0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 0 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________ test_particle_support_selection[strict2023-table-True-empty] _________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'empty', optional_fields = True, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = empty((0,), dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________ test_particle_support_selection[strict2023-table-True-none] __________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'none', optional_fields = True, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = Array([0, 0, 0, 0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 0 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__________ test_particle_support_selection[strict2023-table-True-all] __________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'all', optional_fields = True, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = Array([0, 0, 0, 0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 0 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_________ test_particle_support_selection[strict2023-table-True-mixed] _________

xp = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
selection = 'mixed', optional_fields = True, cutoff = 'table'

    @pytest.mark.parametrize("selection", ["empty", "none", "all", "mixed"])
    @pytest.mark.parametrize("optional_fields", [False, True])
    @pytest.mark.parametrize("cutoff", ["full", "constant", "table"])
    def test_particle_support_selection(xp, selection, optional_fields, cutoff):
        """Select physical rows, preserving unsorted voxel IDs and field shapes/dtypes."""
        engine = object.__new__(ParticleHongPencilBeamEngine)
        engine.dosimetric_lateral_cutoff = 1.0 if cutoff == "full" else 0.995
        engine._calc_bio_dose = optional_fields
        n = 0 if selection == "empty" else 5
        depths = np.array([2, 6, 1, 3, 0], dtype=np.float32)[:n]
        radial = np.array([1, 1, 9, 4, 1], dtype=np.float32)[:n]
        if selection == "none":
            depths[:] = 99
        elif selection == "all":
            depths[:] = 2
            radial[:] = 1
        source = {
            "ix": np.array([29, 8, 17, 42, 3], dtype=np.int64)[:n],
            "rad_depths": depths,
            "radial_dist_sq": radial,
        }
        if optional_fields:
            source.update(
                lat_dists=np.arange(n * 2, dtype=np.float32).reshape(n, 2),
                v_alpha_x=np.arange(n, dtype=np.float64).reshape(n, 1),
                v_beta_x=np.arange(n, dtype=np.float32),
                v_tissue_class_ix=np.array([2, 0, 1, 2, 1], dtype=np.int32)[:n],
            )
        ray = {name: xp.asarray(value) for name, value in source.items()}
        cut_off = [2.0, 2.0] if cutoff == "table" else [2.0]
        bixel = {
            "rad_depth_offset": 0.5,
            "kernel": {
                "offset": 1.5,
                "depths": xp.asarray([0.0, 3.0]),
                "lateral_cut_off": {
                    "depths": xp.asarray([0.0, 3.0]),
                    "cut_off": xp.asarray(cut_off),
                },
            },
        }
        if selection in ("empty", "none"):
            selected = []
        elif selection == "all":
            selected = list(range(n))
        else:
            selected = {"full": [0, 2, 3, 4], "constant": [0, 3, 4], "table": [0, 3]}[cutoff]
    
>       result = engine._get_bixel_indices_on_ray(bixel, ray)

test/dose/test_particle_indexing.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:425: in _get_bixel_indices_on_ray
    interp_vals = array_interp(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:413: in interp1d
    return _interpolation(xq, x, y, left, right)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([1., 4.], dtype=array_api_strict.float64)
key = Array([0, 0, 0, 0, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 0 0 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__________ test_translated_depths_and_cache[float32-array_api_strict] __________

backend = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float32'

    @pytest.mark.parametrize("backend", ["numpy", "torch", "array_api_strict"])
    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    def test_translated_depths_and_cache(backend, dtype):
        """Compare translated field values and invalidate preparation with geometry."""
        backend = pytest.importorskip(backend)
        xp = array_api_compat.array_namespace(backend.asarray([0.0]))
        engine = engine_stub()
        kernel = {
            "depths": xp.asarray([0.0, 0.1, 0.8, 2.0], dtype=getattr(xp, dtype)),
            "offset": 0.03217,
            "idd": xp.asarray([1.0, 2.0, 3.0, 0.2], dtype=getattr(xp, dtype)),
            "sigma": xp.asarray([0.1, 0.2, 0.4, 0.7], dtype=getattr(xp, dtype)),
            "let": xp.asarray([2.0, 4.0, 5.0, 0.0], dtype=getattr(xp, dtype)),
        }
        prepare = Mock(wraps=engine._prepare_depth_interpolator)
        engine._prepare_depth_interpolator = prepare
        for offset in (0.0, 0.134, -0.23, 8.5):
            grid = kernel["depths"] + kernel["offset"] - offset
            q = xp.concat([grid, grid + 0.01, grid - 0.01])
            bixel = dict(kernel=kernel, rad_depths=q, rad_depth_offset=offset)
>           expected = interp1d(
                q,
                grid,
                {
                    "idd": kernel["idd"] * 1.6021766208e-02,
                    "sigma": kernel["sigma"],
                    "let": kernel["let"],
                },
            )

test/dose/test_particle_interpolators.py:48: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.03217, 0.13217, 0.83217, 2.03217], dtype=array_api_strict.float32)
key = Array([0, 0, 1, 2, 0, 1, 2, 2, 0, 0, 1, 2], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 1 2 2 0 0 1 2] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
__________ test_translated_depths_and_cache[float64-array_api_strict] __________

backend = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype = 'float64'

    @pytest.mark.parametrize("backend", ["numpy", "torch", "array_api_strict"])
    @pytest.mark.parametrize("dtype", ["float32", "float64"])
    def test_translated_depths_and_cache(backend, dtype):
        """Compare translated field values and invalidate preparation with geometry."""
        backend = pytest.importorskip(backend)
        xp = array_api_compat.array_namespace(backend.asarray([0.0]))
        engine = engine_stub()
        kernel = {
            "depths": xp.asarray([0.0, 0.1, 0.8, 2.0], dtype=getattr(xp, dtype)),
            "offset": 0.03217,
            "idd": xp.asarray([1.0, 2.0, 3.0, 0.2], dtype=getattr(xp, dtype)),
            "sigma": xp.asarray([0.1, 0.2, 0.4, 0.7], dtype=getattr(xp, dtype)),
            "let": xp.asarray([2.0, 4.0, 5.0, 0.0], dtype=getattr(xp, dtype)),
        }
        prepare = Mock(wraps=engine._prepare_depth_interpolator)
        engine._prepare_depth_interpolator = prepare
        for offset in (0.0, 0.134, -0.23, 8.5):
            grid = kernel["depths"] + kernel["offset"] - offset
            q = xp.concat([grid, grid + 0.01, grid - 0.01])
            bixel = dict(kernel=kernel, rad_depths=q, rad_depth_offset=offset)
>           expected = interp1d(
                q,
                grid,
                {
                    "idd": kernel["idd"] * 1.6021766208e-02,
                    "sigma": kernel["sigma"],
                    "let": kernel["let"],
                },
            )

test/dose/test_particle_interpolators.py:48: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:411: in interp1d
    return {key: _interpolation(xq, x, ytmp, left, right) for key, ytmp in y.items()}
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/compat.py:117: in _interp1d_core
    x0 = x[i0]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([0.03217, 0.13217, 0.83217, 2.03217], dtype=array_api_strict.float64)
key = Array([0, 0, 1, 2, 0, 1, 2, 2, 0, 0, 1, 2], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 0 1 2 0 1 2 2 0 0 1 2] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
______________ test_reused_engine_rebuilds_prepared_state[False] _______________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})
trace_on_dose_grid = False

    @pytest.mark.parametrize("trace_on_dose_grid", [False, True])
    def test_reused_engine_rebuilds_prepared_state(test_data_protons, trace_on_dose_grid):
        """Rebuild every per-calculation cache on engine reuse for both tracing grids."""
        pln, ct, cst, stf, _, _ = test_data_protons
        engine = ParticleHongPencilBeamEngine(pln)
        engine.console_progress = False
        engine.trace_on_dose_grid = trace_on_dose_grid
>       first = engine.calc_dose_influence(ct, cst, stf)

test/dose/test_particle_interpolators.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________ test_reused_engine_rebuilds_prepared_state[True] _______________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})
trace_on_dose_grid = True

    @pytest.mark.parametrize("trace_on_dose_grid", [False, True])
    def test_reused_engine_rebuilds_prepared_state(test_data_protons, trace_on_dose_grid):
        """Rebuild every per-calculation cache on engine reuse for both tracing grids."""
        pln, ct, cst, stf, _, _ = test_data_protons
        engine = ParticleHongPencilBeamEngine(pln)
        engine.console_progress = False
        engine.trace_on_dose_grid = trace_on_dose_grid
>       first = engine.calc_dose_influence(ct, cst, stf)

test/dose/test_particle_interpolators.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
______________ test_particle_beams_use_their_own_lateral_cutoffs _______________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_particle_beams_use_their_own_lateral_cutoffs(test_data_protons):
        # Lateral cutoffs are recomputed per beam from its SSDs; a later beam must not reuse
        # kernels cached with an earlier beam's cutoffs. Off-center, the beams' SSDs differ.
        pln, ct, cst, stf, _, _ = test_data_protons
        assert len(stf.beams) > 1
        stf = _shifted_stf(stf, np.array([0.0, -3.0, 0.0]))
>       dij = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_particle_lateral_cutoff.py:23: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806892,  1.0004952],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806892,  1.0004952],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_________ test_ssd_metadata_transferred_in_batches[array_api_strict-1] _________

engine = <pyRadPlan.dose.engines._hongpb.ParticleHongPencilBeamEngine object at 0x7f51a3307c50>
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51ae51d940>
rays = 1

    @pytest.mark.parametrize("rays", [1, 7])
    def test_ssd_metadata_transferred_in_batches(engine, monkeypatch, rays):
        """Transfer SSD values and validity flags once each, independent of ray count."""
        xp = engine.xp
        beam = {
            "num_of_rays": rays,
            "sad": 1000.0,
            "iso_center": xp.zeros(3, dtype=xp.float32),
            "source_point": xp.asarray([0.0, -1000.0, 0.0], dtype=xp.float32),
            "rays": [
                {
                    "ray_pos_bev": xp.asarray([float(i), 0.0, 0.0], dtype=xp.float32),
                    "target_point": xp.asarray([float(i), 1000.0, 0.0], dtype=xp.float32),
                }
                for i in range(rays)
            ],
        }
        alpha = xp.asarray(np.tile([0.1, 0.3, 0.8], (rays, 1)), dtype=xp.float32)
        rho = xp.asarray(np.tile([0.0, 0.5, 1.0], (rays, 1)), dtype=xp.float32)
        d12 = xp.asarray(np.arange(rays) + 2000, dtype=xp.float32)
        engine._raytracer = SimpleNamespace(trace_rays=lambda *args: (alpha, None, [rho], d12, None))
        transfers = []
    
        def download(value):
            transfers.append(value.shape)
            return to_numpy(value)
    
        monkeypatch.setattr("pyRadPlan.dose.engines._base_pencilbeam.to_numpy", download)
>       engine._compute_ssd({"beam": beam}, None)

test/dose/test_ray_geometry_preparation.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:717: in _compute_ssd
    alpha = to_namespace(xp, alpha, device=self.device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_________ test_ssd_metadata_transferred_in_batches[array_api_strict-7] _________

engine = <pyRadPlan.dose.engines._hongpb.ParticleHongPencilBeamEngine object at 0x7f51b0178b90>
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51ae51d470>
rays = 7

    @pytest.mark.parametrize("rays", [1, 7])
    def test_ssd_metadata_transferred_in_batches(engine, monkeypatch, rays):
        """Transfer SSD values and validity flags once each, independent of ray count."""
        xp = engine.xp
        beam = {
            "num_of_rays": rays,
            "sad": 1000.0,
            "iso_center": xp.zeros(3, dtype=xp.float32),
            "source_point": xp.asarray([0.0, -1000.0, 0.0], dtype=xp.float32),
            "rays": [
                {
                    "ray_pos_bev": xp.asarray([float(i), 0.0, 0.0], dtype=xp.float32),
                    "target_point": xp.asarray([float(i), 1000.0, 0.0], dtype=xp.float32),
                }
                for i in range(rays)
            ],
        }
        alpha = xp.asarray(np.tile([0.1, 0.3, 0.8], (rays, 1)), dtype=xp.float32)
        rho = xp.asarray(np.tile([0.0, 0.5, 1.0], (rays, 1)), dtype=xp.float32)
        d12 = xp.asarray(np.arange(rays) + 2000, dtype=xp.float32)
        engine._raytracer = SimpleNamespace(trace_rays=lambda *args: (alpha, None, [rho], d12, None))
        transfers = []
    
        def download(value):
            transfers.append(value.shape)
            return to_numpy(value)
    
        monkeypatch.setattr("pyRadPlan.dose.engines._base_pencilbeam.to_numpy", download)
>       engine._compute_ssd({"beam": beam}, None)

test/dose/test_ray_geometry_preparation.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:717: in _compute_ssd
    alpha = to_namespace(xp, alpha, device=self.device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:797: in to_namespace
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_________________ test_nominal_set_has_one_flat_slot[protons] __________________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_nominal_set_has_one_flat_slot(case):
        pln, ct, cst, stf = case
>       engine, dij, slots = _calc(pln, ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:104: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_________________ test_nominal_set_has_one_flat_slot[photons] __________________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_nominal_set_has_one_flat_slot(case):
        pln, ct, cst, stf = case
>       engine, dij, slots = _calc(pln, ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:104: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_______________ test_scenarios_are_recorded_on_the_dij[protons] ________________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_scenarios_are_recorded_on_the_dij(case):
        pln, ct, cst, stf = case
        engine = get_engine(pln)
>       dij = engine.calc_dose_influence(ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:116: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________ test_scenarios_are_recorded_on_the_dij[photons] ________________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_scenarios_are_recorded_on_the_dij(case):
        pln, ct, cst, stf = case
        engine = get_engine(pln)
>       dij = engine.calc_dose_influence(ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:116: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
___________________ test_setup_and_range_scenarios[protons] ____________________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51a196bee0>

    def test_setup_and_range_scenarios(case, monkeypatch):
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, (0.0, 0.0, 0.0)],
            range_=[(0.0, 0.0), (0.0, 0.0), RANGE_ABS],
        )
    
>       engine, dij, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:128: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/3 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_setup_and_range_scenarios[photons] ____________________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51a196b8c0>

    def test_setup_and_range_scenarios(case, monkeypatch):
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, (0.0, 0.0, 0.0)],
            range_=[(0.0, 0.0), (0.0, 0.0), RANGE_ABS],
        )
    
>       engine, dij, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:128: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/3 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_______________ test_proton_range_shift_pulls_the_dose_upstream ________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_proton_range_shift_pulls_the_dose_upstream(test_data_protons):
        """A larger radiological depth stops the protons earlier, closer to the source."""
        pln, ct, cst, stf, _, _ = test_data_protons
        scen_set = _scenario_set(range_=[(0.0, 0.0), (10.0, 0.0)])
>       _, dij, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:159: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_shifts_do_not_accumulate[protons] ____________________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_shifts_do_not_accumulate(case):
        pln, ct, cst, stf = case
        iso_before = [beam.iso_center.copy() for beam in stf.beams]
        dump_before = stf.model_dump()
    
        scen_set = _scenario_set(setup=[SHIFT_X, SHIFT_Y, (0.0, 0.0, 0.0), SHIFT_X])
>       _, _, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.873833 , -1.661701 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.49...33 ,  1.661701 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/4 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
____________________ test_shifts_do_not_accumulate[photons] ____________________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_shifts_do_not_accumulate(case):
        pln, ct, cst, stf = case
        iso_before = [beam.iso_center.copy() for beam in stf.beams]
        dump_before = stf.model_dump()
    
        scen_set = _scenario_set(setup=[SHIFT_X, SHIFT_Y, (0.0, 0.0, 0.0), SHIFT_X])
>       _, _, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.873833  , -1.661701  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.661701  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/4 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_________ test_ct_phase_with_moved_outline_matches_standalone[protons] _________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_ct_phase_with_moved_outline_matches_standalone(case):
        """SSD-dependent quantities follow the phase, also when phase 0 is not computed."""
        pln, ct, cst, stf = case
        ct2, ct_phase1 = _two_phase_ct_moved_outline(ct)
    
>       _, _, nominal = _calc(pln, ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_________ test_ct_phase_with_moved_outline_matches_standalone[photons] _________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_ct_phase_with_moved_outline_matches_standalone(case):
        """SSD-dependent quantities follow the phase, also when phase 0 is not computed."""
        pln, ct, cst, stf = case
        ct2, ct_phase1 = _two_phase_ct_moved_outline(ct)
    
>       _, _, nominal = _calc(pln, ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_______________ test_ct_phase_selection_bio_single_and_reordered _______________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_ct_phase_selection_bio_single_and_reordered(test_data_protons):
        pln, ct, cst, stf, _, _ = test_data_protons
        pln.bio_model = "MCN"
        ct2, ct_phase1 = _two_phase_ct_moved_outline(ct)
>       _, ref, _ = _calc(pln, ct_phase1, cst, stf)

test/dose/test_scenarios_pencilbeam.py:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________________ test_ct_phase_selection[protons] _______________________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_ct_phase_selection(case):
        pln, ct, cst, stf = case
        ct2, ct_phase1 = _two_phase_ct(ct)
        assert ct2.num_of_ct_scen == 2
    
        scen_set = _scenario_set(ct_phase=[1, 0], phases=(0, 1))
>       _, dij, slots = _calc(pln, ct2, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:265: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________________ test_ct_phase_selection[photons] _______________________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_ct_phase_selection(case):
        pln, ct, cst, stf = case
        ct2, ct_phase1 = _two_phase_ct(ct)
        assert ct2.num_of_ct_scen == 2
    
        scen_set = _scenario_set(ct_phase=[1, 0], phases=(0, 1))
>       _, dij, slots = _calc(pln, ct2, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:265: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
____________________ test_two_phase_nominal_model_validates ____________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_two_phase_nominal_model_validates(test_data_protons):
        pln, ct, cst, stf, _, _ = test_data_protons
        ct2, _ = _two_phase_ct(ct)
        pln.mult_scen = {"model": "nomScen", "ctScenProb": [(1, 0.5), (2, 0.5)]}
    
        engine = get_engine(pln)
>       dij = engine.calc_dose_influence(ct2, cst, stf)

test/dose/test_scenarios_pencilbeam.py:304: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________ test_derived_scenarios_are_not_computed[protons] _______________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_derived_scenarios_are_not_computed(case):
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, (0.0, 0.0, 0.0)],
            bases=[None, None, ScenarioBasis(coefficients={0: 1.0})],
        )
>       engine, dij, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:318: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______________ test_derived_scenarios_are_not_computed[photons] _______________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_derived_scenarios_are_not_computed(case):
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, (0.0, 0.0, 0.0)],
            bases=[None, None, ScenarioBasis(coefficients={0: 1.0})],
        )
>       engine, dij, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:318: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_______________________ test_explicit_model_is_accepted ________________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_explicit_model_is_accepted(test_data_protons):
        pln, ct, cst, stf, _, _ = test_data_protons
        scen_set = _scenario_set(setup=[(0.0, 0.0, 0.0), SHIFT_X])
        pln.mult_scen = ExplicitScenarioModel.from_set(scen_set)
    
        engine = get_engine(pln)
>       dij = engine.calc_dose_influence(ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:333: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
__________________ test_public_entry_point_scenario_override ___________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_public_entry_point_scenario_override(test_data_protons):
        pln, ct, cst, stf, _, _ = test_data_protons
        model_before = pln.mult_scen.model_dump()
        scen_set = _scenario_set(setup=[(0.0, 0.0, 0.0), SHIFT_X])
    
>       dij = calc_dose_influence(ct, cst, stf, pln, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:383: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
___________________ test_forward_dose_returns_every_scenario ___________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_forward_dose_returns_every_scenario(test_data_photons):
        """The unsuffixed result is the nominal scenario, the list holds one image per slot."""
        pln, ct, cst, stf, _, _ = test_data_photons
        w = np.ones(stf.total_number_of_bixels)
        # the nominal scenario is deliberately not the first slot
        scen_set = _scenario_set(setup=[SHIFT_X, (0.0, 0.0, 0.0)])
    
>       result = calc_dose_forward(ct, cst, stf, pln, w, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.873833  , -1.661701  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.661701  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
___________ test_forward_dose_single_scenario_has_no_scenario_lists ____________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_forward_dose_single_scenario_has_no_scenario_lists(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        w = np.ones(stf.total_number_of_bixels)
        # a derived (alias) scenario does not count as a second computed one
        alias_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), (0.0, 0.0, 0.0)],
            bases=[None, ScenarioBasis(coefficients={0: 1.0})],
        )
>       result = calc_dose_forward(ct, cst, stf, pln, w, scenarios=alias_set)

test/dose/test_scenarios_pencilbeam.py:431: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
__________________ test_shift_scenarios_in_biological_results __________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_shift_scenarios_in_biological_results(test_data_protons):
        """Result assembly reads the reference alpha/beta of every shift scenario correctly."""
>       dij, refs = _bio_shift_dijs(test_data_protons)

test/dose/test_scenarios_pencilbeam.py:455: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:448: in _bio_shift_dijs
    _, dij, _ = _calc(pln, ct, cst, stf, scenarios=_scenario_set(setup=BIO_SHIFTS))
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.873833 , -1.661701 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.49...33 ,  1.661701 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/4 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
________________ test_shift_scenarios_in_biological_quantities _________________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_shift_scenarios_in_biological_quantities(test_data_protons):
        """RBExDose and the indirect AlphaDose / SqrtBetaDose paths per scenario."""
        from pyRadPlan.core.xp_utils import to_numpy
        from pyRadPlan.quantities import QuantityResolver
    
>       dij, refs = _bio_shift_dijs(test_data_protons)

test/dose/test_scenarios_pencilbeam.py:469: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:448: in _bio_shift_dijs
    _, dij, _ = _calc(pln, ct, cst, stf, scenarios=_scenario_set(setup=BIO_SHIFTS))
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.873833 , -1.661701 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.49...33 ,  1.661701 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/4 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_____________ test_progress_counts_the_computed_scenarios[protons] _____________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_progress_counts_the_computed_scenarios(case):
        from pyRadPlan.core import ProgressReport, observe_reports
    
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, SHIFT_Y, (0.0, 0.0, 0.0)],
            bases=[None, None, None, ScenarioBasis(coefficients={0: 1.0})],
        )
        reports = []
        with observe_reports(reports.append):
>           _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:500: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/3 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_____________ test_progress_counts_the_computed_scenarios[photons] _____________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_progress_counts_the_computed_scenarios(case):
        from pyRadPlan.core import ProgressReport, observe_reports
    
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, SHIFT_Y, (0.0, 0.0, 0.0)],
            bases=[None, None, None, ScenarioBasis(coefficients={0: 1.0})],
        )
        reports = []
        with observe_reports(reports.append):
>           _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:500: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/3 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
____________ test_computed_scenarios_share_one_index_array[protons] ____________

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_computed_scenarios_share_one_index_array(case):
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, (0.0, 0.0, 0.0)],
            range_=[(0.0, 0.0), (0.0, 0.0), RANGE_ABS],
        )
        pln.prop_dose_calc["share_scenario_structure"] = False
>       _, _, separate = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:526: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/3 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: share_scenario_structure
____________ test_computed_scenarios_share_one_index_array[photons] ____________

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_computed_scenarios_share_one_index_array(case):
        pln, ct, cst, stf = case
        scen_set = _scenario_set(
            setup=[(0.0, 0.0, 0.0), SHIFT_X, (0.0, 0.0, 0.0)],
            range_=[(0.0, 0.0), (0.0, 0.0), RANGE_ABS],
        )
        pln.prop_dose_calc["share_scenario_structure"] = False
>       _, _, separate = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:526: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/3 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: share_scenario_structure
_________ test_negative_range_shift_and_a_set_without_range_dimension __________

test_data_protons = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... 0., 0., ..., 0., 0., 0.]]], shape=(20, 10, 10)), 'MU': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=uint8), ...})

    def test_negative_range_shift_and_a_set_without_range_dimension(test_data_protons):
        pln, ct, cst, stf, _, _ = test_data_protons
>       _, _, nominal = _calc(pln, ct, cst, stf)

test/dose/test_scenarios_pencilbeam.py:542: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
________ test_rays_missing_the_patient_take_the_ssd_of_their_neighbors _________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_rays_missing_the_patient_take_the_ssd_of_their_neighbors(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        scen_set = _scenario_set(setup=[(0.0, 0.0, 0.0), (0.0, 0.0, 60.0)])
        with pytest.warns(UserWarning, match="do not hit patient"):
>           _, _, slots = _calc(pln, ct, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:557: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError

During handling of the above exception, another exception occurred:

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_rays_missing_the_patient_take_the_ssd_of_their_neighbors(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        scen_set = _scenario_set(setup=[(0.0, 0.0, 0.0), (0.0, 0.0, 60.0)])
>       with pytest.warns(UserWarning, match="do not hit patient"):
E       Failed: DID NOT WARN. No warnings of type (<class 'UserWarning'>,) matching the regex were emitted.
E        Regex: do not hit patient
E        Emitted warnings: [ UserWarning('Property "lateral_model" not found in PhotonPencilBeamSVDEngine!'),
E         UserWarning('Requested GPU device is not available on array_api_strict. Falling back to CPU.'),
E         UserWarning('Requested GPU device is not available on array_api_strict. Falling back to CPU.'),
E         RuntimeWarning('divide by zero encountered in divide'),
E         RuntimeWarning('divide by zero encountered in divide')].

test/dose/test_scenarios_pencilbeam.py:556: Failed
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
____________ test_public_api_accepts_a_3d_structure_set_on_a_4d_ct _____________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_public_api_accepts_a_3d_structure_set_on_a_4d_ct(test_data_photons):
        from pyRadPlan import generate_stf
    
        pln, ct, cst, stf, _, _ = test_data_photons
        ct2, ct_phase1 = _two_phase_ct(ct)
        scen_set = _scenario_set(ct_phase=[1], phases=(0, 1))
    
>       dij = calc_dose_influence(ct2, cst, stf, pln, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:574: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_______ test_4d_structure_set_gives_the_same_influence_matrices[protons] _______

case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 2, 1, 1]), num_of_rays=5, total_number_of_bixels=6)]))

    def test_4d_structure_set_gives_the_same_influence_matrices(case):
        from pyRadPlan.cst import validate_cst
    
        pln, ct, cst, stf = case
        ct2, _ = _two_phase_ct(ct)
        cst4d = validate_cst(cst, ct=ct2)
        assert cst4d.num_of_scenarios == 2
    
        scen_set = _scenario_set(ct_phase=[0, 1], phases=(0, 1))
>       _, _, with_3d = _calc(pln, ct2, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:594: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam_particle.py:558: in _init_beam
    beam_info = super()._init_beam(dij, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767 , -1.767767 ],
        [ 0.9806835,  1.0004953],
        [ 0.8838835, -0.8838835]],

       [[ 2.35...67 ,  1.767767 ],
        [ 0.9806835,  1.0004953],
        [-0.8838835,  0.8838835]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine HongPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
_______ test_4d_structure_set_gives_the_same_influence_matrices[photons] _______

case = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...gnore_outside_densities=None, num_of_bixels_per_ray=array([1, 1, 1, 1, 1]), num_of_rays=5, total_number_of_bixels=5)]))

    def test_4d_structure_set_gives_the_same_influence_matrices(case):
        from pyRadPlan.cst import validate_cst
    
        pln, ct, cst, stf = case
        ct2, _ = _two_phase_ct(ct)
        cst4d = validate_cst(cst, ct=ct2)
        assert cst4d.num_of_scenarios == 2
    
        scen_set = _scenario_set(ct_phase=[0, 1], phases=(0, 1))
>       _, _, with_3d = _calc(pln, ct2, cst, stf, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:594: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:76: in _calc
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
______________________ test_optimization_runs_on_a_4d_ct _______________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_optimization_runs_on_a_4d_ct(test_data_photons):
        from pyRadPlan.optimization.problems import NonLinearFluencePlanningProblem
    
        pln, ct, cst, stf, _, _ = test_data_photons
        ct2, _ = _two_phase_ct(ct)
        scen_set = _scenario_set(ct_phase=[0, 1], phases=(0, 1))
>       dij = calc_dose_influence(ct2, cst, stf, pln, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:606: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_____ test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[none] ______

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})
robustness = 'none'

    @pytest.mark.parametrize("robustness", ["none", "STOCH", "OWC", "COWC"])
    def test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase(test_data_photons, robustness):
        from pyRadPlan.core import xp_utils
    
>       problem, dij = _moving_structure_problem(test_data_photons, robustness)

test/dose/test_scenarios_pencilbeam.py:654: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:643: in _moving_structure_problem
    dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_____ test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[STOCH] _____

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})
robustness = 'STOCH'

    @pytest.mark.parametrize("robustness", ["none", "STOCH", "OWC", "COWC"])
    def test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase(test_data_photons, robustness):
        from pyRadPlan.core import xp_utils
    
>       problem, dij = _moving_structure_problem(test_data_photons, robustness)

test/dose/test_scenarios_pencilbeam.py:654: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:643: in _moving_structure_problem
    dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
______ test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[OWC] ______

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})
robustness = 'OWC'

    @pytest.mark.parametrize("robustness", ["none", "STOCH", "OWC", "COWC"])
    def test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase(test_data_photons, robustness):
        from pyRadPlan.core import xp_utils
    
>       problem, dij = _moving_structure_problem(test_data_photons, robustness)

test/dose/test_scenarios_pencilbeam.py:654: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:643: in _moving_structure_problem
    dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_____ test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[COWC] ______

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})
robustness = 'COWC'

    @pytest.mark.parametrize("robustness", ["none", "STOCH", "OWC", "COWC"])
    def test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase(test_data_photons, robustness):
        from pyRadPlan.core import xp_utils
    
>       problem, dij = _moving_structure_problem(test_data_photons, robustness)

test/dose/test_scenarios_pencilbeam.py:654: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:643: in _moving_structure_problem
    dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
___________ test_none_robustness_sums_each_phase_on_its_own_contour ____________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_none_robustness_sums_each_phase_on_its_own_contour(test_data_photons):
        from pyRadPlan.core import xp_utils
    
>       problem, dij = _moving_structure_problem(test_data_photons, "none")

test/dose/test_scenarios_pencilbeam.py:679: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:643: in _moving_structure_problem
    dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_________ test_voxel_worst_case_uses_the_contour_of_its_selected_phase _________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_voxel_worst_case_uses_the_contour_of_its_selected_phase(test_data_photons):
        from pyRadPlan.optimization.robustness import RobustTerm, VoxelWorstCase
    
>       problem, _ = _moving_structure_problem(test_data_photons, "none")

test/dose/test_scenarios_pencilbeam.py:696: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/dose/test_scenarios_pencilbeam.py:643: in _moving_structure_problem
    dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
______________ test_image_reference_refused_on_moving_structures _______________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_image_reference_refused_on_moving_structures(test_data_photons):
        from pyRadPlan.optimization.objectives import SquaredMimicking
        from pyRadPlan.optimization.problems import NonLinearFluencePlanningProblem
    
        pln, ct, cst, stf, _, _ = test_data_photons
        ct2, _ = _two_phase_ct(ct)
        moved = _cst_with_moving_structures(cst, ct2)
        reference = sitk.Image(ct.cube_hu.GetSize(), sitk.sitkFloat32)
        reference.CopyInformation(ct.cube_hu)
        for voi in moved.vois:
            voi.objectives = [SquaredMimicking(d_ref=reference)]
        scen_set = _scenario_set(ct_phase=[0, 1], phases=(0, 1))
>       dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:723: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
________________ test_moving_structures_on_a_coarser_dose_grid _________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_moving_structures_on_a_coarser_dose_grid(test_data_photons):
        pln, ct, cst, stf, _, _ = test_data_photons
        ct2, _ = _two_phase_ct(ct)
        moved = _cst_with_moving_structures(cst, ct2)
        # Different voxel counts per phase also on the coarser grid
        for voi in moved.vois:
            masks = sitk.GetArrayFromImage(voi.mask)
            masks[1, : masks.shape[1] // 2] = 0
            mask = sitk.GetImageFromArray(masks, isVector=False)
            mask.CopyInformation(voi.mask)
            voi.mask = mask
        resolution = ct.grid.resolution
        pln.prop_dose_calc["dose_grid"] = {
            "resolution": {axis: 2.0 * resolution[axis] for axis in ("x", "y", "z")}
        }
        scen_set = _scenario_set(ct_phase=[0, 1], phases=(0, 1))
>       dij = calc_dose_influence(ct2, moved, stf, pln, scenarios=scen_set)

test/dose/test_scenarios_pencilbeam.py:745: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.2963624 , -1.0606601 ],
        [ 0.80995476,  0.9909502 ],
        [ 0.9722718 , -0.79549515]],

       [... 1.0606601 ],
        [ 0.80995476,  0.9909502 ],
        [-0.9722718 ,  0.79549515]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
Scenarios:   0%|          | 0/2 [00:00<?, ?scen/s]
Beam:   0%|          | 0/2 [00:00<?, ?b/s]
                                                  
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_________________________________ test_photons _________________________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_photons(test_data_photons):
        pln, ct, cst, stf, dij, result = test_data_photons
        pln.prop_dose_calc["enable_dij_sampling"] = False
>       result_py = calc_dose_forward(ct, cst, stf, pln, weights=None)

test/dose/test_svdpb.py:17: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
___________________________ test_photons_field_based ___________________________

    def test_photons_field_based():
        """Exercise the per-ray kernel interpolation path (_init_ray) on the configured backend.
    
        Field-based dose calculation is the one configuration that reaches ``_init_ray``'s
        custom fluence branch, which builds its own kernel interpolators via FFT convolution.
        The default configuration skips it entirely, so it needs its own coverage.
        """
        ct, cst = load_tg119()
    
        leaf_width = 5.0
        positions = [[-20.0, 20.0]] * 4
        boundaries = np.arange(-2 * leaf_width, 2 * leaf_width, leaf_width)
    
        mlc = create_bld(
            {
                "device_type": "MLC",
                "device_orientation": "X",
                "leaf_position_boundaries": boundaries,
                "leaf_positions": positions,
                "leaf_width": leaf_width,
                "leaf_leakage": 0.1,
            }
        )
    
        pln = PhotonPlan(machine="Generic")
        pln.prop_stf = {
            "gantry_angles": [0.0],
            "couch_angles": [0.0],
            "generator": "photonSingleBixel",
            "field_based": True,
            "blds": [mlc],
            "resolution": 2.0,
            "energy": 6.0,
        }
        pln.prop_dose_calc["dosimetric_lateral_cutoff"] = 0.995
        pln.prop_dose_calc["lateral_model"] = "single"
    
        stf = generate_stf(ct, cst, pln)
        masks = [b.mask.copy() for beam in stf.beams for ray in beam.rays for b in ray.beamlets]
>       dose = sitk.GetArrayFromImage(calc_dose_forward(ct, cst, stf, pln)["physical_dose"])

test/dose/test_svdpb.py:63: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:93: in calc_dose_forward
    return engine.calc_dose_forward(ct, cst, stf, weights, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 35.75258   , -35.6652    ],
        [  0.69623196,   1.1575938 ],
        [  2.1070771 ,  -2.0712812 ]],

  ...65203  ],
        [  0.69623196,   1.1575938 ],
        [ -2.1070771 ,   2.0712812 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
____________ test_bixel_based_lateral_cutoff_includes_half_diagonal ____________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_bixel_based_lateral_cutoff_includes_half_diagonal(test_data_photons):
        """matRad widens the lateral cutoff by bixelWidth/sqrt(2) for bixel-based dose too."""
        pln, ct, cst, stf, _, _ = test_data_photons
    
        engine = get_engine(pln)
>       engine.calc_dose_forward(ct, cst, stf, None)

test/dose/test_svdpb.py:91: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:469: in calc_dose_forward
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
_______________ test_photons_constant_rbe_is_stored_and_applied ________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_photons_constant_rbe_is_stored_and_applied(test_data_photons):
        """A constant-RBE photon plan yields dij.rbe and rbe * physical_dose."""
        pln, ct, cst, stf, _dij, result = test_data_photons
        pln.bio_model = {"model": "constant_rbe", "rbe": 1.5}
    
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_svdpb_bio.py:24: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
____________________ test_photons_default_model_is_recorded ____________________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})

    def test_photons_default_model_is_recorded(test_data_photons):
        pln, ct, cst, stf, _dij, _result = test_data_photons
    
>       dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_svdpb_bio.py:40: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
______________ test_photon_engine_drops_a_model_it_cannot_compute ______________

test_data_photons = (PhotonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'is...   [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ]]], shape=(20, 10, 10)), ...})
caplog = <_pytest.logging.LogCaptureFixture object at 0x7f51ae570ad0>

    def test_photon_engine_drops_a_model_it_cannot_compute(test_data_photons, caplog):
        """A model with outputs the engine cannot compute falls back to physical dose, reported."""
        pln, ct, cst, stf, _dij, _result = test_data_photons
        pln.bio_model = _PhotonLQModel()
    
        with caplog.at_level(logging.WARNING):
>           dij_py = calc_dose_influence(ct, cst, stf, pln)

test/dose/test_svdpb_bio.py:63: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/_calc_dose.py:51: in calc_dose_influence
    dij = engine.calc_dose_influence(ct, cst, stf, scenarios=scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:523: in calc_dose_influence
    dij = self._calc_dose_with_scenarios(ct, cst, stf, scenarios)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:535: in _calc_dose_with_scenarios
    return self._calc_dose(ct, cst, stf)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:230: in _calc_dose
    curr_beam = self._init_beam(dij, ct, cst, scen_stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_svdpb.py:163: in _init_beam
    beam_info = super()._init_beam(beam_info, ct, cst, stf, i)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:597: in _init_beam
    rad_depth_cubes = self._raytracer.trace_cubes_array(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.767767  , -1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [ 0.7856742 , -0.7856742 ]],

       [... 1.767767  ],
        [ 0.82191783,  1.0045662 ],
        [-0.7856742 ,  0.7856742 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.dose.engines._factory:_factory.py:143 No engine specified in Plan. Using first available engine SVDPB.
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: lateral_model
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dose_grid
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: enable_dij_sampling
WARNING  pyRadPlan.core._configurable:_configurable.py:336 Property overwritten from pln.propDoseCalc: dosimetric_lateral_cutoff
WARNING  pyRadPlan.dose.engines._base:_base.py:1006 Biological model 'photon_lq_capability_test' cannot be evaluated for this machine and these structures: Biological model 'photon_lq_capability_test' not available: The dose engine SVDPB does not compute biological influence matrices. Biological quantities (e.g. RBExDose) will not be available; only the physical dose influence is computed. Choose a biological model and tissue parameters matching the machine base data, or set calc_bio_dose=True to make this an error.
_ test_kernel_interpolators_reuse_and_fluence_isolation[array_api_strict-float32] _

interpolation_backend = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype_name = 'float32'

    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_kernel_interpolators_reuse_and_fluence_isolation(interpolation_backend, dtype_name):
        """Repeated queries preserve values; another field cannot overwrite the first."""
        xp = interpolation_backend
        dtype = getattr(xp, dtype_name)
        engine = SimpleNamespace(xp=xp, device=None)
        grid = xp.asarray([-2, -1, 0, 1, 2], dtype=dtype)
        kernels = xp.asarray(np.arange(18).reshape(2, 3, 3) / 20, dtype=dtype)
        fluence = xp.asarray([[0.0, 1.0], [0.5, 0.25]], dtype=dtype)
        info = {
            "kernel": SimpleNamespace(num_kernel_components=2),
            "kernel_conv_grid": grid,
            "kernel_conv_size": 5,
            "kernel_mxs": kernels,
        }
        queries = xp.asarray([[-2, -2], [2, 2], [0.25, -0.75], [-1, 1]], dtype=dtype)
        expected = []
        for c in range(kernels.shape[0]):
            kernel = kernels[c, ...]
            values = xp.real(_ifft2(_fft2(fluence, s=(5, 5)) * _fft2(kernel, s=(5, 5))))
>           expected.append(interpnd(queries, (grid, grid), values, bounds_error=True))

test/dose/test_svdpb_interpolators.py:45: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:230: in interpnd
    return ArrayBackendGridInterpolator(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2., -1.,  0.,  1.,  2.], dtype=array_api_strict.float32)
key = Array([0, 3, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 3 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_ test_kernel_interpolators_reuse_and_fluence_isolation[array_api_strict-float64] _

interpolation_backend = <module 'array_api_strict' from '/usr/lib/python3.14/site-packages/array_api_strict/__init__.py'>
dtype_name = 'float64'

    @pytest.mark.parametrize("dtype_name", ["float32", "float64"])
    def test_kernel_interpolators_reuse_and_fluence_isolation(interpolation_backend, dtype_name):
        """Repeated queries preserve values; another field cannot overwrite the first."""
        xp = interpolation_backend
        dtype = getattr(xp, dtype_name)
        engine = SimpleNamespace(xp=xp, device=None)
        grid = xp.asarray([-2, -1, 0, 1, 2], dtype=dtype)
        kernels = xp.asarray(np.arange(18).reshape(2, 3, 3) / 20, dtype=dtype)
        fluence = xp.asarray([[0.0, 1.0], [0.5, 0.25]], dtype=dtype)
        info = {
            "kernel": SimpleNamespace(num_kernel_components=2),
            "kernel_conv_grid": grid,
            "kernel_conv_size": 5,
            "kernel_mxs": kernels,
        }
        queries = xp.asarray([[-2, -2], [2, 2], [0.25, -0.75], [-1, 1]], dtype=dtype)
        expected = []
        for c in range(kernels.shape[0]):
            kernel = kernels[c, ...]
            values = xp.real(_ifft2(_fft2(fluence, s=(5, 5)) * _fft2(kernel, s=(5, 5))))
>           expected.append(interpnd(queries, (grid, grid), values, bounds_error=True))

test/dose/test_svdpb_interpolators.py:45: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:230: in interpnd
    return ArrayBackendGridInterpolator(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:154: in __call__
    result = self._evaluate(xi)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_interpolation.py:248: in _interp2d_core
    gx0 = gx[ix]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([-2., -1.,  0.,  1.,  2.], dtype=array_api_strict.float64)
key = Array([0, 3, 2, 0], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 3 2 0] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
_______________________ test_import_ct_matches_reference _______________________

dicom_dir = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom')
dicom_reference_mat = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom/dicom_testData.mat')

    def test_import_ct_matches_reference(dicom_dir, dicom_reference_mat):
        ref_ct = validate_ct(matfile.load(dicom_reference_mat)["ct"])
        ct = DicomImporter(dicom_dir).load_ct()
    
        assert isinstance(ct, CT)
        assert ct.cube_hu.GetSize() == ref_ct.cube_hu.GetSize()
>       assert np.allclose(ct.cube_hu.GetSpacing(), ref_ct.cube_hu.GetSpacing())
E       AssertionError: assert False
E        +  where False = <function allclose at 0x7f51cbe7cf30>((15.0, 10.0, 5.0), (10.0, 15.0, 5.0))
E        +    where <function allclose at 0x7f51cbe7cf30> = np.allclose
E        +    and   (15.0, 10.0, 5.0) = GetSpacing()
E        +      where GetSpacing = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a32256b0> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a32256b0> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a32256b0> >, reso...10.,    0.,   10.,   20.,\n         30.,   40.]), z=array([-25., -20., -15., -10.,  -5.,   0.,   5.,  10.,  15.,  20.])).cube_hu
E        +    and   (10.0, 15.0, 5.0) = GetSpacing()
E        +      where GetSpacing = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51ad99ff00> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51ad99ff00> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51ad99ff00> >, reso...60.,   75.,   90.,  105.,\n        120.,  135.]), z=array([-25., -20., -15., -10.,  -5.,   0.,   5.,  10.,  15.,  20.])).cube_hu

test/io/test_io_dicom.py:76: AssertionError
----------------------------- Captured stderr call -----------------------------
                                                        
______________________ test_import_cst_matches_reference _______________________

dicom_dir = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom')
dicom_reference_mat = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom/dicom_testData.mat')

    def test_import_cst_matches_reference(dicom_dir, dicom_reference_mat):
        m = matfile.load(dicom_reference_mat)
        ref_ct = validate_ct(m["ct"])
        ref_cst = validate_cst(m["cst"], ref_ct)
    
        importer = DicomImporter(dicom_dir)
        ct = importer.load_ct()
        cst = importer.load_cst(ct)
    
        assert isinstance(cst, StructureSet)
    
        sets = _voxel_sets(cst)
        ref_sets = _voxel_sets(ref_cst)
        assert set(sets) == set(ref_sets)
        for name, voxels in ref_sets.items():
            # The RTSTRUCT contour->mask conversion must reproduce the reference voxels exactly.
>           assert voxels == sets[name], f"VOI '{name}' voxels differ from reference"
E           AssertionError: VOI 'target' voxels differ from reference
E           assert {1341, 1342, ...57, 1358, ...} == {1399, 1400, ...29, 1430, ...}
E             
E             Extra items in the left set:
E             1641
E             1642
E             1643
E             1356
E             1357...
E             
E             ...Full output truncated (21 lines hidden), use '-vv' to show

test/io/test_io_dicom.py:101: AssertionError
----------------------------- Captured stderr call -----------------------------
                                                 
_______________________ test_load_data_from_dicom_folder _______________________

dicom_dir = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom')
dicom_reference_mat = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom/dicom_testData.mat')

    def test_load_data_from_dicom_folder(dicom_dir, dicom_reference_mat):
        data = load_data(dicom_dir)
        assert isinstance(data["ct"], CT)
        assert isinstance(data["cst"], StructureSet)
        assert isinstance(data["dose"], sitk.Image)
    
        # The folder holds per-beam doses and LET cubes (all modality RTDOSE);
        # load_data must select the plan-level physical dose, not an arbitrary one.
        ref_dose = np.asarray(matfile.load(dicom_reference_mat)["resultGUI"]["physicalDose"])
        ref_zyx = np.transpose(ref_dose, (2, 0, 1))
        arr = sitk.GetArrayFromImage(data["dose"])
        assert arr.shape == ref_zyx.shape
>       assert np.allclose(arr, ref_zyx, atol=1e-3)
E       assert False
E        +  where False = <function allclose at 0x7f51cbe7cf30>(array([[[0.        , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ],\n        [0.        , 0...      , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ]]], shape=(10, 20, 15), dtype=float32), array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]...0000000e+00, 0.00000000e+00, ...,\n         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]],\n      shape=(10, 20, 15)), atol=0.001)
E        +    where <function allclose at 0x7f51cbe7cf30> = np.allclose

test/io/test_io_dicom.py:165: AssertionError
----------------------------- Captured stderr call -----------------------------
                                                 
___________________________ test_dicom_roundtrip_ct ____________________________

dicom_dir = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom')
tmp_path = PosixPath('/tmp/pytest-of-mockbuild/pytest-0/test_dicom_roundtrip_ct0')

    def test_dicom_roundtrip_ct(dicom_dir, tmp_path):
        ct = DicomImporter(dicom_dir).load_ct()
        DicomExporter(tmp_path).save(ct=ct)
    
        ct2 = DicomImporter(tmp_path).load_ct()
        assert ct2.cube_hu.GetSize() == ct.cube_hu.GetSize()
>       assert np.allclose(ct2.cube_hu.GetSpacing(), ct.cube_hu.GetSpacing())
E       AssertionError: assert False
E        +  where False = <function allclose at 0x7f51cbe7cf30>((10.0, 15.0, 5.0), (15.0, 10.0, 5.0))
E        +    where <function allclose at 0x7f51cbe7cf30> = np.allclose
E        +    and   (10.0, 15.0, 5.0) = GetSpacing()
E        +      where GetSpacing = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a1033cc0> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a1033cc0> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a1033cc0> >, reso...60.,   75.,   90.,  105.,\n        120.,  135.]), z=array([-25., -20., -15., -10.,  -5.,   0.,   5.,  10.,  15.,  20.])).cube_hu
E        +    and   (15.0, 10.0, 5.0) = GetSpacing()
E        +      where GetSpacing = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a17192f0> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a17192f0> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a17192f0> >, reso...10.,    0.,   10.,   20.,\n         30.,   40.]), z=array([-25., -20., -15., -10.,  -5.,   0.,   5.,  10.,  15.,  20.])).cube_hu

test/io/test_io_dicom.py:229: AssertionError
----------------------------- Captured stderr call -----------------------------
                                                        
__________________________ test_dicom_roundtrip_dose ___________________________

dicom_dir = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom')
tmp_path = PosixPath('/tmp/pytest-of-mockbuild/pytest-0/test_dicom_roundtrip_dose0')

    def test_dicom_roundtrip_dose(dicom_dir, tmp_path):
        dose = import_dose(str(dicom_dir / "RTDose_6_physicalDose.dcm"))
        DicomExporter(tmp_path).save(ct=DicomImporter(dicom_dir).load_ct(), dose=dose)
    
        dose2 = DicomImporter(tmp_path).load_dose()
        a = sitk.GetArrayFromImage(dose)
        b = sitk.GetArrayFromImage(dose2)
        assert b.shape == a.shape
        # uint32 quantization -> values reproduced to high precision.
>       assert np.allclose(a, b, atol=1e-4 * max(a.max(), 1.0))
E       assert False
E        +  where False = <function allclose at 0x7f51cbe7cf30>(array([[[0.        , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ],\n        [0.        , 0...      , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ]]], shape=(10, 20, 15), dtype=float32), array([[[0.        , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ],\n        [0.        , 0...      , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ]]], shape=(10, 20, 15), dtype=float32), atol=(0.0001 * np.float32(1.6662134)))
E        +    where <function allclose at 0x7f51cbe7cf30> = np.allclose
E        +    and   np.float32(1.6662134) = max(np.float32(1.6662134), 1.0)
E        +      where np.float32(1.6662134) = <built-in method max of numpy.ndarray object at 0x7f51a1adff90>()
E        +        where <built-in method max of numpy.ndarray object at 0x7f51a1adff90> = array([[[0.        , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ],\n        [0.        , 0...      , 0.        , 0.        , ..., 0.        ,\n         0.        , 0.        ]]], shape=(10, 20, 15), dtype=float32).max

test/io/test_io_dicom.py:245: AssertionError
----------------------------- Captured stderr call -----------------------------
                                                        
_______________________ test_dicom_roundtrip_structures ________________________

dicom_dir = PosixPath('/builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/data/dicom')
tmp_path = PosixPath('/tmp/pytest-of-mockbuild/pytest-0/test_dicom_roundtrip_structure0')

    def test_dicom_roundtrip_structures(dicom_dir, tmp_path):
        importer = DicomImporter(dicom_dir)
        ct = importer.load_ct()
        cst = importer.load_cst(ct)
    
        DicomHandler(tmp_path).save(ct=ct, cst=cst)
    
        cst2 = DicomImporter(tmp_path).load_cst()
        sets = _voxel_sets(cst)
        sets2 = _voxel_sets(cst2)
        assert set(sets) == set(sets2)
        for name, voxels in sets.items():
            # Contour extraction then re-rasterization is near-lossless (boundary voxels).
>           assert _dice(voxels, sets2[name]) > 0.95, f"VOI '{name}' degraded in roundtrip"
E           AssertionError: VOI 'target' degraded in roundtrip
E           assert 0.0 > 0.95
E            +  where 0.0 = _dice({1399, 1400, 1414, 1415, 1429, 1430, ...}, {1341, 1342, 1343, 1356, 1357, 1358, ...})

test/io/test_io_dicom.py:261: AssertionError
----------------------------- Captured stderr call -----------------------------
                                                 
_________________________ test_expected_value_operator _________________________

three_scenarios = ScenarioSet(num_scenarios=3, dimensions=('ct_phase', 'setup', 'range'), axes=(('ct_phase.index',), ('setup.x', 'setup.y', 'setup.z'), ('range.abs', 'range.rel')))

    def test_expected_value_operator(three_scenarios):
        objective = SquaredDeviation(d_ref=1.0, priority=3.0)
        op = get_robustness_operator("STOCH", three_scenarios, RobustnessOptions())
        assert op.scenario_indices == [0, 1, 2]
        np.testing.assert_allclose(op.weights, [0.5, 0.3, 0.2])
        values = xp.asarray(np.array([[2.0, 0.0], [1.0, 1.0], [0.0, 3.0]]))
        entries = [RobustTermEntry(objective, "TARGET")]
        per_scenario = [float(objective.compute_objective(values[s, ...])) for s in range(3)]
        expected = 3.0 * np.dot([0.5, 0.3, 0.2], per_scenario)
>       assert float(op.evaluate(entries, [values])) == pytest.approx(expected)

test/optimization/robustness/test_robustness_contract.py:163: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_value.py:34: in evaluate
    weights = as_backend(self.weights, first)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_gradient_follows_the_dtype_of_the_values[expected_value] _________

three_scenarios = ScenarioSet(num_scenarios=3, dimensions=('ct_phase', 'setup', 'range'), axes=(('ct_phase.index',), ('setup.x', 'setup.y', 'setup.z'), ('range.abs', 'range.rel')))
robustness = 'expected_value'

    @pytest.mark.parametrize("robustness", ["none", "expected_value", "scenario_stack"])
    def test_gradient_follows_the_dtype_of_the_values(three_scenarios, robustness):
        objective = (
            _Float64StackGradient(d_max=1.0)
            if robustness == "scenario_stack"
            else _Float64Gradient(d_max=1.0)
        )
        op = get_robustness_operator(robustness, three_scenarios)
        values = xp.asarray(np.full((op.num_scenarios, 3), 2.0), dtype=xp.float32)
>       grad = op.gradient([RobustTermEntry(objective)], [values])[0]

test/optimization/robustness/test_robustness_contract.py:254: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_value.py:42: in gradient
    weights = as_backend(self.weights, first)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
____________________________ test_expected_quantity ____________________________

three_scenarios = ScenarioSet(num_scenarios=3, dimensions=('ct_phase', 'setup', 'range'), axes=(('ct_phase.index',), ('setup.x', 'setup.y', 'setup.z'), ('range.abs', 'range.rel')))
operator_fd = <function operator_fd.<locals>._fd at 0x7f51a0bb73d0>

    def test_expected_quantity(three_scenarios, operator_fd):
        objective = SquaredDeviation(d_ref=2.0, priority=3.0)
        op = get_robustness_operator("expected_quantity", three_scenarios)
        assert isinstance(op, ExpectedQuantity)
        assert op.scenario_indices == [0, 1, 2]
        entries = [RobustTermEntry(objective, "TARGET")]
        values = [xp.asarray(VALUES)]
        expected = np.array([0.5, 0.3, 0.2]) @ VALUES
>       assert float(op.evaluate(entries, values)) == pytest.approx(
            3.0 * np.mean((expected - 2.0) ** 2)
        )

test/optimization/robustness/test_robustness_operators.py:44: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_quantity.py:37: in evaluate
    _, expected = self._expected(values[0])
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_quantity.py:31: in _expected
    weights = as_backend(self.weights / total if total > 0.0 else self.weights, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_expected_quantity_is_a_mean_for_any_weight_sum[weights0] _________

make_set = <function _make_set at 0x7f51ad75b110>, weights = [1.0, 1.0]

    @pytest.mark.parametrize("weights", [[1.0, 1.0], [0.3, 0.3]])
    def test_expected_quantity_is_a_mean_for_any_weight_sum(make_set, weights):
        scen_set = make_set([(0.0, 0.0, 0.0), (3.0, 0.0, 0.0)], weights=weights)
        op = ExpectedQuantity(scen_set)
        objective = SquaredDeviation(d_ref=2.0)
        entries = [RobustTermEntry(objective)]
        values = xp.asarray(np.array([[1.0, 1.0], [3.0, 3.0]]))
    
>       assert float(op.evaluate(entries, [values])) == pytest.approx(0.0)

test/optimization/robustness/test_robustness_operators.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_quantity.py:37: in evaluate
    _, expected = self._expected(values[0])
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_quantity.py:31: in _expected
    weights = as_backend(self.weights / total if total > 0.0 else self.weights, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________ test_expected_quantity_is_a_mean_for_any_weight_sum[weights1] _________

make_set = <function _make_set at 0x7f51ad75b110>, weights = [0.3, 0.3]

    @pytest.mark.parametrize("weights", [[1.0, 1.0], [0.3, 0.3]])
    def test_expected_quantity_is_a_mean_for_any_weight_sum(make_set, weights):
        scen_set = make_set([(0.0, 0.0, 0.0), (3.0, 0.0, 0.0)], weights=weights)
        op = ExpectedQuantity(scen_set)
        objective = SquaredDeviation(d_ref=2.0)
        entries = [RobustTermEntry(objective)]
        values = xp.asarray(np.array([[1.0, 1.0], [3.0, 3.0]]))
    
>       assert float(op.evaluate(entries, [values])) == pytest.approx(0.0)

test/optimization/robustness/test_robustness_operators.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_quantity.py:37: in evaluate
    _, expected = self._expected(values[0])
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_quantity.py:31: in _expected
    weights = as_backend(self.weights / total if total > 0.0 else self.weights, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
____ test_operators_on_phase_contours_match_equal_contours[expected_value] _____

make_set = <function _make_set at 0x7f51ad75b110>, name = 'expected_value'

    @pytest.mark.parametrize(
        "name", ["none", "expected_value", "objective_worst_case", "composite_worst_case"]
    )
    def test_operators_on_phase_contours_match_equal_contours(make_set, name):
        """A list per scenario gives the same as the stack, and works for unequal voxel counts."""
        scen_set = make_set([(0.0, 0.0, 0.0), (0.0, 0.0, 0.0)], weights=[0.5, 0.5], ct_phase=[0, 1])
        op = get_robustness_operator(name, scen_set, RobustnessOptions())
        assert op.phase_contours
        entries = [RobustTermEntry(SquaredDeviation(d_ref=1.0, priority=2.0), "TARGET")]
        first = np.array([2.0, 0.0])
        second = np.array([1.0, 3.0])
    
        stacked = xp.asarray(np.stack([first, second]))
        listed = [xp.asarray(first), xp.asarray(second)]
>       assert float(op.evaluate(entries, [listed])) == pytest.approx(
            float(op.evaluate(entries, [stacked]))
        )

test/optimization/robustness/test_robustness_operators.py:239: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_value.py:34: in evaluate
    weights = as_backend(self.weights, first)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
__________________ test_variance_closed_form_uniform_default ___________________

    def test_variance_closed_form_uniform_default():
        objective = Variance()
        values = xp.asarray(np.array([[1.0, 2.0], [3.0, 4.0]]))
>       assert float(objective.compute_objective(values)) == pytest.approx(1.0)

test/optimization/robustness/test_variance_objective.py:23: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:63: in compute_objective
    xp, weights, deviation = self._deviation(values, weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:43: in _deviation
    xp_utils.from_numpy(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
__________________ test_variance_weighted_value_and_gradient ___________________

finite_difference = <function _finite_difference at 0x7f51ad75b1c0>

    def test_variance_weighted_value_and_gradient(finite_difference):
        objective = Variance()
        values = xp.asarray(VALUES)
        mean = WEIGHTS @ VALUES
        expected = np.mean(np.average((VALUES - mean) ** 2, axis=0, weights=WEIGHTS))
>       assert float(objective.compute_objective(values, weights=WEIGHTS)) == pytest.approx(expected)

test/optimization/robustness/test_variance_objective.py:36: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:63: in compute_objective
    xp, weights, deviation = self._deviation(values, weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:43: in _deviation
    xp_utils.from_numpy(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_____________________ test_variance_through_scenario_stack _____________________

three_scenarios = ScenarioSet(num_scenarios=3, dimensions=('ct_phase', 'setup', 'range'), axes=(('ct_phase.index',), ('setup.x', 'setup.y', 'setup.z'), ('range.abs', 'range.rel')))
operator_fd = <function operator_fd.<locals>._fd at 0x7f518672b8a0>

    def test_variance_through_scenario_stack(three_scenarios, operator_fd):
        objective = Variance(priority=4.0, robustness="OWC")
        assert objective.robustness == "scenario_stack"
        assert Variance.available_robustness() == ["scenario_stack"]
        op = ScenarioStack(three_scenarios)
        entries = [RobustTermEntry(objective)]
        values = [xp.asarray(VALUES)]
        mean = WEIGHTS @ VALUES
        expected = 4.0 * np.mean(np.average((VALUES - mean) ** 2, axis=0, weights=WEIGHTS))
>       assert float(op.evaluate(entries, values)) == pytest.approx(expected)

test/optimization/robustness/test_variance_objective.py:64: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_base.py:191: in evaluate
    return objective.priority * objective.compute_objective(values[0], weights=self.weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:63: in compute_objective
    xp, weights, deviation = self._deviation(values, weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:43: in _deviation
    xp_utils.from_numpy(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.optimization.objectives._objective:_objective.py:384 Objective 'Variance' evaluates the scenarios itself; robustness 'OWC' is replaced by 'scenario_stack'.
___________________ test_solver_dict_configuration_survives ____________________

small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    def test_solver_dict_configuration_survives(small_proton_case):
        """max_iter/display from the solver dict are kept when not set at the top level."""
>       solver = _solved_solver(
            small_proton_case, {"solver": {"name": "scipy", "max_iter": 42, "display": False}}
        )

test/optimization/test_fluence_optiprob.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/optimization/test_fluence_optiprob.py:96: in _solved_solver
    prob.solve(ct, cst, stf, dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_____________________ test_top_level_overrides_solver_dict _____________________

small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    def test_top_level_overrides_solver_dict(small_proton_case):
        """An explicitly set top-level value takes precedence over the same key in the dict."""
>       solver = _solved_solver(
            small_proton_case,
            {"solver": {"name": "scipy", "max_iter": 42, "display": False}, "max_iter": 7},
        )

test/optimization/test_fluence_optiprob.py:112: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/optimization/test_fluence_optiprob.py:96: in _solved_solver
    prob.solve(ct, cst, stf, dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_________________ test_unset_properties_leave_solver_defaults __________________

small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    def test_unset_properties_leave_solver_defaults(small_proton_case):
        """Without any configuration the solver keeps its own defaults."""
        reference = OptimizerSciPy()
>       solver = _solved_solver(small_proton_case, {"solver": "scipy"})

test/optimization/test_fluence_optiprob.py:125: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/optimization/test_fluence_optiprob.py:96: in _solved_solver
    prob.solve(ct, cst, stf, dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
___________ test_constant_rbe_evaluates_only_the_quantities_it_uses ____________

small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...el(rbe=1.1), num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51a0228600>

    def test_constant_rbe_evaluates_only_the_quantities_it_uses(small_proton_case, monkeypatch):
        """The photon LQ effect a dij with alphax / betax allows is not evaluated for a constant RBE."""
        pln, ct, cst, stf, dij = small_proton_case
        num_voxels = dij.dose_grid.num_voxels
        dij.alphax = np.full((num_voxels, 1), 0.1)
        dij.betax = np.full((num_voxels, 1), 0.05)
        dij.bio_model = {"model": "constant_rbe", "rbe": 1.1}
        pln.prop_opt = {"solver": {"name": "scipy", "max_iter": 2, "display": False}}
    
        prob = NonLinearFluencePlanningProblem(pln)
>       prob.solve(ct, cst, stf, dij)

test/optimization/test_fluence_optiprob.py:182: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log call -------------------------------
ERROR    pyRadPlan.optimization.problems._optiprob:_optiprob.py:449 Inconsistency found in bio parameters between biologocal reference alpha and beta parameters in CST and Dij.
WARNING  pyRadPlan.optimization.problems._optiprob:_optiprob.py:414 2 physical-dose objective(s) were converted to 'rbe_x_dose' for the biological model of the dose influence data. Set convert_dose_objectives=False to optimize physical dose instead.
_________ test_objective_jacobian_has_one_row_per_objective[constant] __________

small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...el(rbe=1.1), num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))
rbe_path = 'constant'

    @pytest.mark.parametrize("rbe_path", ["constant", "effect"])
    def test_objective_jacobian_has_one_row_per_objective(small_proton_case, rbe_path):
        """Without the bypass, each objective gets its own gradient row, also across quantities."""
        pln, ct, cst, stf, dij = small_proton_case
        if rbe_path == "constant":
            dij.bio_model = {"model": "constant_rbe", "rbe": 1.1}
        else:
            num_voxels = dij.dose_grid.num_voxels
            dij.alphax = np.full((num_voxels, 1), 0.1)
            dij.betax = np.full((num_voxels, 1), 0.05)
            alpha_dose = np.empty_like(dij.physical_dose)
            sqrt_beta_dose = np.empty_like(dij.physical_dose)
            for i, mat in enumerate(dij.physical_dose.flat):
                alpha_dose.flat[i] = 0.12 * mat
                sqrt_beta_dose.flat[i] = np.sqrt(0.05) * mat
            dij.alpha_dose = alpha_dose
            dij.sqrt_beta_dose = sqrt_beta_dose
        pln.prop_opt = {"solver": {"name": "scipy", "max_iter": 2, "display": False}}
    
        prob = NonLinearFluencePlanningProblem(pln)
>       prob.solve(ct, cst, stf, dij)

test/optimization/test_fluence_optiprob.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.optimization.problems._optiprob:_optiprob.py:414 2 physical-dose objective(s) were converted to 'rbe_x_dose' for the biological model of the dose influence data. Set convert_dose_objectives=False to optimize physical dose instead.
__________ test_objective_jacobian_has_one_row_per_objective[effect] ___________

small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o... total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose', 'alpha_dose', 'sqrt_beta_dose']))
rbe_path = 'effect'

    @pytest.mark.parametrize("rbe_path", ["constant", "effect"])
    def test_objective_jacobian_has_one_row_per_objective(small_proton_case, rbe_path):
        """Without the bypass, each objective gets its own gradient row, also across quantities."""
        pln, ct, cst, stf, dij = small_proton_case
        if rbe_path == "constant":
            dij.bio_model = {"model": "constant_rbe", "rbe": 1.1}
        else:
            num_voxels = dij.dose_grid.num_voxels
            dij.alphax = np.full((num_voxels, 1), 0.1)
            dij.betax = np.full((num_voxels, 1), 0.05)
            alpha_dose = np.empty_like(dij.physical_dose)
            sqrt_beta_dose = np.empty_like(dij.physical_dose)
            for i, mat in enumerate(dij.physical_dose.flat):
                alpha_dose.flat[i] = 0.12 * mat
                sqrt_beta_dose.flat[i] = np.sqrt(0.05) * mat
            dij.alpha_dose = alpha_dose
            dij.sqrt_beta_dose = sqrt_beta_dose
        pln.prop_opt = {"solver": {"name": "scipy", "max_iter": 2, "display": False}}
    
        prob = NonLinearFluencePlanningProblem(pln)
>       prob.solve(ct, cst, stf, dij)

test/optimization/test_fluence_optiprob.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log call -------------------------------
ERROR    pyRadPlan.optimization.problems._optiprob:_optiprob.py:449 Inconsistency found in bio parameters between biologocal reference alpha and beta parameters in CST and Dij.
WARNING  pyRadPlan.optimization.problems._optiprob:_optiprob.py:414 2 physical-dose objective(s) were converted to 'rbe_x_dose' for the biological model of the dose influence data. Set convert_dose_objectives=False to optimize physical dose instead.
___ test_multi_scenario_gradient_backprojects_each_scenario_separately[None] ___

small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o...nstantRBEModel(rbe=1.1), num_of_scenarios=2, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose']))
backend = None

    @pytest.mark.parametrize("backend", [None, "jax.numpy"])
    def test_multi_scenario_gradient_backprojects_each_scenario_separately(small_proton_case, backend):
        """
        Gradient over several scenarios is sum_s w_s D_s^T g_s, not sum_s D_s^T (sum_s' g_s').
    
        Non-robust objectives evaluate the nominal scenario only, so the objectives use the
        expected value over the set; equal weights keep the expectation a plain (scaled) sum over
        the scenarios, which is what this test checks the back-projection of.
        """
        xp_new = None if backend is None else pytest.importorskip(backend)
        from pyRadPlan.scenarios import (
            CTPhaseUncertainty,
            RangeUncertainty,
            ScenarioSet,
            SetupUncertainty,
        )
    
        pln, ct, cst, stf, dij = small_proton_case
        rbe = 1.1
        dij.bio_model = {"model": "constant_rbe", "rbe": rbe}
        base = dij.physical_dose.flat[0]
        for name in dij.quantities:
            if name != "physical_dose":
                setattr(dij, name, None)
        container = np.empty((2,), dtype=object)
        container[0] = base
        container[1] = 0.8 * base
        dij.physical_dose = container
        dij.scenarios = ScenarioSet.from_points(
            (CTPhaseUncertainty(), SetupUncertainty(), RangeUncertainty()),
            {"setup": [[0, 0, 0], [3, 0, 0]]},
            weights=[0.5, 0.5],
        )
        for voi in cst.vois:
            for obj in voi.objectives:
                obj.robustness = "expected_value"
        pln.prop_opt = {"solver": {"name": "scipy", "max_iter": 1, "display": False}}
    
        prob = NonLinearFluencePlanningProblem(pln)
>       prob.solve(ct, cst, stf, dij if xp_new is None else dij.to_namespace(xp_new))

test/optimization/test_fluence_optiprob.py:280: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.optimization.problems._optiprob:_optiprob.py:414 2 physical-dose objective(s) were converted to 'rbe_x_dose' for the biological model of the dose influence data. Set convert_dose_objectives=False to optimize physical dose instead.
______________ test_backend_arrays_and_vector_bounds[True-strict] ______________

backend = 'strict', force_cpu = True

    @pytest.mark.parametrize("backend", ["strict", "torch"])
    @pytest.mark.parametrize("force_cpu", [False, True])
    def test_backend_arrays_and_vector_bounds(backend, force_cpu):
        namespace = xp if backend == "strict" else pytest.importorskip("array_api_compat.torch")
        solver = get_solver("ipax")
        solver.allow_keyboard_cancel = False
        solver.force_cpu_inner_solve = force_cpu
    
        def objective(x):
            assert array_api_compat.array_namespace(x) is namespace
            return namespace.sum(x**2)
    
        solver.objective = objective
        solver.gradient = lambda x: 2 * x
        solver.bounds = (
            namespace.asarray([1.0, 2.0], dtype=namespace.float64),
            namespace.asarray([3.0, 4.0], dtype=namespace.float64),
        )
        x0 = namespace.asarray([2.0, 3.0], dtype=namespace.float64)
>       result, info = solver.solve(x0)

test/optimization/test_ipax_solver.py:185: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_ipax_solver.py:222: in _solve_problem
    result = ipax.solve(problem, x0, options=options, callback=self._callback)
/usr/lib/python3.14/site-packages/ipax/solve.py:189: in solve
    problem_scaling = compute_scaling(
/usr/lib/python3.14/site-packages/ipax/problem/scaling.py:221: in compute_scaling
    obj = _scalar_factor(xp, problem.gradient(x0), max_gradient)
/usr/lib/python3.14/site-packages/ipax/problem/derivatives.py:94: in gradient
    return self._gradient_fn(x)
/usr/lib/python3.14/site-packages/ipax/problem/function.py:152: in gradient
    return self._gradient(x)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_ipax_solver.py:197: in gradient
    xb = xp_utils.from_numpy(backend_xp, x, device=device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_________________ test_problem_objective_fills_history[scipy] __________________

solver_name = 'scipy'
small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    @pytest.mark.parametrize("solver_name", SOLVERS)
    def test_problem_objective_fills_history(solver_name, small_proton_case):
        """Setting obj_history makes the problem's own objective record each evaluation."""
        pln, ct, cst, stf, dij = small_proton_case
        pln.prop_opt = {"solver": solver_name, "display": False, "max_iter": 25}
    
        prob = NonLinearFluencePlanningProblem(pln)
        prob.obj_history = []
>       prob.solve(ct, cst, stf, dij)

test/optimization/test_obj_history.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________________ test_history_stays_empty_when_disabled[scipy] _________________

solver_name = 'scipy'
small_proton_case = (IonPlan(prop_stf={'gantry_angles': array([  0, 180], dtype=uint8), 'couch_angles': array([0, 0], dtype=uint8), 'num_o..._model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose', 'let_dose']))

    @pytest.mark.parametrize("solver_name", SOLVERS)
    def test_history_stays_empty_when_disabled(solver_name, small_proton_case):
        """A problem left with obj_history=None accumulates nothing while solving."""
        pln, ct, cst, stf, dij = small_proton_case
        pln.prop_opt = {"solver": solver_name, "display": False, "max_iter": 25}
    
        prob = NonLinearFluencePlanningProblem(pln)
>       prob.solve(ct, cst, stf, dij)

test/optimization/test_obj_history.py:38: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_________________________ test_SquaredMimicking_cache __________________________

    def test_SquaredMimicking_cache():
        array = xp.full((2, 2, 2), 60.0)
        grid = Grid(
            resolution={"x": 3.0, "y": 3.0, "z": 3.0}, dimensions=(2, 2, 2), origin=[0.0, 0.0, 0.0]
        )
        sq_mimic = SquaredMimicking(d_ref=(array, grid))
>       sq_mimic.preprocess_image_reference_parameters(target_grid=grid, index_list=xp.arange(8))

test/optimization/test_objectives.py:223: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_objective.py:184: in preprocess_image_reference_parameters
    resampled_array = resampled_array[index_list]
/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:630: in __getitem__
    self._validate_index(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Array([60., 60., 60., 60., 60., 60., 60.,
       60.], dtype=array_api_strict.float64)
key = Array([0, 1, 2, 3, 4, 5, 6, 7], dtype=array_api_strict.int64)

    def _validate_index(self, key):
        """
        Validate an index according to the array API.
    
        The array API specification only requires a subset of indices that are
        supported by NumPy. This function will reject any index that is
        allowed by NumPy but not required by the array API specification. We
        always raise ``IndexError`` on such indices (the spec does not require
        any specific behavior on them, but this makes the NumPy array API
        namespace a minimal implementation of the spec). See
        https://data-apis.org/array-api/latest/API_specification/indexing.html
        for the full list of required indexing behavior
    
        This function raises IndexError if the index ``key`` is invalid. It
        only raises ``IndexError`` on indices that are not already rejected by
        NumPy, as NumPy will already raise the appropriate error on such
        indices. ``shape`` may be None, in which case, only cases that are
        independent of the array shape are checked.
    
        The following cases are allowed by NumPy, but not specified by the array
        API specification:
    
        - Indices to not include an implicit ellipsis at the end. That is,
          every axis of an array must be explicitly indexed or an ellipsis
          included. This behaviour is sometimes referred to as flat indexing.
    
        - The start and stop of a slice may not be out of bounds. In
          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
          following are allowed:
    
          - ``i`` or ``j`` omitted (``None``).
          - ``-n <= i <= max(0, n - 1)``.
          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
    
        - Boolean array indices are not allowed as part of a larger tuple
          index.
    
        - Integer array indices are not allowed (with the exception of 0-D
          arrays, which are treated the same as scalars).
    
        Additionally, it should be noted that indices that would return a
        scalar in NumPy will return a 0-D array. Array scalars are not allowed
        in the specification, only 0-D arrays. This is done in the
        ``Array._new`` constructor, not this function.
    
        """
        _key = key if isinstance(key, tuple) else (key,)
        for i in _key:
            if isinstance(i, bool) or not (
                isinstance(i, SupportsIndex)  # i.e. ints
                or isinstance(i, slice)
                or i == Ellipsis
                or i is None
                or isinstance(i, Array)
                or isinstance(i, np.ndarray)
            ):
                raise IndexError(
                    f"Single-axes index {i} has {type(i)=}, but only "
                    "integers, slices (:), ellipsis (...), newaxis (None), "
                    "zero-dimensional integer arrays and boolean arrays "
                    "are specified in the Array API."
                )
    
        nonexpanding_key = []
        single_axes = []
        n_ellipsis = 0
        key_has_mask = False
        for i in _key:
            if i is not None:
                nonexpanding_key.append(i)
                if isinstance(i, np.ndarray):
                    raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
                if isinstance(i, Array):
                    if i.dtype in _boolean_dtypes:
                        key_has_mask = True
                    single_axes.append(i)
                else:
                    # i must not be an array here, to avoid elementwise equals
                    if i == Ellipsis:
                        n_ellipsis += 1
                    else:
                        single_axes.append(i)
    
        n_single_axes = len(single_axes)
        if n_ellipsis > 1:
            return  # handled by ndarray
        elif n_ellipsis == 0:
            # Note boolean masks must be the sole index, which we check for
            # later on.
            if not key_has_mask and n_single_axes < self.ndim:
                raise IndexError(
                    f"{self.ndim=}, but the multi-axes index only specifies "
                    f"{n_single_axes} dimensions. If this was intentional, "
                    "add a trailing ellipsis (...) which expands into as many "
                    "slices (:) as necessary - this is what np.ndarray arrays "
                    "implicitly do, but such flat indexing behaviour is not "
                    "specified in the Array API."
                )
    
        if n_ellipsis == 0:
            indexed_shape = self.shape
        else:
            ellipsis_start = None
            for pos, i in enumerate(nonexpanding_key):
                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
                    if i == Ellipsis:
                        ellipsis_start = pos
                        break
            assert ellipsis_start is not None  # sanity check
            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
            indexed_shape = (
                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
            )
        for i, side in zip(single_axes, indexed_shape):
            if isinstance(i, slice):
                if side == 0:
                    f_range = "0 (or None)"
                else:
                    f_range = f"between -{side} and {side - 1} (or None)"
                if i.start is not None:
                    try:
                        start = operator.index(i.start)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= start <= side):
                            raise IndexError(
                                f"Slice {i} contains {start=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds starts are not specified in "
                                "the Array API)"
                            )
                if i.stop is not None:
                    try:
                        stop = operator.index(i.stop)
                    except TypeError:
                        pass  # handled by ndarray
                    else:
                        if not (-side <= stop <= side):
                            raise IndexError(
                                f"Slice {i} contains {stop=}, but should be "
                                f"{f_range} for an axis of size {side} "
                                "(out-of-bounds stops are not specified in "
                                "the Array API)"
                            )
            elif isinstance(i, Array):
                if i.dtype in _boolean_dtypes:
                    if len(_key) != 1:
                        assert isinstance(key, tuple)  # sanity check
                        raise IndexError(
                            f"Single-axes index {i} is a boolean array and "
                            f"{len(key)=}, but masking is only specified in the "
                            "Array API when the array is the sole index."
                        )
                    if not get_array_api_strict_flags()['boolean_indexing']:
                        raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
    
                elif i.dtype in _integer_dtypes and i.ndim != 0:
>                   raise IndexError(
                        f"Single-axes index {i} is a non-zero-dimensional "
                        "integer array, but advanced integer indexing is not "
                        "specified in the Array API."
E                       IndexError: Single-axes index [0 1 2 3 4 5 6 7] is a non-zero-dimensional integer array, but advanced integer indexing is not specified in the Array API.

/usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:445: IndexError
________________________ test_MinDVH_compute_objective _________________________

    def test_MinDVH_compute_objective():
        min_dvh = MinDVH(d=30.0, v_min=95)
        dose = xp.ones(100)
        dose_2 = xp.ones(100) * 50
>       assert min_dvh.compute_objective(dose) == 841

test/optimization/test_objectives.py:391: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_min_dvh.py:52: in compute_objective
    deviation = xp.where(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([False, False, False, False, False,
       False, False, False, False, False,
       False, False, False, False,...alse,
       False, False, False, False, False,
       False, False, False, False, False], dtype=array_api_strict.bool)
x1 = 0
x2 = Array([-29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -... -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29.], dtype=array_api_strict.float64)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'int' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_________________________ test_MinDVH_compute_gradient _________________________

    def test_MinDVH_compute_gradient():
        min_dvh = MinDVH(d=30.0, v_min=95)
        dose = xp.ones(100)
        dose_2 = xp.ones(100) * 50
        grad_expected = xp.ones(100) * -0.58
        grad_expected2 = xp.zeros(100)
>       assert xp.all(min_dvh.compute_gradient(dose) == grad_expected)

test/optimization/test_objectives.py:401: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_min_dvh.py:65: in compute_gradient
    deviation = xp.where(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([False, False, False, False, False,
       False, False, False, False, False,
       False, False, False, False,...alse,
       False, False, False, False, False,
       False, False, False, False, False], dtype=array_api_strict.bool)
x1 = 0
x2 = Array([-29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -... -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29.], dtype=array_api_strict.float64)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'int' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
________________________ test_MaxDVH_compute_objective _________________________

    def test_MaxDVH_compute_objective():
        max_dvh = MaxDVH(d=30.0, v_max=50)
        dose = xp.ones(100)
        dose_2 = xp.ones(100) * 50
>       assert max_dvh.compute_objective(dose) == 0

test/optimization/test_objectives.py:420: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_max_dvh.py:52: in compute_objective
    deviation = xp.where(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = 0
x2 = Array([-29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -... -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29.], dtype=array_api_strict.float64)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'int' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_________________________ test_MaxDVH_compute_gradient _________________________

    def test_MaxDVH_compute_gradient():
        max_dvh = MaxDVH(d=30.0, v_max=50)
        dose = xp.ones(100)
        dose_2 = xp.ones(100) * 50
        grad_expected = xp.zeros(100)
        grad_expected2 = xp.ones(100) * 0.4
>       assert xp.all(max_dvh.compute_gradient(dose) == grad_expected)

test/optimization/test_objectives.py:430: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_max_dvh.py:65: in compute_gradient
    deviation = xp.where(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = 0
x2 = Array([-29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29., -... -29., -29.,
       -29., -29., -29., -29., -29., -29.,
       -29., -29., -29., -29.], dtype=array_api_strict.float64)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'int' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
__________ test_expected_value_is_the_weighted_sum_of_scenario_values __________

proton_case = ({'machine': 'Generic', 'numOfFractions': 30, 'propDoseCalc': {'doseGrid': {'resolution': {'x': 10, 'y': 10, 'z': 10}}...es=None, bio_model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose']))

    def test_expected_value_is_the_weighted_sum_of_scenario_values(proton_case):
        pln, ct, cst, stf, dij = proton_case
        _set_robustness(cst, "expected_value")
        robust = _problem(proton_case, _dij_with(dij, SCALES, _scenario_set()))
        x = _fluence(robust)
    
        _set_robustness(cst, "none")
        expected = sum(
            w * _value(_problem(proton_case, _dij_with(dij, (scale,))), x)
            for scale, w in zip(SCALES, WEIGHTS)
        )
>       assert _value(robust, x) == pytest.approx(expected, rel=1e-12)

test/optimization/test_robust_fluence_optiprob.py:217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/optimization/test_robust_fluence_optiprob.py:174: in _value
    return float(prob._objective_function(x))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:138: in _objective_function
    f = xp.sum(self._objective_functions(x))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:130: in _objective_functions
    value = xp.asarray(term.operator.evaluate(term.entries, values))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_value.py:34: in evaluate
    weights = as_backend(self.weights, first)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
________________ test_mixed_gradient_matches_finite_differences ________________

mixed_problem = <pyRadPlan.optimization.problems._nonlin_fluence.NonLinearFluencePlanningProblem object at 0x7f51867c65d0>

    def test_mixed_gradient_matches_finite_differences(mixed_problem):
        prob = mixed_problem
        rng = np.random.default_rng(1)
        for seed in range(3):
            x = _fluence(prob, seed)
>           gradient = xp_utils.to_numpy(prob._objective_gradient(x))

test/optimization/test_robust_fluence_optiprob.py:269: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:267: in _objective_gradient
    jac = xp.sum(self._objective_jacobian(x), axis=0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:237: in _objective_jacobian
    grads = term.operator.gradient(term.entries, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_value.py:42: in gradient
    weights = as_backend(self.weights, first)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
______________________ test_jacobian_has_one_row_per_term ______________________

mixed_problem = <pyRadPlan.optimization.problems._nonlin_fluence.NonLinearFluencePlanningProblem object at 0x7f51867c7d90>

    def test_jacobian_has_one_row_per_term(mixed_problem):
        prob = mixed_problem
        x = _fluence(prob)
>       gradient = xp_utils.to_numpy(prob._objective_gradient(x))

test/optimization/test_robust_fluence_optiprob.py:284: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:267: in _objective_gradient
    jac = xp.sum(self._objective_jacobian(x), axis=0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:237: in _objective_jacobian
    grads = term.operator.gradient(term.entries, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_value.py:42: in gradient
    weights = as_backend(self.weights, first)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
__________ test_shipped_operators_match_finite_differences[logsumexp] __________

worst_case_problem = <pyRadPlan.optimization.problems._nonlin_fluence.NonLinearFluencePlanningProblem object at 0x7f51a02a4910>

    @pytest.mark.parametrize("worst_case_problem", ["logsumexp", "pnorm"], indirect=True)
    def test_shipped_operators_match_finite_differences(worst_case_problem):
        prob = worst_case_problem
        names = [term.operator.name for term in prob._terms]
        assert names == [
            "objective_worst_case",
            "composite_worst_case",
            "voxel_worst_case",
            "scenario_stack",
            "voxel_worst_case_inverted",
            "expected_quantity",
        ]
        assert len(prob._terms[1].items) == 2
        rng = np.random.default_rng(2)
        for seed in range(2):
            x = _fluence(prob, seed)
>           gradient = xp_utils.to_numpy(prob._objective_gradient(x))

test/optimization/test_robust_fluence_optiprob.py:386: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:267: in _objective_gradient
    jac = xp.sum(self._objective_jacobian(x), axis=0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:237: in _objective_jacobian
    grads = term.operator.gradient(term.entries, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_base.py:197: in gradient
    grad = objective.compute_gradient(values[0], weights=self.weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:69: in compute_gradient
    _, weights, deviation = self._deviation(values, weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:43: in _deviation
    xp_utils.from_numpy(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
____________ test_shipped_operators_match_finite_differences[pnorm] ____________

worst_case_problem = <pyRadPlan.optimization.problems._nonlin_fluence.NonLinearFluencePlanningProblem object at 0x7f51a02a42d0>

    @pytest.mark.parametrize("worst_case_problem", ["logsumexp", "pnorm"], indirect=True)
    def test_shipped_operators_match_finite_differences(worst_case_problem):
        prob = worst_case_problem
        names = [term.operator.name for term in prob._terms]
        assert names == [
            "objective_worst_case",
            "composite_worst_case",
            "voxel_worst_case",
            "scenario_stack",
            "voxel_worst_case_inverted",
            "expected_quantity",
        ]
        assert len(prob._terms[1].items) == 2
        rng = np.random.default_rng(2)
        for seed in range(2):
            x = _fluence(prob, seed)
>           gradient = xp_utils.to_numpy(prob._objective_gradient(x))

test/optimization/test_robust_fluence_optiprob.py:386: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:267: in _objective_gradient
    jac = xp.sum(self._objective_jacobian(x), axis=0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:237: in _objective_jacobian
    grads = term.operator.gradient(term.entries, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_base.py:197: in gradient
    grad = objective.compute_gradient(values[0], weights=self.weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:69: in compute_gradient
    _, weights, deviation = self._deviation(values, weights)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/objectives/_variance.py:43: in _deviation
    xp_utils.from_numpy(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_________________ test_robust_fluence_optimization_end_to_end __________________

proton_case = ({'machine': 'Generic', 'numOfFractions': 30, 'propDoseCalc': {'doseGrid': {'resolution': {'x': 10, 'y': 10, 'z': 10}}...es=None, bio_model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose']))

    def test_robust_fluence_optimization_end_to_end(proton_case):
        from pyRadPlan.optimization import fluence_optimization
        from pyRadPlan.plan import validate_pln
    
        pln, ct, cst, stf, dij = proton_case
        cst.vois[0].objectives = [
            SquaredDeviation(priority=100.0, d_ref=1.0, robustness="composite_worst_case")
        ]
        cst.vois[1].objectives = [
            SquaredOverdosing(priority=10.0, d_max=0.3, robustness="expected_value")
        ]
        pln = validate_pln(
            {
                **dict(pln),
                "prop_opt": {
                    "solver": {"name": "scipy", "max_iter": 3, "display": False},
                    "convert_dose_objectives": False,
                    "max_approximation": "logsumexp",
                },
            }
        )
        robust_dij = _dij_with(dij, SCALES, _scenario_set())
>       w = fluence_optimization(ct, cst, stf, robust_dij, pln)

test/optimization/test_robust_fluence_optiprob.py:420: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/_fluence_optimization.py:95: in fluence_optimization
    x, result_info = planning_prob.solve(_ct, _cst, _stf, _dij)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_optiprob.py:604: in solve
    x, info = self._solve()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:303: in _solve
    result = self.solver.solve(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.optimization.problems._factory:_factory.py:138 No PlanningProblem specified in Plan. Using first available problem nonlin_fluence.
_ test_reinitializing_a_problem_with_other_scenarios_resets_the_gradient_caches _

proton_case = ({'machine': 'Generic', 'numOfFractions': 30, 'propDoseCalc': {'doseGrid': {'resolution': {'x': 10, 'y': 10, 'z': 10}}...es=None, bio_model=None, num_of_scenarios=1, total_num_of_bixels=12, num_of_voxels=2000, quantities=['physical_dose']))

    def test_reinitializing_a_problem_with_other_scenarios_resets_the_gradient_caches(proton_case):
        pln, ct, cst, stf, dij = proton_case
        _set_robustness(cst, "none")
        prob = _problem(proton_case, _dij_with(dij, SCALES, _scenario_set()))
        x = _fluence(prob)
        prob._objective_gradient(x)
    
        _set_robustness(cst, "expected_value")
        prob._initialize()
>       gradient = prob._objective_gradient(x)

test/optimization/test_robust_fluence_optiprob.py:465: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:267: in _objective_gradient
    jac = xp.sum(self._objective_jacobian(x), axis=0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/problems/_nonlin_fluence.py:237: in _objective_jacobian
    grads = term.operator.gradient(term.entries, values)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_expected_value.py:42: in gradient
    weights = as_backend(self.weights, first)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/robustness/_utils.py:26: in as_backend
    xp_utils.from_numpy(xp, np.asarray(array), device=array_api_compat.device(like)),
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
__________________________ test_simple_problem_scipy ___________________________

    def test_simple_problem_scipy():
        solver = get_solver("scipy")
    
        # Define the problem
        def objective(x):
            return xp.sum(x**2)
    
        def gradient(x):
            return 2 * x
    
        solver.objective = objective
        solver.gradient = gradient
    
        # Initial guess
        x0 = xp.asarray([1.0, 1.0], dtype=xp.float64)
    
        # Solve
>       result = solver.solve(x0)

test/optimization/test_scipy_solver.py:40: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
___________________ test_scipy_emits_status_through_callback ___________________

    def test_scipy_emits_status_through_callback():
        solver = _quadratic_solver()
        reports = []
        solver.status_callback = lambda message="", **data: reports.append(data) or True
    
>       solver.solve(xp.asarray([1.0, 1.0], dtype=xp.float64))

test/optimization/test_scipy_solver.py:57: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
__________ test_allow_keyboard_cancel_is_honoured_after_construction ___________

    def test_allow_keyboard_cancel_is_honoured_after_construction():
        """The flag is solver-level config, so a change after __init__ must reach the listener."""
        solver = _quadratic_solver()
        assert solver._keyboard_listener.allow_keyboard_cancel is True  # class default for scipy
    
        solver.allow_keyboard_cancel = False
>       solver.solve(xp.asarray([1.0, 1.0], dtype=xp.float64))

test/optimization/test_scipy_solver.py:71: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
___________ test_cancel_key_settings_are_honoured_after_construction ___________

    def test_cancel_key_settings_are_honoured_after_construction():
        """``cancel_key`` / ``allow_esc_cancel`` are solver-level config like the enable flag."""
        solver = _quadratic_solver()
        solver.allow_keyboard_cancel = False  # keep the listener thread out of the test
        solver.cancel_key = "x"
        solver.allow_esc_cancel = False
    
>       solver.solve(xp.asarray([1.0, 1.0], dtype=xp.float64))

test/optimization/test_scipy_solver.py:87: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
_____________________ test_scipy_status_callback_can_stop ______________________

    def test_scipy_status_callback_can_stop():
        solver = _quadratic_solver()
        calls = []
    
        def _cb(message="", **data):
            calls.append(data)
            return False  # request stop on the very first iteration
    
        solver.status_callback = _cb
>       x, _info = solver.solve(xp.asarray([5.0, 5.0], dtype=xp.float64))

test/optimization/test_scipy_solver.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_base_solvers.py:118: in solve
    x, status = self._solve_problem(x0)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:192: in _solve_problem
    result = minimize(
/usr/lib64/python3.14/site-packages/scipy/optimize/_minimize.py:784: in minimize
    res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
/usr/lib64/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:413: in _minimize_lbfgsb
    sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
/usr/lib64/python3.14/site-packages/scipy/optimize/_optimize.py:310: in _prepare_scalar_function
    sf = ScalarFunction(fun, x0, args, grad, hess,
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:274: in __init__
    self._update_fun()
/usr/lib64/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:353: in _update_fun
    fx = self._wrapped_fun(self.x)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:590: in __call__
    fx = self.f(np.copy(x), *self.args)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/optimization/solvers/_scipy_solver.py:185: in scipy_objective
    return xp_utils.to_numpy(self.objective(xp_utils.from_numpy(xp, x, device=device)))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:691: in from_numpy
    device = _parse_device_to_dlpack(device)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:198: in _parse_device_to_dlpack
    if array_api_strict is not None and isinstance(device, array_api_strict.Device):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'Device'

    def __getattr__(name):
        if name in ['linalg', 'fft']:
            if name in get_array_api_strict_flags()['enabled_extensions']:
                if name == 'linalg':
                    from . import _linalg
                    return _linalg
                elif name == 'fft':
                    from . import _fft
                    return _fft
            else:
                raise AttributeError(f"The {name!r} extension has been disabled for array_api_strict")
>       raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
E       AttributeError: module 'array_api_strict' has no attribute 'Device'. Did you mean: 'divide'?

/usr/lib/python3.14/site-packages/array_api_strict/__init__.py:344: AttributeError
___________________ test_rbe_x_dose_uses_voxel_param_column ____________________

two_scenario_dij = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=2, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])

    def test_rbe_x_dose_uses_voxel_param_column(two_scenario_dij):
        dij = two_scenario_dij
        rbe = RBExDose(dij)
        assert list(rbe.scenarios) == [0, 1]
>       result = rbe(xp.arange(N_BIX, dtype=xp.float32))

test/quantities/test_multi_scenario_quantities.py:305: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:254: in __call__
    return self.compute(fluence)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([ 9.23858  , 10.549352 , 11.840602 ,
       10.603649 , 11.967773 ,  5.453381 ,
       12.007432 , 10.000741 ,  ...  7.3490834,
        5.7154975, 10.329718 , 10.610678 ,
       13.411147 , 13.160281 ], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_____________ test_alias_scenarios_share_dose_but_not_voxel_params _____________

alias_dij = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=2, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])

    def test_alias_scenarios_share_dose_but_not_voxel_params(alias_dij):
        dij = alias_dij
        assert dij.voxel_param_column(0) == 0
        assert dij.voxel_param_column(1) == 1
        fluence = xp.arange(N_BIX, dtype=xp.float32)
        w = np.arange(N_BIX, dtype=np.float32)
    
        dose = Dose(dij, scenarios=[0, 1])
        dose_result = dose(fluence)
        assert dose_result.flat[1] is dose_result.flat[0]
    
        rbe = RBExDose(dij, scenarios=[0, 1])
>       result = rbe(fluence)

test/quantities/test_multi_scenario_quantities.py:357: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:254: in __call__
    return self.compute(fluence)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([10.521752 ,  7.7439146, 11.06683  ,
       10.885351 , 11.675577 , 15.509579 ,
        7.965419 ,  9.880358 , 1... 10.343923 ,
        9.865117 ,  6.355603 , 12.273873 ,
       12.153698 , 10.404239 ], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
____________ test_derived_scenario_is_linear_combination[RBExDose] _____________

derived_dij = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=3, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])
quantity_cls = <class 'pyRadPlan.quantities._rbe_x_dose.RBExDose'>

    @pytest.mark.parametrize("quantity_cls", [Dose, Effect, RBExDose])
    def test_derived_scenario_is_linear_combination(derived_dij, quantity_cls):
        quantity = quantity_cls(derived_dij, scenarios=[0, 1, 2])
        fluence = xp.arange(N_BIX, dtype=xp.float32)
>       result = quantity(fluence)

test/quantities/test_multi_scenario_quantities.py:385: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:254: in __call__
    return self.compute(fluence)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([ 7.3446918, 12.032897 ,  8.084624 ,
        9.836628 ,  6.666314 ,  9.0304165,
        7.779864 ,  6.5125656, 1... 11.938342 ,
       11.60448  , 10.413535 ,  8.711117 ,
        9.674815 , 12.916856 ], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_____________ test_extend_scenarios_appends_containers_and_columns _____________

    def test_extend_scenarios_appends_containers_and_columns():
        rng = np.random.default_rng(3)
        first = _single_dij(rng, _phase_set([0]), alphas=(0.1,))
        second = _single_dij(rng, _phase_set([1]), alphas=(0.5,))
    
        merged = first.extend_scenarios(second)
        assert merged.num_of_scenarios == 2
        assert merged.physical_dose.shape == (2,)
        assert merged.physical_dose.flat[0] is first.physical_dose.flat[0]
        assert merged.physical_dose.flat[1] is second.physical_dose.flat[0]
        assert [merged.voxel_param_column(i) for i in range(2)] == [0, 1]
        assert np.allclose(merged.alphax[:, merged.voxel_param_column(1)], 0.5)
        assert np.allclose(merged.scenarios.ct_phases(), [0, 1])
    
        rbe = RBExDose(merged)
>       result = rbe(xp.arange(N_BIX, dtype=xp.float32))

test/quantities/test_multi_scenario_quantities.py:469: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:254: in __call__
    return self.compute(fluence)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([11.840266 , 12.32343  ,  8.011889 ,
       12.554123 ,  8.772326 , 10.576969 ,
        7.7545686,  7.588301 , 1... 12.832273 ,
        9.58289  ,  8.894093 , 12.701789 ,
        8.755134 , 10.094968 ], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
________________ test_result_arrays_preserve_array_api_backend _________________

two_scenario_dij = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=2, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])

    def test_result_arrays_preserve_array_api_backend(two_scenario_dij):
        dij = two_scenario_dij.to_namespace(xp, keep_sparse_compat=False)
>       result = dij.get_result_arrays_from_intensity(xp.arange(N_BIX, dtype=xp.float32))

test/quantities/test_multi_scenario_quantities.py:553: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/_dij.py:1011: in get_result_arrays_from_intensity
    out = self._result_arrays(intensity, scenario_index)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/_dij.py:1155: in _result_arrays
    out["rbe_x_dose"] = lq_inverse_dose(out["effect"], alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([ 9.23858  , 10.549352 , 11.840602 ,
       10.603649 , 11.967773 ,  5.453381 ,
       12.007432 , 10.000741 ,  ...  7.3490834,
        5.7154975, 10.329718 , 10.610678 ,
       13.411147 , 13.160281 ], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
__________________________ test_RBExDose_effect_dense __________________________

sample_dij_dense = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=1, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])

    def test_RBExDose_effect_dense(sample_dij_dense):
        rbe_x_dose = RBExDose(sample_dij_dense)
    
        fluence = xp.arange(10, dtype=xp.float32)
>       ret_callable = rbe_x_dose(fluence)

test/quantities/test_rbe_x_dose_quantity.py:122: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:254: in __call__
    return self.compute(fluence)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([92., 92., 92., 92., 92., 92., 92.,
       92., 92., 92., 92., 92., 92., 92.,
       92., 92., 92., 92., 92., 92...., 92.,
       92., 92., 92., 92., 92., 92., 92.,
       92., 92., 92., 92., 92., 92.], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_________________________ test_RBExDose_effect_sparse __________________________

sample_dij_sparse = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=1, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])

    def test_RBExDose_effect_sparse(sample_dij_sparse):
        rbe_x_dose = RBExDose(sample_dij_sparse)
    
        fluence = xp.arange(10, dtype=xp.float32)
>       ret_callable = rbe_x_dose(fluence)

test/quantities/test_rbe_x_dose_quantity.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:254: in __call__
    return self.compute(fluence)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([ 2., 92.,  2.,  2.,  2.,  2.,  2.,
        2., 92.,  2.,  2.,  2., 92.,  2.,
        2.,  2.,  2.,  2.,  2.,  2...., 92.,
       92.,  2., 92., 92.,  2.,  2.,  2.,
        2., 92.,  2.,  2.,  2.,  2.], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_______ test_lq_inverse_dose_supports_linear_limit_and_immutable_arrays ________

    def test_lq_inverse_dose_supports_linear_limit_and_immutable_arrays():
        effect = xp.asarray([2.0, 2.0, 2.0])
        alpha_x = xp.asarray([1.0, 2.0, 0.0])
        beta_x = xp.asarray([1.0, 0.0, 0.0])
    
>       result = lq_inverse_dose(effect, alpha_x, beta_x)

test/quantities/test_rbe_x_dose_quantity.py:323: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True, False], dtype=array_api_strict.bool)
x1 = Array([4., 4., 0.], dtype=array_api_strict.float64), x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
______ test_lq_inverse_dose_is_accurate_in_single_precision_at_low_effect ______

    def test_lq_inverse_dose_is_accurate_in_single_precision_at_low_effect():
        effect = np.asarray([1e-7, 1e-4, 0.5, 4.0])
        alpha_x, beta_x = np.full(4, 0.1), np.full(4, 0.05)
        reference = (np.sqrt(alpha_x**2 + 4 * beta_x * effect) - alpha_x) / (2 * beta_x)
    
>       result = lq_inverse_dose(
            xp.asarray(effect, dtype=xp.float32),
            xp.asarray(alpha_x, dtype=xp.float32),
            xp.asarray(beta_x, dtype=xp.float32),
        )

test/quantities/test_rbe_x_dose_quantity.py:333: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([0.2000001 , 0.20009995, 0.43166247,
       1.        ], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
________ test_lq_inverse_dose_without_alpha_x_solves_the_quadratic_term ________

    def test_lq_inverse_dose_without_alpha_x_solves_the_quadratic_term():
>       result = lq_inverse_dose(
            xp.asarray([4.0, 0.0]), xp.asarray([0.0, 0.0]), xp.asarray([1.0, 1.0])
        )

test/quantities/test_rbe_x_dose_quantity.py:343: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True, False], dtype=array_api_strict.bool)
x1 = Array([4., 0.], dtype=array_api_strict.float64), x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
___________ test_rbe_x_dose_effect_evaluates_in_the_matrix_precision ___________

sample_dij_sparse = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=1, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])

    def test_rbe_x_dose_effect_evaluates_in_the_matrix_precision(sample_dij_sparse):
        """Double-precision reference parameters do not promote single-precision matrices."""
        dij = sample_dij_sparse.model_copy()
        dij.alphax = np.full((125, 1), 0.1, dtype=np.float64)
        dij.betax = np.full((125, 1), 0.05, dtype=np.float64)
        quantity = RBExDose(dij)
        fluence = xp.ones(dij.total_num_of_bixels, dtype=xp.float32)
    
>       rbe_x_dose = quantity(fluence).flat[0]

test/quantities/test_rbe_x_dose_quantity.py:358: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:254: in __call__
    return self.compute(fluence)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([0.2      , 0.2      , 4.7914815,
       4.7914815, 4.7914815, 4.7914815,
       0.2      , 0.2      , 4.7914815...     , 0.2      ,
       0.2      , 0.2      , 0.2      ,
       0.2      , 0.2      ], dtype=array_api_strict.float32)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
___________ test_RBExDose_effect_gradient_matches_finite_differences ___________

sample_dij_float64_effect = Dij(dose_grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 3.0}, dimensions=(5, 5, 5), origin=array([0., 0., 0.]), direct...f_scenarios=1, total_num_of_bixels=10, num_of_voxels=125, quantities=['physical_dose', 'alpha_dose', 'sqrt_beta_dose'])

    def test_RBExDose_effect_gradient_matches_finite_differences(sample_dij_float64_effect):
        quantity = RBExDose(sample_dij_float64_effect)
        assert quantity.path == "effect"
    
        fluence = np.linspace(0.2, 1.4, 10)
        rng = np.random.default_rng(3)
        d_quantity = rng.random((1, 125))
    
        # the effect path linearises around the current fluence, so evaluate it first
>       quantity.compute(xp.asarray(fluence, dtype=xp.float64))

test/quantities/test_rbe_x_dose_quantity.py:443: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:279: in compute
    self._compute_quantity_cache()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:381: in _compute_quantity_cache
    self._q_cache.flat[scenario_index] = self._compute_quantity_single_scenario(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:154: in _compute_quantity_single_scenario
    return lq_inverse_dose(effect_slice, alphax, betax)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_rbe_x_dose.py:26: in lq_inverse_dose
    return xp.where(positive, 2 * effect / xp.where(positive, denominator, 1.0), 0.0)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([ True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,...True,
        True,  True,  True,  True,  True,
        True,  True,  True,  True,  True], dtype=array_api_strict.bool)
x1 = Array([0.77934319, 0.6634629 , 0.68883752,
       0.67811777, 0.75610558, 0.75023457,
       0.77080284, 0.70231174, 0... 0.69353287,
       0.71981528, 0.74937788, 0.66143098,
       0.67821522, 0.73136959], dtype=array_api_strict.float64)
x2 = 1.0

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
______________________________ test_stfgen_to_stf ______________________________

tg119_raw = ({'cube': array([[[0.02670588, 0.02670588, 0.02670588, ..., 0.02670588,
         0.02670588, 0.02670588],
        [0.0...0.1, 'betaX': 0.05, ...}, {'className': 'DoseObjectives.matRad_SquaredOverdosing', 'parameters': 30, 'penalty': 100}]])

    def test_stfgen_to_stf(tg119_raw):
        ct_raw, cst_raw = tg119_raw
    
        ct = validate_ct(ct_raw)
        cst = validate_cst(cst_raw, ct=ct)
        pln = IonPlan(radiation_mode="protons", machine="Generic")
    
        stfgen = StfGeneratorIMPT(pln)
        stfgen.bixel_width = 5.0
        stfgen.gantry_angles = [0.0]
    
>       stf_native = stfgen.generate(ct, cst)

test/stf/test_stf.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_base.py:86: in generate
    stf = self._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:228: in _generate_source_geometry
    stf = super()._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_externalbeam.py:237: in _generate_source_geometry
    beam["rays"] = self._create_rays(beam)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:293: in _create_rays
    _, lengths, rhos, _, _ = rt.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 3.1226115 , -3.1398883 ],
        [ 0.48825425,  0.51330423],
        [ 2.0174017 , -2.0138483 ]],

       [... 3.1398883 ],
        [ 0.48825425,  0.51330423],
        [-2.0174017 ,  2.0138483 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
___________________________ test_from_and_to_matrad ____________________________

test_data_stf_n_beams_raw = {'__globals__': [], '__header__': b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Fri Nov  8 13:50:07 2024', '__...000, 10000], 'bixelWidth': [5, 5, 5, 5, 5], 'couchAngle': [0, 0, 0, 0, 0], 'gantryAngle': [0, 72, 144, 216, 288], ...}}

    def test_from_and_to_matrad(test_data_stf_n_beams_raw):
        stf_from_matRad = create_stf(test_data_stf_n_beams_raw)
>       stf_to_matrad = stf_from_matRad.to_matrad()

test/stf/test_stf.py:181: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_steeringinformation.py:120: in to_matrad
    export = super().to_matrad(context=context)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/datamodel.py:191: in to_matrad
    return self_copy.model_dump(by_alias=True, context=context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = SteeringInformation(beams=[Beam(gantry_angle=0.0, couch_angle=0.0, bixel_width=5.0, radiation_mode='protons', machine=...   9,  9,  9,  8,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9]), num_of_rays=221, total_number_of_bixels=4140)])

    def model_dump(
        self,
        *,
        mode: Literal['json', 'python'] | str = 'python',
        include: IncEx | None = None,
        exclude: IncEx | None = None,
        context: Any | None = None,
        by_alias: bool | None = None,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        exclude_computed_fields: bool = False,
        round_trip: bool = False,
        warnings: bool | Literal['none', 'warn', 'error'] = True,
        fallback: Callable[[Any], Any] | None = None,
        serialize_as_any: bool = False,
    ) -> dict[str, Any]:
        """!!! abstract "Usage Documentation"
            [`model_dump`](../concepts/serialization.md#python-mode)
    
        Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
    
        Args:
            mode: The mode in which `to_python` should run.
                If mode is 'json', the output will only contain JSON serializable types.
                If mode is 'python', the output may contain non-JSON-serializable Python objects.
            include: A set of fields to include in the output.
            exclude: A set of fields to exclude from the output.
            context: Additional context to pass to the serializer.
            by_alias: Whether to use the field's alias in the dictionary key if defined.
            exclude_unset: Whether to exclude fields that have not been explicitly set.
            exclude_defaults: Whether to exclude fields that are set to their default value.
            exclude_none: Whether to exclude fields that have a value of `None`.
            exclude_computed_fields: Whether to exclude computed fields.
                While this can be useful for round-tripping, it is usually recommended to use the dedicated
                `round_trip` parameter instead.
            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
            fallback: A function to call when an unknown value is encountered. If not provided,
                a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
    
        Returns:
            A dictionary representation of the model.
        """
>       return self.__pydantic_serializer__.to_python(
            self,
            mode=mode,
            by_alias=by_alias,
            include=include,
            exclude=exclude,
            context=context,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            exclude_computed_fields=exclude_computed_fields,
            round_trip=round_trip,
            warnings=warnings,
            fallback=fallback,
            serialize_as_any=serialize_as_any,
        )
E       pydantic_core._pydantic_core.PydanticSerializationError: Error calling function `custom_rays_serializer`: TypeError: Cannot interpret 'numpydantic.ndarray.NDArray[tuple[typing.Literal[3]], numpy.float64]' as a data type

/usr/lib/python3.14/site-packages/pydantic/main.py:464: PydanticSerializationError
________ test_generate_with_round_tripped_plan_machine[protons-loaded] _________

sample_ct = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f5185d4cc30> >, reso... 0.,  3.,  6.,  9., 12., 15., 18., 21., 24., 27., 30., 33., 36.,
       39., 42.]), z=array([ 0.,  4.,  8., 12., 16.]))
sample_cst = StructureSet(vois=[VOI(name='testtarget', grid=Grid(resolution={'x': 2.0, 'y': 3.0, 'z': 4.0}, dimensions=(25, 15, 5),...0.,  3.,  6.,  9., 12., 15., 18., 21., 24., 27., 30., 33., 36.,
       39., 42.]), z=array([ 0.,  4.,  8., 12., 16.])))
radiation_mode = 'protons', default_mode = False

    @pytest.mark.parametrize("default_mode", [False, True], ids=["loaded", "default-mode"])
    @pytest.mark.parametrize("radiation_mode", ["protons", "photons"])
    def test_generate_with_round_tripped_plan_machine(
        sample_ct, sample_cst, radiation_mode, default_mode
    ):
        """Steering generation works with a loaded machine after a plan dump round trip."""
        machine = load_from_name(radiation_mode, "Generic")
        if default_mode:
            data = machine.model_dump(exclude_unset=True)
            del data["radiation_mode"]
            machine = type(machine).model_validate(data)
        pln = create_pln(radiation_mode=radiation_mode, machine=machine, prop_stf={"bixel_width": 5})
        pln = create_pln(pln.model_dump())
    
        stf_gen = get_generator(pln)
        stf_gen.gantry_angles = [0.0]
        stf_gen.couch_angles = [0.0]
>       stf = stf_gen.generate(sample_ct, sample_cst)

test/stf/test_stf_gen.py:278: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_base.py:86: in generate
    stf = self._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:228: in _generate_source_geometry
    stf = super()._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_externalbeam.py:237: in _generate_source_geometry
    beam["rays"] = self._create_rays(beam)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:293: in _create_rays
    _, lengths, rhos, _, _ = rt.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 2.5    , -2.5    ],
        [ 0.49895,  0.5012 ],
        [ 1.     , -1.     ]],

       [[ 2.5    , -2.5   ...   [[-2.5    ,  2.5    ],
        [ 0.49895,  0.5012 ],
        [    -inf,      inf]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.stf.generators._factory:_factory.py:105 No generator specified in Plan. Using first available generator IMPT.
_____ test_generate_with_round_tripped_plan_machine[protons-default-mode] ______

sample_ct = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f518723d5c0> >, reso... 0.,  3.,  6.,  9., 12., 15., 18., 21., 24., 27., 30., 33., 36.,
       39., 42.]), z=array([ 0.,  4.,  8., 12., 16.]))
sample_cst = StructureSet(vois=[VOI(name='testtarget', grid=Grid(resolution={'x': 2.0, 'y': 3.0, 'z': 4.0}, dimensions=(25, 15, 5),...0.,  3.,  6.,  9., 12., 15., 18., 21., 24., 27., 30., 33., 36.,
       39., 42.]), z=array([ 0.,  4.,  8., 12., 16.])))
radiation_mode = 'protons', default_mode = True

    @pytest.mark.parametrize("default_mode", [False, True], ids=["loaded", "default-mode"])
    @pytest.mark.parametrize("radiation_mode", ["protons", "photons"])
    def test_generate_with_round_tripped_plan_machine(
        sample_ct, sample_cst, radiation_mode, default_mode
    ):
        """Steering generation works with a loaded machine after a plan dump round trip."""
        machine = load_from_name(radiation_mode, "Generic")
        if default_mode:
            data = machine.model_dump(exclude_unset=True)
            del data["radiation_mode"]
            machine = type(machine).model_validate(data)
        pln = create_pln(radiation_mode=radiation_mode, machine=machine, prop_stf={"bixel_width": 5})
        pln = create_pln(pln.model_dump())
    
        stf_gen = get_generator(pln)
        stf_gen.gantry_angles = [0.0]
        stf_gen.couch_angles = [0.0]
>       stf = stf_gen.generate(sample_ct, sample_cst)

test/stf/test_stf_gen.py:278: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_base.py:86: in generate
    stf = self._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:228: in _generate_source_geometry
    stf = super()._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_externalbeam.py:237: in _generate_source_geometry
    beam["rays"] = self._create_rays(beam)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:293: in _create_rays
    _, lengths, rhos, _, _ = rt.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 2.5    , -2.5    ],
        [ 0.49895,  0.5012 ],
        [ 1.     , -1.     ]],

       [[ 2.5    , -2.5   ...   [[-2.5    ,  2.5    ],
        [ 0.49895,  0.5012 ],
        [    -inf,      inf]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
------------------------------ Captured log call -------------------------------
WARNING  pyRadPlan.stf.generators._factory:_factory.py:105 No generator specified in Plan. Using first available generator IMPT.
__________________________________ test_impt ___________________________________

sample_ct = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51877f5650> >, reso... 0.,  3.,  6.,  9., 12., 15., 18., 21., 24., 27., 30., 33., 36.,
       39., 42.]), z=array([ 0.,  4.,  8., 12., 16.]))
sample_cst = StructureSet(vois=[VOI(name='testtarget', grid=Grid(resolution={'x': 2.0, 'y': 3.0, 'z': 4.0}, dimensions=(25, 15, 5),...0.,  3.,  6.,  9., 12., 15., 18., 21., 24., 27., 30., 33., 36.,
       39., 42.]), z=array([ 0.,  4.,  8., 12., 16.])))
sample_proton_pln = IonPlan(prop_stf={'bixel_width': 2.0}, prop_opt={}, prop_dose_calc={}, prop_seq={}, num_of_fractions=30, dose_conventi...ies=[1.0]), wc_sigma=1.0, distribution='independent_gaussian'), bio_model=ConstantRBEModel(), radiation_mode='protons')

    def test_impt(sample_ct, sample_cst, sample_proton_pln):
        stf_gen = StfGeneratorIMPT(sample_proton_pln)
    
        stf_gen.gantry_angles = [0.0, 270.0]
        stf_gen.couch_angles = [0.0, 90.0]
    
>       stf = stf_gen.generate(sample_ct, sample_cst)

test/stf/test_stf_gen.py:291: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_base.py:86: in generate
    stf = self._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:228: in _generate_source_geometry
    stf = super()._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_externalbeam.py:237: in _generate_source_geometry
    beam["rays"] = self._create_rays(beam)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:293: in _create_rays
    _, lengths, rhos, _, _ = rt.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 1.25     , -1.25     ],
        [ 0.49895  ,  0.5012   ],
        [ 0.625    , -0.625    ]],

       [[ 1.25...   ,  1.25     ],
        [ 0.49895  ,  0.5012   ],
        [-1.25     ,  1.25     ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
_______________________________ test_impt_tg119 ________________________________

tg119 = {'cst': StructureSet(vois=[OAR(name='Core', grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 2.5}, dimensions=(167, 167,...130. ,  132.5,  135. ,  137.5,
        140. ,  142.5,  145. ,  147.5,  150. ,  152.5,  155. ,  157.5,
        160. ]))}
sample_proton_pln = IonPlan(prop_stf={'bixel_width': 2.0}, prop_opt={}, prop_dose_calc={}, prop_seq={}, num_of_fractions=30, dose_conventi...ies=[1.0]), wc_sigma=1.0, distribution='independent_gaussian'), bio_model=ConstantRBEModel(), radiation_mode='protons')

    def test_impt_tg119(tg119, sample_proton_pln):
        ct = tg119["ct"]
        cst = tg119["cst"]
    
        stf_gen = StfGeneratorIMPT(sample_proton_pln)
        stf_gen.bixel_width = 5.0
>       stf = stf_gen.generate(ct, cst)

test/stf/test_stf_gen.py:317: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_base.py:86: in generate
    stf = self._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:228: in _generate_source_geometry
    stf = super()._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_externalbeam.py:237: in _generate_source_geometry
    beam["rays"] = self._create_rays(beam)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:293: in _create_rays
    _, lengths, rhos, _, _ = rt.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 3.1226115 , -3.1398883 ],
        [ 0.48825425,  0.51330423],
        [ 2.0174017 , -2.0138483 ]],

       [... 3.1398883 ],
        [ 0.48825425,  0.51330423],
        [-2.0174017 ,  2.0138483 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
___________ test_impt_longitudinal_spot_spacing_thins_energy_layers ____________

tg119 = {'cst': StructureSet(vois=[OAR(name='Core', grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 2.5}, dimensions=(167, 167,...130. ,  132.5,  135. ,  137.5,
        140. ,  142.5,  145. ,  147.5,  150. ,  152.5,  155. ,  157.5,
        160. ]))}
sample_proton_pln = IonPlan(prop_stf={'bixel_width': 2.0}, prop_opt={}, prop_dose_calc={}, prop_seq={}, num_of_fractions=30, dose_conventi...ies=[1.0]), wc_sigma=1.0, distribution='independent_gaussian'), bio_model=ConstantRBEModel(), radiation_mode='protons')

    def test_impt_longitudinal_spot_spacing_thins_energy_layers(tg119, sample_proton_pln):
        """Energy layers must be spaced by at least longitudinal_spot_spacing in peak position."""
        ct, cst = tg119["ct"], tg119["cst"]
    
        def spots_and_layers(spacing):
            stf_gen = StfGeneratorIMPT(sample_proton_pln)
            stf_gen.bixel_width = 5.0
            stf_gen.longitudinal_spot_spacing = spacing
            beams = stf_gen.generate(ct, cst)
            spots = sum(len(ray.beamlets) for beam in beams for ray in beam["rays"])
            layers = np.unique(
                np.concatenate([ray.energies for beam in beams for ray in beam["rays"]])
            )
            return spots, layers, stf_gen
    
>       dense_spots, dense_layers, gen = spots_and_layers(0.0)

test/stf/test_stf_gen.py:361: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
test/stf/test_stf_gen.py:354: in spots_and_layers
    beams = stf_gen.generate(ct, cst)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_base.py:86: in generate
    stf = self._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:228: in _generate_source_geometry
    stf = super()._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_externalbeam.py:237: in _generate_source_geometry
    beam["rays"] = self._create_rays(beam)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:293: in _create_rays
    _, lengths, rhos, _, _ = rt.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 3.1226115 , -3.1398883 ],
        [ 0.48825425,  0.51330423],
        [ 2.0174017 , -2.0138483 ]],

       [... 3.1398883 ],
        [ 0.48825425,  0.51330423],
        [-2.0174017 ,  2.0138483 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
_________________ test_impt_zero_spot_spacing_keeps_all_layers _________________

tg119 = {'cst': StructureSet(vois=[OAR(name='Core', grid=Grid(resolution={'x': 3.0, 'y': 3.0, 'z': 2.5}, dimensions=(167, 167,...130. ,  132.5,  135. ,  137.5,
        140. ,  142.5,  145. ,  147.5,  150. ,  152.5,  155. ,  157.5,
        160. ]))}
sample_proton_pln = IonPlan(prop_stf={'bixel_width': 2.0}, prop_opt={}, prop_dose_calc={}, prop_seq={}, num_of_fractions=30, dose_conventi...ies=[1.0]), wc_sigma=1.0, distribution='independent_gaussian'), bio_model=ConstantRBEModel(), radiation_mode='protons')

    def test_impt_zero_spot_spacing_keeps_all_layers(tg119, sample_proton_pln):
        """A spacing of zero disables the thinning entirely."""
        ct, cst = tg119["ct"], tg119["cst"]
    
        counts = []
        for spacing in (0.0, 1e-9):
            stf_gen = StfGeneratorIMPT(sample_proton_pln)
            stf_gen.bixel_width = 5.0
            stf_gen.longitudinal_spot_spacing = spacing
>           beams = stf_gen.generate(ct, cst)

test/stf/test_stf_gen.py:387: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_base.py:86: in generate
    stf = self._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:228: in _generate_source_geometry
    stf = super()._generate_source_geometry()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_externalbeam.py:237: in _generate_source_geometry
    beam["rays"] = self._create_rays(beam)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/generators/_ions.py:293: in _create_rays
    _, lengths, rhos, _, _ = rt.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ 3.1226115 , -3.1398883 ],
        [ 0.48825425,  0.51330423],
        [ 2.0174017 , -2.0138483 ]],

       [... 3.1398883 ],
        [ 0.48825425,  0.51330423],
        [-2.0174017 ,  2.0138483 ]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
----------------------------- Captured stderr call -----------------------------
                                          
_______________________ test_raytracer_trace_single_ray ________________________

sample_cube = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51858c47b0> >

    def test_raytracer_trace_single_ray(sample_cube):
        raytracer = RayTracerSiddon(sample_cube)
    
        isocenter = sample_cube.TransformIndexToPhysicalPoint([3, 2, 1])
    
        source_points = np.array([0, -5, 0]).astype(float)
        target_points = np.array([0, 5, 0]).astype(float)
    
>       alpha, l, rho, d12, ix = raytracer.trace_ray(isocenter, source_points, target_points)

test/test_raytracersiddon.py:51: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:57: in trace_ray
    alphas, lengths, rho, d12, ix = self.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[-inf,  inf],
        [0.25, 0.75],
        [-inf,  inf]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
______________________ test_raytracer_trace_multiple_rays ______________________

sample_cube = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51a0b93ab0> >

    def test_raytracer_trace_multiple_rays(sample_cube):
        raytracer = RayTracerSiddon(sample_cube)
    
        isocenter = sample_cube.TransformIndexToPhysicalPoint([3, 2, 1])
    
        source_points = np.array([[0, -5, 0], [0, -5, 0]]).astype(float)
        target_points = np.array([[0, 5, 0], [2, 5, 0]]).astype(float)
    
>       alpha, l, rho, d12, ix = raytracer.trace_rays(isocenter, source_points, target_points)

test/test_raytracersiddon.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ -inf,   inf],
        [ 0.25,  0.75],
        [ -inf,   inf]],

       [[-1.75,  1.75],
        [ 0.25,  0.75],
        [ -inf,   inf]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_____________________ test_raytracer_trace_multiple_cubes ______________________

sample_cube = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51858c7bd0> >

    def test_raytracer_trace_multiple_cubes(sample_cube):
        raytracer = RayTracerSiddon([sample_cube, sample_cube])
    
        isocenter = sample_cube.TransformIndexToPhysicalPoint([3, 2, 1])
    
        source_points = np.array([[0, -5, 0], [0, -5, 0]]).astype(float)
        target_points = np.array([[0, 5, 0], [2, 5, 0]]).astype(float)
    
>       alpha, l, rho, d12, ix = raytracer.trace_rays(isocenter, source_points, target_points)

test/test_raytracersiddon.py:109: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ -inf,   inf],
        [ 0.25,  0.75],
        [ -inf,   inf]],

       [[-1.75,  1.75],
        [ 0.25,  0.75],
        [ -inf,   inf]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
_______________________ test_raytracer_ray_does_not_hit ________________________

sample_cube = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51858c54d0> >

    def test_raytracer_ray_does_not_hit(sample_cube):
        raytracer = RayTracerSiddon(sample_cube)
    
        isocenter = sample_cube.TransformIndexToPhysicalPoint([3, 2, 1])
    
        source_point = np.array([100, -5, 100]).astype(float)
        target_point = np.array([100, 5, 100]).astype(float)
    
>       alpha, l, rho, d12, ix = raytracer.trace_ray(isocenter, source_point, target_point)

test/test_raytracersiddon.py:130: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:57: in trace_ray
    alphas, lengths, rho, d12, ix = self.trace_rays(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[-inf, -inf],
        [0.25, 0.75],
        [-inf, -inf]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
______ test_readonly_sitk_buffer_is_copied_for_backend[array_api_strict] _______

sample_cube = <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0x7f51858c7e10> >
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51adc89e10>
backend = 'array_api_strict'

    @pytest.mark.parametrize("backend", ["array_api_strict", "torch"])
    def test_readonly_sitk_buffer_is_copied_for_backend(sample_cube, monkeypatch, backend):
        if backend == "torch":
            pytest.importorskip("torch")
    
        monkeypatch.setattr(settings.xp, "prefer_gpu", False)
        monkeypatch.setattr(settings.xp, "preferred_cpu_array_backend", backend)
        cube_before = sitk.GetArrayViewFromImage(sample_cube).copy()
        raytracer = RayTracerSiddon(sample_cube)
        isocenter = sample_cube.TransformIndexToPhysicalPoint([3, 2, 1])
    
        with warnings.catch_warnings(record=True) as caught:
>           _, _, rho, _, ix = raytracer.trace_rays(
                isocenter,
                np.array([[0.0, -5.0, 0.0]]),
                np.array([[0.0, 5.0, 0.0], [2.0, 5.0, 0.0]]),
            )

test/test_raytracersiddon.py:170: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:88: in trace_rays
    alphas, lengths, rho, d12, ix = self._trace_rays_core(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[ -inf,   inf],
        [ 0.25,  0.75],
        [ -inf,   inf]],

       [[-1.75,  1.75],
        [ 0.25,  0.75],
        [ -inf,   inf]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
__________ test_trace_depth_arrays_stay_on_backend[array_api_strict] ___________

monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7f51adc8a900>
backend = 'array_api_strict'

    @pytest.mark.parametrize("backend", ["numpy", "cupy", "array_api_strict"])
    def test_trace_depth_arrays_stay_on_backend(monkeypatch, backend):
        """The array interface avoids host conversion while the image wrapper preserves metadata."""
        module = pytest.importorskip(backend)
        if backend == "cupy" and not module.cuda.is_available():
            pytest.skip("CUDA unavailable")
        monkeypatch.setattr(settings.xp, "prefer_gpu", backend == "cupy")
        monkeypatch.setattr(
            settings.xp, "preferred_cpu_array_backend", backend if backend != "cupy" else "numpy"
        )
        monkeypatch.setattr(settings.xp, "preferred_gpu_array_backend", "cupy")
        cube = sitk.GetImageFromArray(np.ones((5, 6, 7), dtype=np.float32))
        cube.SetSpacing((1.3, 1.7, 2.1))
        cube.SetOrigin((-3.0, -4.0, -5.0))
        tracer = RayTracerSiddon(cube)
        beam = Beam(
            gantry_angle=30.0,
            couch_angle=20.0,
            iso_center=np.zeros(3),
            SAD=10000.0,
            rays=[Ray(ray_pos_bev=np.zeros(3), ray_pos=np.zeros(3), beamlets=[Beamlet(energy=100.0)])],
        )
>       expected = tracer.trace_cubes(beam)[0]

test/test_raytracersiddon.py:274: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:183: in trace_cubes
    return self._depth_arrays_to_images(self.trace_cubes_array(beam, patient_coords))
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_base.py:290: in trace_cubes_array
    lengths, rho, ix = self._trace_rays_device(
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:116: in _trace_rays_device
    _, lengths, rho, _, ix = self._trace_rays_core(isocenter, source_points, target_points)
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:161: in _trace_rays_core
    alphas = self._compute_all_alphas()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:258: in _compute_all_alphas
    alpha_limits = self._compute_alpha_limits()
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:402: in _compute_alpha_limits
    alpha_axis_min = xp.min(xp.where(alpha_nans, -xp.inf, alpha_planes), axis=-1)  # (N, 3)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

condition = Array([[[False, False],
        [False, False],
        [False, False]],

       [[False, False],
        [False, Fals...False, False]],

       [[False, False],
        [False, False],
        [False, False]]], dtype=array_api_strict.bool)
x1 = -inf
x2 = Array([[[0.9954338 , 0.9935073 ],
        [0.99896014, 1.0001373 ],
        [1.0225602 , 1.028861  ]],

       [[0.995...39 , 1.0026199 ],
        [0.99832463, 0.999501  ],
        [0.9701518 , 0.97612965]]], dtype=array_api_strict.float32)

    def where(condition: Array, x1: Array, x2: Array, /) -> Array:
        """
        Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
    
        See its docstring for more information.
        """
        # Call result type here just to raise on disallowed type combinations
>       _result_type(x1.dtype, x2.dtype)
E       AttributeError: 'float' object has no attribute 'dtype'

/usr/lib/python3.14/site-packages/array_api_strict/_searching_functions.py:78: AttributeError
=============================== warnings summary ===============================
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:36
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:36
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:36: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    origin: Union[NDArray[Shape["3"], np.float64], NDArray[Shape["4"], np.float64]] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:36
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:36
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:36: DeprecationWarning: String shape expressions like '4' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    origin: Union[NDArray[Shape["3"], np.float64], NDArray[Shape["4"], np.float64]] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:39
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:39
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:39: DeprecationWarning: String shape expressions like '3,3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    direction: Union[NDArray[Shape["3,3"], np.float64], NDArray[Shape["4,4"], np.float64]] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:39
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:39
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_grids.py:39: DeprecationWarning: String shape expressions like '4,4' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    direction: Union[NDArray[Shape["3,3"], np.float64], NDArray[Shape["4,4"], np.float64]] = Field(

../../../../../usr/lib/python3.14/site-packages/array_api_strict/_flags.py:344
  /usr/lib/python3.14/site-packages/array_api_strict/_flags.py:344: UserWarning: The 2023.12 version of the array API specification is still preliminary. Some functions are not yet implemented, and it has not been fully tested.
    set_array_api_strict_flags(**kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/_base.py:44
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/_base.py:44
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/_base.py:44: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    tissue_properties: Optional[NDArray[Shape["1-*"], TissueProperties]] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/_external_beam.py:26
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/_external_beam.py:26
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/_external_beam.py:26: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    energies: NDArray[Shape["1-*"], np.float64]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:37
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:37
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:37: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    kernel_betas: NDArray[Shape["1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:40
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:40
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:40: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    kernel_ssds: NDArray[Shape["1-*"], np.float64] = Field(description="Kernel SSD points")

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:41
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:41
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:41: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    kernel_pos: NDArray[Shape["1-*"], np.float64] = Field(description="Kernel position")

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:42
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:42
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:42: DeprecationWarning: String shape expressions like '1-*,1-*, 1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    kernel_data: NDArray[Shape["1-*,1-*, 1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:46
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:46
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/photons/_svd_kernel.py:46: DeprecationWarning: String shape expressions like '1-*,2' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    primary_fluence: NDArray[Shape["1-*,2"], np.float64] = Field(description="Primary fluence")

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_focus.py:16
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_focus.py:16
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_focus.py:16: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    dist: NDArray[Shape["1-*"], np.float64]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_focus.py:17
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_focus.py:17
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_focus.py:17: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    sigma: NDArray[Shape["1-*"], np.float64]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:58
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:58
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:58: DeprecationWarning: String shape expressions like '1-*, 1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    fluence_spectrum: NDArray[Shape["1-*, 1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:61
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:61
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:61: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    energy: NDArray[Shape["1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:64
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:64
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_fragment_spectrum.py:64: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    fluenceZ: NDArray[Shape["1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_cutoff.py:16
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_cutoff.py:16
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_cutoff.py:16: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    cut_off: NDArray[Shape["1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_cutoff.py:19
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_cutoff.py:19
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_cutoff.py:19: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    depths: NDArray[Shape["1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:203
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:203: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    cls, v: Union[NDArray[Shape["1-*"], np.float64], None], info: ValidationInfo

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:204
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:204: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    ) -> Union[NDArray[Shape["1-*"], np.float64], None]:

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:258
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:258: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    @computed_field(return_type=NDArray[Shape["1-*"], np.float64], alias="alphaBetaRatio")

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:28
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:28
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:28: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    depths: NDArray[Shape["1-*"], np.float64]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:31
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:31
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:31: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    idd: NDArray[Shape["1-*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:34
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:34
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:34: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    sigma: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:35
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:35
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:35: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    sigma_1: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:36
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:36
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:36: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    sigma_2: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:37
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:37
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:37: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    sigma_x: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:38
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:38
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:38: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    sigma_y: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:39
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:39
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:39: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    weight: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:40
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:40
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:40: DeprecationWarning: String shape expressions like '1-*,1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    sigma_multi: Optional[NDArray[Shape["1-*,1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:42
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:42
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:42: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    Union[NDArray[Shape["1-*"], np.float64], NDArray[Shape["1-*,1-*"], np.float64]]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:42
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:42
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:42: DeprecationWarning: String shape expressions like '1-*,1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    Union[NDArray[Shape["1-*"], np.float64], NDArray[Shape["1-*,1-*"], np.float64]]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:44
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:44
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:44: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    let: Optional[NDArray[Shape["1-*"], np.float64]] = Field(alias="LET", default=None)

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:45
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:45
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:45: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    zs: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:46
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:46
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:46: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    alpha_x: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:47
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:47
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:47: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    beta_x: Optional[NDArray[Shape["1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:48
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:48
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:48: DeprecationWarning: String shape expressions like '1-*,1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    alpha: Optional[NDArray[Shape["1-*,1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:49
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:49
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/kernel/_base.py:49: DeprecationWarning: String shape expressions like '1-*,1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    beta: Optional[NDArray[Shape["1-*,1-*"], np.float64]] = None

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_energy.py:73
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_energy.py:73
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_energy.py:73: DeprecationWarning: String shape expressions like '*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    energy: NDArray[Shape["*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_energy.py:76
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_energy.py:76
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_beam_energy.py:76: DeprecationWarning: String shape expressions like '*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    weight: NDArray[Shape["*"], np.float64] = Field(description="Weight of each energy bin")

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_base.py:43
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_base.py:43
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_base.py:43: DeprecationWarning: String shape expressions like '2,*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    lut_spot_size: NDArray[Shape["2,*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_ions.py:44
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_ions.py:44
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/particles/_ions.py:44: DeprecationWarning: String shape expressions like '1-*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    peak_positions: NDArray[Shape["1-*"], np.float64]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:69
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:69: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    ) -> NDArray[Shape["3"], np.float64]:

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:57
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:57
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:57: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    ray_pos_bev: NDArray[Shape["3"], np.float64] = Field(alias="rayPos_bev")

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:58
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:58
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:58: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    ray_pos: NDArray[Shape["3"], np.float64]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:60
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:60
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:60: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    target_point: Optional[NDArray[Shape["3"], np.float64]] = Field(default=None)

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:61
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:61
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_ray.py:61: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    target_point_bev: Optional[NDArray[Shape["3"], np.float64]] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:79
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:79
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:79: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    iso_center: NDArray[Shape["3"], np.float64]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:84
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:84
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:84: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    source_point_bev: NDArray[Shape["3"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:87
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:87
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/stf/_beam.py:87: DeprecationWarning: String shape expressions like '3' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    source_point: NDArray[Shape["3"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/_dij.py:39
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/_dij.py:39: DeprecationWarning: String shape expressions like '*, ...' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    InfluenceMatrixContainer = NDArray[Shape["*, ..."], object]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/_dij.py:39
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dij/_dij.py:39: DeprecationWarning: Treating '...' in a string as meaning any number of dimensions of any size is now written as *Repeat. The old behavior is deprecated in 2.0 and will be removed in 3.0.
    InfluenceMatrixContainer = NDArray[Shape["*, ..."], object]

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:74
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:74
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:74: DeprecationWarning: String shape expressions like '*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    diff_volume: NDArray[Shape["*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:81
../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:81
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:81: DeprecationWarning: String shape expressions like '*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    bin_edges: NDArray[Shape["*"], np.float64] = Field(

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:128
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:128: DeprecationWarning: String shape expressions like '*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    def cum_volume(self) -> NDArray[Shape["*"], np.float64]:

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:140
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:140: DeprecationWarning: String shape expressions like '2,*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    def cumulative(self) -> NDArray[Shape["2,*"], np.float64]:

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:161
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:161: DeprecationWarning: String shape expressions like '2,*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    def differential(self) -> NDArray[Shape["2,*"], np.float64]:

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:197
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:197: DeprecationWarning: String shape expressions like '*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    def bins(self) -> NDArray[Shape["*"], np.float64]:

../BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:223
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/analysis/_dvh.py:223: DeprecationWarning: String shape expressions like '*' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    def bin_centers(self) -> NDArray[Shape["*"], np.float64]:

test/scenarios/test_scenario_models.py:7
test/scenarios/test_scenario_models.py:7
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_models.py:7: DeprecationWarning: NominalScenario is deprecated and will be removed after one release; use NominalScenarioModel.
    from pyRadPlan.scenarios import (

test/scenarios/test_scenario_set.py:351
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_set.py:351: SyntaxWarning: "\[" is an invalid escape sequence. Such sequences will not work in the future. Did you mean "\\["? A raw string is also an option.
    with pytest.raises(ValueError, match="Derived scenarios \[3\]"):

test/scenarios/test_scenario_set.py:353
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_set.py:353: SyntaxWarning: "\[" is an invalid escape sequence. Such sequences will not work in the future. Did you mean "\\["? A raw string is also an option.
    with pytest.raises(ValueError, match="Derived scenarios \[2, 3\]"):

test/test_plan.py:12
test/test_plan.py:12
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_plan.py:12: DeprecationWarning: NominalScenario is deprecated and will be removed after one release; use NominalScenarioModel.
    from pyRadPlan.scenarios import NominalScenario

test/test_scenarios.py:10
test/test_scenarios.py:10
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:10: DeprecationWarning: NominalScenario is deprecated and will be removed after one release; use NominalScenarioModel.
    from pyRadPlan.scenarios import NominalScenario, NominalScenarioModel, ScenarioModel

test/biology/models/test_custom_model_extension.py: 1 warning
test/dose/test_base_pencilbeam.py: 5 warnings
test/dose/test_bio_tissue_check.py: 26 warnings
test/dose/test_default_bio_models.py: 17 warnings
test/dose/test_dose_engine_base.py: 10 warnings
test/dose/test_engine_reuse.py: 6 warnings
test/dose/test_factory.py: 2 warnings
test/dose/test_fred.py: 16 warnings
test/dose/test_fred_bio.py: 17 warnings
test/dose/test_hongpb.py: 29 warnings
test/dose/test_hongpb_bio.py: 21 warnings
test/dose/test_particle_interpolators.py: 2 warnings
test/dose/test_particle_lateral_cutoff.py: 1 warning
test/dose/test_pb_geodist.py: 4 warnings
test/dose/test_scenarios_pencilbeam.py: 44 warnings
test/dose/test_svdpb.py: 3 warnings
test/dose/test_svdpb_bio.py: 3 warnings
test/dose/test_topas.py: 15 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base.py:229: UserWarning: Requested GPU device is not available on array_api_strict. Falling back to CPU.
    self.device = choose_device()

test/biology/models/test_custom_model_extension.py: 1 warning
test/dose/test_bio_tissue_check.py: 19 warnings
test/dose/test_default_bio_models.py: 14 warnings
test/dose/test_engine_reuse.py: 2 warnings
test/dose/test_fred_bio.py: 1 warning
test/dose/test_hongpb.py: 27 warnings
test/dose/test_hongpb_bio.py: 14 warnings
test/dose/test_particle_interpolators.py: 2 warnings
test/dose/test_particle_lateral_cutoff.py: 1 warning
test/dose/test_scenarios_pencilbeam.py: 20 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_base_pencilbeam.py:392: UserWarning: stf does not record use_given_eq_density_cube and ignore_outside_densities (e.g. because it was imported), so its energy selection cannot be verified against the dose engine (use_given_eq_density_cube=False, ignore_outside_densities=False). Make sure the stf was generated with the same settings.
    warnings.warn(

test/biology/models/test_custom_model_extension.py: 1 warning
test/dose/test_bio_tissue_check.py: 19 warnings
test/dose/test_default_bio_models.py: 14 warnings
test/dose/test_engine_reuse.py: 6 warnings
test/dose/test_fred_bio.py: 1 warning
test/dose/test_hongpb.py: 27 warnings
test/dose/test_hongpb_bio.py: 14 warnings
test/dose/test_particle_interpolators.py: 2 warnings
test/dose/test_particle_lateral_cutoff.py: 1 warning
test/dose/test_scenarios_pencilbeam.py: 41 warnings
test/dose/test_svdpb.py: 3 warnings
test/dose/test_svdpb_bio.py: 3 warnings
test/stf/test_stf.py: 1 warning
test/stf/test_stf_gen.py: 6 warnings
test/test_raytracersiddon.py: 6 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:29: UserWarning: Requested GPU device is not available on array_api_strict. Falling back to CPU.
    self.device = xp_utils.choose_device()

test/biology/models/test_custom_model_extension.py: 1 warning
test/dose/test_bio_tissue_check.py: 19 warnings
test/dose/test_default_bio_models.py: 14 warnings
test/dose/test_engine_reuse.py: 6 warnings
test/dose/test_hongpb.py: 27 warnings
test/dose/test_hongpb_bio.py: 12 warnings
test/dose/test_particle_interpolators.py: 2 warnings
test/dose/test_particle_lateral_cutoff.py: 1 warning
test/dose/test_scenarios_pencilbeam.py: 42 warnings
test/dose/test_svdpb.py: 3 warnings
test/dose/test_svdpb_bio.py: 3 warnings
test/stf/test_stf.py: 1 warning
test/stf/test_stf_gen.py: 6 warnings
test/test_raytracersiddon.py: 4 warnings
  /usr/lib/python3.14/site-packages/array_api_strict/_array_object.py:862: RuntimeWarning: divide by zero encountered in divide
    res = self._array.__truediv__(other._array)

test/core/test_resample.py::test_resample_numpy_array
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/core/test_resample.py:36: DeprecationWarning: resample_numpy_array is deprecated; use resample_array instead.
    resampled_array = resample_numpy_array(

test/core/test_resample.py::test_resample_numpy_array_with_target_image
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/core/test_resample.py:44: DeprecationWarning: resample_numpy_array is deprecated; use resample_array instead.
    resampled_array = resample_numpy_array(sample_array, sample_image, target_image=sample_image)

test/core/xp_utils/test_initialization.py::test_choose_device_defaults
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/core/xp_utils/test_initialization.py:111: UserWarning: Requested GPU device is not available on array_api_strict. Falling back to CPU.
    dev = choose_device()

test/core/xp_utils/test_initialization.py::test_choose_device_numpy
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/core/xp_utils/test_initialization.py:192: UserWarning: Requested GPU device is not available on array_api_compat.numpy. Falling back to CPU.
    dev = choose_device(xp)

test/core/xp_utils/test_reductions.py::test_nan_and_initial[numpy]
test/core/xp_utils/test_reductions.py::test_nan_initial[numpy]
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/_reductions.py:56: RuntimeWarning: invalid value encountered in maximum
    xp.maximum.at(result, groups, values)

test/cst/test_cst_4d_ct.py::test_4d_structures_resample_phase_by_phase
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/cst/_voi.py:1125: UserWarning: Resampling created an empty structure
    warnings.warn("Resampling created an empty structure")

test/dose/test_dose_engine_base.py::test_DoseEngineBase_init
test/dose/test_dose_engine_base.py::test_assign_properties_from_pln
test/dose/test_dose_engine_base.py::test_calc_dose_forward
test/dose/test_dose_engine_base.py::test_calc_dose_influence
test/dose/test_dose_engine_base.py::test_init_dose_calc
test/dose/test_dose_engine_base.py::test_init_dose_calc_uses_plan_machine
test/dose/test_dose_engine_base.py::test_plan_machine_dict_is_validated_once
test/dose/test_dose_engine_base.py::test_init_dose_calc_rejects_plan_machine_not_matching_stf
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "dosimetric_lateral_cutoff" not found in DummyDoseEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/dose/test_dose_engine_base.py::test_DoseEngineBase_init
test/dose/test_dose_engine_base.py::test_assign_properties_from_pln
test/dose/test_dose_engine_base.py::test_calc_dose_forward
test/dose/test_dose_engine_base.py::test_calc_dose_influence
test/dose/test_dose_engine_base.py::test_init_dose_calc
test/dose/test_dose_engine_base.py::test_init_dose_calc_uses_plan_machine
test/dose/test_dose_engine_base.py::test_plan_machine_dict_is_validated_once
test/dose/test_dose_engine_base.py::test_init_dose_calc_rejects_plan_machine_not_matching_stf
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "lateral_model" not found in DummyDoseEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/dose/test_dose_engine_base.py: 1 warning
test/dose/test_engine_reuse.py: 4 warnings
test/dose/test_scenarios_pencilbeam.py: 25 warnings
test/dose/test_svdpb.py: 3 warnings
test/dose/test_svdpb_bio.py: 3 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "lateral_model" not found in PhotonPencilBeamSVDEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/dose/test_factory.py::test_get_engine_invalid
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/dose/engines/_factory.py:140: UserWarning: Engine 'InvalidEngine' not available for Plan.
    warnings.warn(f"Engine '{pln.prop_dose_calc['engine']}' not available for Plan.")

test/dose/test_fred.py: 10 warnings
test/dose/test_scenarios_pencilbeam.py: 1 warning
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "dosimetric_lateral_cutoff" not found in ParticleFredMCEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/dose/test_fred.py: 10 warnings
test/dose/test_scenarios_pencilbeam.py: 1 warning
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "lateral_model" not found in ParticleFredMCEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/dose/test_particle_indexing.py: 24 warnings
  /usr/lib/python3.14/site-packages/array_api_strict/_flags.py:311: UserWarning: The 2023.12 version of the array API specification is still preliminary. Some functions are not yet implemented, and it has not been fully tested.
    set_array_api_strict_flags(**self.kwargs)

test/dose/test_particle_indexing.py: 24 warnings
  /usr/lib/python3.14/site-packages/array_api_strict/_flags.py:314: UserWarning: The 2023.12 version of the array API specification is still preliminary. Some functions are not yet implemented, and it has not been fully tested.
    set_array_api_strict_flags(**self.old_flags)

test/dose/test_scenarios_pencilbeam.py: 10 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "enable_dij_sampling" not found in ParticleHongPencilBeamEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/dose/test_scenarios_pencilbeam.py::test_ct_phase_selection_bio[False-numpy]
test/dose/test_scenarios_pencilbeam.py::test_ct_phase_selection_bio[True-numpy]
test/dose/test_scenarios_pencilbeam.py::test_sampling_with_setup_range_and_ct_scenarios[False-numpy]
test/dose/test_scenarios_pencilbeam.py::test_sampling_with_setup_range_and_ct_scenarios[True-numpy]
test/test_raytracersiddon.py::test_boundary_planes_do_not_create_duplicate_voxel_segments
test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[0.0-0.0]
test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[45.0-0.0]
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:388: RuntimeWarning: divide by zero encountered in divide
    (p_min - self._source_points) / self._ray_vec,  # alpha to "near" plane

test/dose/test_scenarios_pencilbeam.py::test_ct_phase_selection_bio[False-numpy]
test/dose/test_scenarios_pencilbeam.py::test_ct_phase_selection_bio[True-numpy]
test/dose/test_scenarios_pencilbeam.py::test_sampling_with_setup_range_and_ct_scenarios[False-numpy]
test/dose/test_scenarios_pencilbeam.py::test_sampling_with_setup_range_and_ct_scenarios[True-numpy]
test/test_raytracersiddon.py::test_boundary_planes_do_not_create_duplicate_voxel_segments
test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[0.0-0.0]
test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[45.0-0.0]
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_siddon.py:389: RuntimeWarning: divide by zero encountered in divide
    (p_max - self._source_points) / self._ray_vec,

test/dose/test_scenarios_pencilbeam.py::test_two_phase_nominal_model_validates
test/dose/test_scenarios_pencilbeam.py::test_explicit_model_is_accepted
  /usr/lib/python3.14/site-packages/pydantic/main.py:1073: DeprecationWarning: Plan.mult_scen is deprecated; use Plan.scenarios.
    return lambda model, _name, val: attr.__set__(model, val)

test/dose/test_scenarios_pencilbeam.py::test_public_entry_point_scenario_override
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/dose/test_scenarios_pencilbeam.py:380: DeprecationWarning: Plan.mult_scen is deprecated; use Plan.scenarios.
    model_before = pln.mult_scen.model_dump()

test/dose/test_scenarios_pencilbeam.py::test_sampling_with_setup_range_and_ct_scenarios[False-numpy]
test/dose/test_scenarios_pencilbeam.py::test_sampling_with_setup_range_and_ct_scenarios[True-numpy]
test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[0.0-0.0]
test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[45.0-0.0]
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/raytracer/_kernels.py:94: RuntimeWarning: invalid value encountered in multiply
    ijk = sp_scaled[:, :, None] + rv_scaled[:, :, None] * alphas_mid[:, None, :]

test/dose/test_topas.py::test_init_topas
test/dose/test_topas.py::test_default_simu_dir_is_in_user_data_dir
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "dosimetric_lateral_cutoff" not found in ParticleTOPASMCEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/dose/test_topas.py::test_init_topas
test/dose/test_topas.py::test_default_simu_dir_is_in_user_data_dir
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/_configurable.py:310: UserWarning: Property "lateral_model" not found in ParticleTOPASMCEngine!
    warnings.warn(f'Property "{key}" not found in {type(self).__name__}!')

test/io/test_io_dicom.py::test_import_ct_matches_reference
test/io/test_io_dicom.py::test_import_cst_matches_reference
test/io/test_io_dicom.py::test_import_dose_matches_reference
test/io/test_io_dicom.py::test_load_data_from_dicom_folder
  /usr/lib/python3.14/site-packages/pymatreader/utils.py:393: UserWarning: Complex objects (like classes) are not supported. They are imported on a best effort base but your mileage will vary.
    warn(

test/io/test_io_matlab.py::test_matlab_roundtrip_preserves_plan
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/io/test_io_matlab.py:212: DeprecationWarning: Plan.mult_scen is deprecated; use Plan.scenarios.
    assert reloaded.mult_scen.ct_scen_prob == [(0, 1.0)]

test/io/test_io_matlab.py::test_matlab_roundtrip_preserves_plan
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/io/test_io_matlab.py:212: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert reloaded.mult_scen.ct_scen_prob == [(0, 1.0)]

test/io/test_io_sitk.py::test_load_patient_single_nifti_file
test/io/test_io_sitk.py::test_single_file_ct_roundtrip
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/io/_load_save.py:150: UserWarning: cst/StructureSet is missing from the patient file.
    warnings.warn("cst/StructureSet is missing from the patient file.")

test/optimization/test_dose_convention.py: 21 warnings
test/optimization/test_fluence_optiprob.py: 7 warnings
test/optimization/test_obj_history.py: 6 warnings
test/optimization/test_opt_info.py: 11 warnings
test/optimization/test_robust_fluence_optiprob.py: 14 warnings
test/quantities/test_effect_quantity.py: 3 warnings
test/quantities/test_let_quantity.py: 5 warnings
test/quantities/test_multi_scenario_quantities.py: 7 warnings
test/quantities/test_rbe_x_dose_quantity.py: 12 warnings
test/quantities/test_resolver.py: 13 warnings
test/quantities/test_sqrt_beta_dose_quantity.py: 1 warning
test/test_dij_scenarios.py: 1 warning
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_resolver.py:47: UserWarning: Requested GPU device is not available on array_api_strict. Falling back to CPU.
    device = compute_backend.choose_device(xp)

test/optimization/test_dose_convention.py: 21 warnings
test/optimization/test_fluence_optiprob.py: 7 warnings
test/optimization/test_obj_history.py: 6 warnings
test/optimization/test_opt_info.py: 11 warnings
test/optimization/test_robust_fluence_optiprob.py: 14 warnings
test/quantities/test_alpha_dose_quantity.py: 3 warnings
test/quantities/test_dose_quantity.py: 3 warnings
test/quantities/test_effect_quantity.py: 3 warnings
test/quantities/test_let_dose_quantity.py: 3 warnings
test/quantities/test_let_quantity.py: 5 warnings
test/quantities/test_multi_scenario_quantities.py: 11 warnings
test/quantities/test_rbe_x_dose_quantity.py: 12 warnings
test/quantities/test_resolver.py: 11 warnings
test/quantities/test_sqrt_beta_dose_quantity.py: 4 warnings
test/test_dij_scenarios.py: 1 warning
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/quantities/_base.py:151: UserWarning: Requested GPU device is not available on array_api_strict. Falling back to CPU.
    device = compute_backend.choose_device(xp)

test/optimization/test_obj_history.py: 4 warnings
test/optimization/test_opt_info.py: 7 warnings
test/optimization/test_robust_fluence_optiprob.py: 5 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/core/xp_utils/helpers.py:484: UserWarning: Synchronization helper for namespace 'array_api_strict' is not implemented.
    warnings.warn(

test/scenarios/test_scenario_gridded.py: 31 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:81: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(model.scen_for_prob.shape, expected.shape)

test/scenarios/test_scenario_gridded.py: 31 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:82: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    np.testing.assert_allclose(model.scen_for_prob, expected, rtol=1e-12, atol=1e-12)

test/scenarios/test_scenario_gridded.py: 31 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:84: DeprecationWarning: ScenarioModel.scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    np.testing.assert_allclose(model.scen_prob, ref["scenProb"].reshape(-1), rtol=1e-10)

test/scenarios/test_scenario_gridded.py: 31 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:85: DeprecationWarning: ScenarioModel.scen_weight is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    np.testing.assert_allclose(model.scen_weight, ref["scenWeight"].reshape(-1), rtol=1e-10)

test/scenarios/test_scenario_gridded.py: 30 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:91: DeprecationWarning: ScenarioModel.linear_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(model.linear_mask, linear)

test/scenarios/test_scenario_gridded.py: 30 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:93: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.scen_mask.shape == mask_shape

test/scenarios/test_scenario_gridded.py: 30 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:94: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(model.scen_mask, ref["scenMask"].astype(bool))

test/scenarios/test_scenario_gridded.py: 30 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:95: DeprecationWarning: ScenarioModel.tot_num_shift_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.tot_num_shift_scen == int(_scalar(ref["totNumShiftScen"]))

test/scenarios/test_scenario_gridded.py: 30 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:96: DeprecationWarning: ScenarioModel.tot_num_range_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.tot_num_range_scen == int(_scalar(ref["totNumRangeScen"]))

test/scenarios/test_scenario_gridded.py: 30 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:97: DeprecationWarning: ScenarioModel.tot_num_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.tot_num_scen == int(_scalar(ref["totNumScen"])) == len(scen)

test/scenarios/test_scenario_gridded.py: 30 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:103: DeprecationWarning: ScenarioModel.tot_num_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    for i in range(model.tot_num_scen):

test/scenarios/test_scenario_gridded.py: 9966 warnings
test/scenarios/test_scenario_random.py: 1 warning
test/test_scenarios.py: 3 warnings
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/_legacy.py:175: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    np.ravel_multi_index((ct_scen, shift_scen, range_shift_scen), self.scen_mask.shape)

test/scenarios/test_scenario_gridded.py::test_even_grid_without_nominal_point_has_value_consistent_layout
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:129: DeprecationWarning: ScenarioModel.tot_num_shift_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.tot_num_shift_scen == 9

test/scenarios/test_scenario_gridded.py::test_wc_scen_defaults_and_counts
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:140: DeprecationWarning: ScenarioModel.tot_num_shift_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert (m.tot_num_shift_scen, m.tot_num_range_scen) == (7, 3)

test/scenarios/test_scenario_gridded.py::test_wc_scen_defaults_and_counts
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:140: DeprecationWarning: ScenarioModel.tot_num_range_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert (m.tot_num_shift_scen, m.tot_num_range_scen) == (7, 3)

test/scenarios/test_scenario_gridded.py::test_zero_sd_collapses_duplicates
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_gridded.py:177: DeprecationWarning: ScenarioModel.tot_num_range_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.tot_num_range_scen == 1

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_models.py:125: DeprecationWarning: ScenarioModel.range_rel_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.range_rel_sd == 3.5 and m.range_abs_sd == 1.0

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_models.py:125: DeprecationWarning: ScenarioModel.range_abs_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.range_rel_sd == 3.5 and m.range_abs_sd == 1.0

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_models.py:126: DeprecationWarning: ScenarioModel.shift_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.shift_sd == (2.25, 2.25, 2.25)

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_models.py:127: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.ct_scen_prob == [(0, 1.0)]

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /usr/lib/python3.14/site-packages/pydantic/main.py:1073: DeprecationWarning: ScenarioModel.range_rel_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    return lambda model, _name, val: attr.__set__(model, val)

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /usr/lib/python3.14/site-packages/pydantic/main.py:1073: DeprecationWarning: ScenarioModel.range_abs_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    return lambda model, _name, val: attr.__set__(model, val)

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /usr/lib/python3.14/site-packages/pydantic/main.py:1073: DeprecationWarning: ScenarioModel.shift_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    return lambda model, _name, val: attr.__set__(model, val)

test/scenarios/test_scenario_models.py::test_legacy_flat_properties_read_and_write
  /usr/lib/python3.14/site-packages/pydantic/main.py:1073: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    return lambda model, _name, val: attr.__set__(model, val)

test/scenarios/test_scenario_models.py::test_validate_scenario_model_ctscenprob_shapes[ct_scen_prob0]
test/scenarios/test_scenario_models.py::test_validate_scenario_model_ctscenprob_shapes[ct_scen_prob1]
test/scenarios/test_scenario_models.py::test_validate_scenario_model_ctscenprob_shapes[ct_scen_prob2]
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_models.py:258: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.ct_scen_prob == [(0, 1.0)]

test/scenarios/test_scenario_random.py::test_legacy_mask_is_diagonal
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:120: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.scen_mask.shape == (3, 4, 4)

test/scenarios/test_scenario_random.py::test_legacy_mask_is_diagonal
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:121: DeprecationWarning: ScenarioModel.tot_num_shift_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.tot_num_shift_scen == m.tot_num_range_scen == 4

test/scenarios/test_scenario_random.py::test_legacy_mask_is_diagonal
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:121: DeprecationWarning: ScenarioModel.tot_num_range_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.tot_num_shift_scen == m.tot_num_range_scen == 4

test/scenarios/test_scenario_random.py::test_legacy_mask_is_diagonal
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:122: DeprecationWarning: ScenarioModel.tot_num_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert m.tot_num_scen == 8

test/scenarios/test_scenario_random.py::test_legacy_mask_is_diagonal
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:125: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(m.scen_mask, expected)

test/scenarios/test_scenario_random.py::test_legacy_mask_is_diagonal
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:126: DeprecationWarning: ScenarioModel.linear_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(m.linear_mask, [[p, i, i] for p in (0, 1) for i in range(4)])

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:147: DeprecationWarning: ScenarioModel.linear_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(model.linear_mask, ref["linearMask"].astype(int) - 1)

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:148: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(model.scen_mask, ref["scenMask"].astype(bool))

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:149: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.scen_mask.shape == tuple(int(v) for v in ref["scenMaskSize"].reshape(-1))

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:150: DeprecationWarning: ScenarioModel.scen_weight is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    np.testing.assert_allclose(model.scen_weight, ref["scenWeight"].reshape(-1), rtol=1e-10)

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:151: DeprecationWarning: ScenarioModel.tot_num_shift_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.tot_num_shift_scen == int(ref["totNumShiftScen"].item())

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:152: DeprecationWarning: ScenarioModel.tot_num_range_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.tot_num_range_scen == int(ref["totNumRangeScen"].item())

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:153: DeprecationWarning: ScenarioModel.tot_num_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert model.tot_num_scen == int(ref["totNumScen"].item()) == len(s)

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:154: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    np.testing.assert_array_equal(model.scen_for_prob[:, 0], ref["scenForProb"][:, 0] - 1)

test/scenarios/test_scenario_random.py::test_matches_matrad_structure
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/scenarios/test_scenario_random.py:157: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.all(model.scen_for_prob[[0, 4], 1:] == 0)

test/test_keyboard_listener.py: 10 warnings
  /usr/lib64/python3.14/pty.py:66: DeprecationWarning: This process (pid=3838) is multi-threaded, use of forkpty() may lead to deadlocks in the child.
    pid, fd = os.forkpty()

test/test_machine_model.py::test_photon_machine_model_svdpb_get_kernel
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_machine_model.py:97: DeprecationWarning: String shape expressions like '501, 3, 360' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    assert isinstance(kernel.kernel_data, NDArray[Shape[f"{nssds}, {nk}, {npos}"], np.float64])

test/test_machine_model.py::test_photon_machine_model_svdpb_get_kernel
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_machine_model.py:100: DeprecationWarning: String shape expressions like '3, 360' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    assert isinstance(kernel1, NDArray[Shape[f"{nk}, {npos}"], np.float64])

test/test_machine_model.py::test_photon_machine_model_svdpb_get_kernel
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_machine_model.py:102: DeprecationWarning: String shape expressions like '3, 360' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    assert isinstance(kernel2, NDArray[Shape[f"{nk}, {npos}"], np.float64])

test/test_machine_model.py::test_photon_machine_model_svdpb_get_kernel
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_machine_model.py:108: DeprecationWarning: String shape expressions like '3, 360' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    assert isinstance(kernel_low, NDArray[Shape[f"{nk}, {npos}"], np.float64])

test/test_machine_model.py::test_photon_machine_model_svdpb_get_kernel
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_machine_model.py:113: DeprecationWarning: String shape expressions like '3, 360' are deprecated, use type expressions instead, e.g. Shape[L[3], int, Range[L[2], L[4]], *Repeat]
    assert isinstance(kernel_high, NDArray[Shape[f"{nk}, {npos}"], np.float64])

test/test_plan.py::test_create_pln_dict_photons_snake
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_plan.py:107: DeprecationWarning: Plan.mult_scen is deprecated; use Plan.scenarios.
    assert isinstance(pln.mult_scen, NominalScenario)

test/test_plan.py::test_create_pln_dict_photons_camel
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_plan.py:157: DeprecationWarning: Plan.mult_scen is deprecated; use Plan.scenarios.
    assert isinstance(pln.mult_scen, NominalScenario)

test/test_plan.py::test_legacy_photon_kernels_are_rescaled_exactly_once
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/machines/base/_external_beam.py:41: UserWarning: Meta data key version is already present in the data dictionary.
    warnings.warn(

test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[45.0-0.0]
test/test_raytracersiddon.py::test_trace_cubes_traverses_full_cube_at_oblique_angles[30.0-45.0]
test/test_raytracersiddon.py::test_trace_depth_arrays_stay_on_backend[numpy]
  /usr/lib64/python3.14/site-packages/numpy/lib/_function_base_impl.py:1518: RuntimeWarning: invalid value encountered in subtract
    a = op(a[slice1], a[slice2])

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:70: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.ct_scen_prob == [(0, 1.0)]

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:71: DeprecationWarning: ScenarioModel.num_of_ct_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.num_of_ct_scen == 1

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:72: DeprecationWarning: ScenarioModel.tot_num_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.tot_num_scen == 1

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:73: DeprecationWarning: ScenarioModel.tot_num_shift_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.tot_num_shift_scen == 1

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:74: DeprecationWarning: ScenarioModel.tot_num_range_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.tot_num_range_scen == 1

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:75: DeprecationWarning: ScenarioModel.rel_range_shift is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.rel_range_shift == 0.0

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:76: DeprecationWarning: ScenarioModel.abs_range_shift is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.abs_range_shift == 0.0

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:77: DeprecationWarning: ScenarioModel.iso_shift is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.iso_shift, np.array([[0.0, 0.0, 0.0]]))

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:78: DeprecationWarning: ScenarioModel.max_abs_range_shift is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.max_abs_range_shift == 0.0

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:79: DeprecationWarning: ScenarioModel.max_rel_range_shift is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.max_rel_range_shift == 0.0

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:80: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.scen_mask, np.ones((1, 1, 1)).astype(bool))

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:81: DeprecationWarning: ScenarioModel.linear_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.linear_mask, np.array([[0, 0, 0]]))

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:82: DeprecationWarning: ScenarioModel.ct_scen_ix is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.ct_scen_ix, [0])

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:83: DeprecationWarning: ScenarioModel.scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.allclose(scenario.scen_prob, helper_mvar_gauss(scenario))

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:31: DeprecationWarning: ScenarioModel.shift_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    scenario_model.shift_sd,

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:32: DeprecationWarning: ScenarioModel.range_abs_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    scenario_model.range_abs_sd,

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:33: DeprecationWarning: ScenarioModel.range_rel_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    scenario_model.range_rel_sd / 100.0,

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:40: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    tmp_standardized = np.linalg.solve(cs, scenario_model.scen_for_prob[:, 1:].T).T

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:49: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    for phase in scenario_model.scen_for_prob[:, 0]

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
test/test_scenarios.py::test_legacy_view_with_two_phases
test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:48: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    [x[1] for x in scenario_model.ct_scen_prob if x[0] == phase][0]

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:84: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.scen_for_prob, np.zeros((1, 6)))

test/test_scenarios.py::test_nominal_scenario_construct
test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:85: DeprecationWarning: ScenarioModel.scen_weight is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.scen_weight, np.ones((1,), dtype=float))

test/test_scenarios.py::test_nominal_scenario_construct_with_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:98: DeprecationWarning: ScenarioModel.num_of_available_ct_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.num_of_available_ct_scen == 1

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:103: DeprecationWarning: ScenarioModel.num_of_ct_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.num_of_ct_scen == 2

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:104: DeprecationWarning: ScenarioModel.num_of_available_ct_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.num_of_available_ct_scen == 3

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:105: DeprecationWarning: ScenarioModel.tot_num_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.tot_num_scen == 2

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:106: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.scen_mask.shape == (3, 1, 1)

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:107: DeprecationWarning: ScenarioModel.scen_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.scen_mask[:, 0, 0], [True, False, True])

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:108: DeprecationWarning: ScenarioModel.linear_mask is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.linear_mask, [[0, 0, 0], [2, 0, 0]])

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:109: DeprecationWarning: ScenarioModel.ct_scen_ix is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.ct_scen_ix, [0, 2])

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:114: DeprecationWarning: ScenarioModel.scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.allclose(scenario.scen_prob, helper_mvar_gauss(scenario))

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:115: DeprecationWarning: ScenarioModel.scen_weight is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.allclose(scenario.scen_weight, [0.5, 0.5])

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/scenarios/_legacy.py:190: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    return self.scen_for_prob

test/test_scenarios.py::test_legacy_view_with_two_phases
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:116: DeprecationWarning: ScenarioModel.scen_for_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.update_scenarios(), scenario.scen_for_prob)

test/test_scenarios.py::test_nominal_scenario_creation_by_legacy_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:142: DeprecationWarning: ScenarioModel.range_rel_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.range_rel_sd == 5.0

test/test_scenarios.py::test_nominal_scenario_creation_by_legacy_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:143: DeprecationWarning: ScenarioModel.range_abs_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.range_abs_sd == 2.5

test/test_scenarios.py::test_nominal_scenario_creation_by_legacy_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:144: DeprecationWarning: ScenarioModel.shift_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.shift_sd == (3.0, 3.0, 3.0)

test/test_scenarios.py::test_nominal_scenario_creation_by_legacy_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:145: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.ct_scen_prob == [(0, 0.5)]

test/test_scenarios.py::test_nominal_scenario_creation_by_matrad_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:154: DeprecationWarning: ScenarioModel.range_rel_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.range_rel_sd == 5.0

test/test_scenarios.py::test_nominal_scenario_creation_by_matrad_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:155: DeprecationWarning: ScenarioModel.range_abs_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.range_abs_sd == 2.5

test/test_scenarios.py::test_nominal_scenario_creation_by_matrad_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:156: DeprecationWarning: ScenarioModel.shift_sd is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.shift_sd == (3.0, 3.0, 3.0)

test/test_scenarios.py::test_nominal_scenario_creation_by_matrad_dict
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:157: DeprecationWarning: ScenarioModel.ct_scen_prob is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert scenario.ct_scen_prob == [(0, 0.5)]

test/test_scenarios.py::test_scenario_serialization_emits_no_pydantic_warning
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:175: DeprecationWarning: ScenarioModel.rel_range_shift is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.rel_range_shift, np.zeros(1))

test/test_scenarios.py::test_scenario_serialization_emits_no_pydantic_warning
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:176: DeprecationWarning: ScenarioModel.abs_range_shift is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert np.array_equal(scenario.abs_range_shift, np.zeros(1))

test/test_scenarios.py::test_nominal_scenario_extract_single_scenario
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyradplan-0.8.0/test/test_scenarios.py:183: DeprecationWarning: ScenarioModel.tot_num_scen is deprecated and will be removed after one release; configure the setup, range and ct_phase dimensions and query the ScenarioSet.
    assert single.tot_num_scen == 1

test/visualization/test_plot_slice.py::test_plot_slice_ct
  /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr/lib/python3.14/site-packages/pyRadPlan/visualization/_plot_slice.py:410: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
    _, axes = plt.subplots(rows, cols, figsize=(5 * cols, 5 * rows))

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
FAILED test/biology/models/test_custom_model_extension.py::test_custom_model_runs_in_particle_engine
FAILED test/biology/models/test_fragment_species_selection.py::test_spectrum_tables_are_cached_per_logical_device
FAILED test/biology/models/test_kernel_bio_model.py::test_KernelBasedLQModel_evaluate_mixed_tissue_classes
FAILED test/biology/models/test_kernel_bio_model.py::test_declared_tissue_classes_are_mapped_onto_kernel_rows_by_value
FAILED test/biology/models/test_kernel_bio_model.py::test_machine_without_tissue_parameters_uses_the_kernel_classes
FAILED test/biology/models/test_kernel_let_model.py::test_LinearScaling_alpha_beta
FAILED test/biology/models/test_reference_parameter_domain.py::test_outside_structure_voxels_evaluate_to_zero_array_api[LinearScaling]
FAILED test/biology/models/test_tabulated_kernel_model.py::test_TabulatedAlphaBetaModel_evaluate
FAILED test/biology/models/test_tissue_lookup.py::test_exact_gather - IndexEr...
FAILED test/biology/models/test_tissue_lookup.py::test_exact_gather_takes_one_dimensional_kernels_per_voxel
FAILED test/biology/models/test_tissue_lookup.py::test_exact_gather_skips_the_check_once_the_grid_is_validated
FAILED test/biology/models/test_tissue_lookup.py::test_gather_tissue_parameters_matches_the_per_bixel_conversion
FAILED test/biology/models/test_tissue_lookup.py::test_reference_cache_distinguishes_logical_devices
FAILED test/biology/models/test_tissue_lookup.py::test_gather_works_on_a_non_default_logical_device
FAILED test/biology/models/test_zstar_bio_model.py::test_MKM_evaluate_combines_kernel_rows_with_the_class_parameters
FAILED test/biology/models/test_zstar_bio_model.py::test_mkm_copies_no_host_data_per_bixel
FAILED test/biology/models/test_zstar_bio_model.py::test_MKM_evaluate_influence_weights_the_class_parameters_with_the_dose
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict-nearest-23]
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict-nearest-4]
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict--3.25-23]
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict--3.25-4]
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict-False-23]
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict-False-4]
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict-True-23]
FAILED test/core/test_resample_array_api.py::test_enums_extrapolation_and_backend[array_api_strict-True-4]
FAILED test/core/test_resample_array_api.py::test_small_bspline_fallback_is_preserved[array_api_strict]
FAILED test/core/xp_utils/compat/test_backend_consistency.py::test_interp1d_2d_boundaries[array_api_strict]
FAILED test/core/xp_utils/compat/test_backend_consistency.py::test_interp1d_container_boundaries[array_api_strict]
FAILED test/core/xp_utils/compat/test_backend_consistency.py::test_interp1d_multidimensional_queries[array_api_strict]
FAILED test/core/xp_utils/compat/test_backend_consistency.py::test_interpnd_bounds_and_multiple_queries[array_api_strict]
FAILED test/core/xp_utils/compat/test_interpnd.py::test_interpnd_generic_returns[2d]
FAILED test/core/xp_utils/compat/test_interpnd.py::test_interpnd_generic_returns[3d]
FAILED test/core/xp_utils/compat/test_interpnd.py::test_interpnd_generic_2d_handles_descending_axes
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_basic[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_basic[dtype1]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_on_points[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_on_points[dtype1]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_2darray[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_2darray[dtype1]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_multiple_y_stack_sequence[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_multiple_y_stack_sequence[dtype1]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1_multiple_y_sequence[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1_multiple_y_sequence[dtype1]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_mixed_1d_2d_sequence[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_mixed_1d_2d_sequence[dtype1]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_dict_input[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_dict_input[dtype1]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_out_of_bounds[dtype0]
FAILED test/core/xp_utils/compat/test_math_interp1d.py::test_interp1d_out_of_bounds[dtype1]
FAILED test/core/xp_utils/helpers/test_device_parsing.py::test_parse_array_api_strict_default_device
FAILED test/core/xp_utils/helpers/test_device_parsing.py::test_device_repr_containing_cpu_is_not_classified_as_cpu
FAILED test/core/xp_utils/helpers/test_device_parsing.py::test_device_cache_key_prefers_the_backend_device_object
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float32-2-True]
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float32-2-False]
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float32-3-True]
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float32-3-False]
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float64-2-True]
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float64-2-False]
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float64-3-True]
FAILED test/core/xp_utils/test_interpolation.py::test_repeated_queries[array_api_strict-float64-3-False]
FAILED test/core/xp_utils/test_interpolation.py::test_bounds_and_query_validation[array_api_strict-True]
FAILED test/core/xp_utils/test_interpolation.py::test_bounds_and_query_validation[array_api_strict-False]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape0-2-True]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape0-2-False]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape0-3-True]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape0-3-False]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape1-2-True]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape1-2-False]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape1-3-True]
FAILED test/core/xp_utils/test_interpolation.py::test_shared_value_dimensions[array_api_strict-value_shape1-3-False]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-array-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-array-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-fields-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-fields-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-mixed_rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-mixed_rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-mixed_dtype-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None-None-mixed_dtype-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-array-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-array-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-fields-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-fields-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_dtype-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-None--3.5-mixed_dtype-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-array-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-array-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-fields-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-fields-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_dtype-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723-None-mixed_dtype-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-array-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-array-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-fields-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-fields-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_dtype-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict--0.1723--3.5-mixed_dtype-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-array-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-array-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-fields-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-fields-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_dtype-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234-None-mixed_dtype-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-array-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-array-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-fields-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-fields-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_rows-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_rows-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_dtype-float32]
FAILED test/core/xp_utils/test_interpolation1d.py::test_prepared_matches_one_shot[array_api_strict-13.234--3.5-mixed_dtype-float64]
FAILED test/core/xp_utils/test_interpolation1d.py::test_dispatch_preference[array_api_strict-None]
FAILED test/core/xp_utils/test_interpolation1d.py::test_dispatch_preference[array_api_strict-True]
FAILED test/core/xp_utils/test_interpolation1d.py::test_dispatch_preference[array_api_strict-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue0-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue2-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue3-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[carbon-alpha_beta_kernels-tissue4-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-alpha_beta_kernels-tissue6-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-zstar_kernels-tissue8-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-zstar_kernels-tissue9-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[oxygen-LEM-tissue11-False]
FAILED test/dose/test_bio_tissue_check.py::test_requested_bio_dose_raises_exactly_when_the_check_reports_errors[protons-WED-tissue14-False]
FAILED test/dose/test_bio_tissue_check.py::test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[carbon-alpha_beta_kernels-tissue0]
FAILED test/dose/test_bio_tissue_check.py::test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[carbon-zstar_kernels-tissue1]
FAILED test/dose/test_bio_tissue_check.py::test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-alpha_beta_kernels-tissue2]
FAILED test/dose/test_bio_tissue_check.py::test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-zstar_kernels-tissue3]
FAILED test/dose/test_bio_tissue_check.py::test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[oxygen-zstar_kernels-tissue4]
FAILED test/dose/test_bio_tissue_check.py::test_automatic_bio_dose_falls_back_to_physical_dose_on_errors[protons-MCN-tissue5]
FAILED test/dose/test_bio_tissue_check.py::test_differing_provenance_label_warns_and_evaluates_the_model[oxygen-zstar_kernels-tissue0-relabelled0-auto]
FAILED test/dose/test_bio_tissue_check.py::test_differing_provenance_label_warns_and_evaluates_the_model[oxygen-zstar_kernels-tissue0-relabelled0-True]
FAILED test/dose/test_bio_tissue_check.py::test_differing_provenance_label_warns_and_evaluates_the_model[carbon-alpha_beta_kernels-tissue1-relabelled1-auto]
FAILED test/dose/test_bio_tissue_check.py::test_differing_provenance_label_warns_and_evaluates_the_model[carbon-alpha_beta_kernels-tissue1-relabelled1-True]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_matching_tissue[protons]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_matching_tissue[helium]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_matching_tissue[carbon]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_matching_tissue[oxygen]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[protons]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[helium]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[carbon]
FAILED test/dose/test_default_bio_models.py::test_default_bio_model_with_unmatched_tissue_keeps_physical_workflow[oxygen]
FAILED test/dose/test_default_bio_models.py::test_unmatched_tissue_with_physical_dose_only[carbon]
FAILED test/dose/test_default_bio_models.py::test_unmatched_tissue_with_physical_dose_only[oxygen]
FAILED test/dose/test_default_bio_models.py::test_oxygen_lem_dose_calculation[0.1]
FAILED test/dose/test_default_bio_models.py::test_oxygen_lem_dose_calculation[0.5]
FAILED test/dose/test_default_bio_models.py::test_oxygen_mkm_dose_calculation
FAILED test/dose/test_default_bio_models.py::test_oxygen_mkm_rejects_unset_alpha_0[auto]
FAILED test/dose/test_engine_reuse.py::test_reused_engine_repeats_forward_dose
FAILED test/dose/test_engine_reuse.py::test_reused_engine_uses_the_structures_of_each_call
FAILED test/dose/test_engine_reuse.py::test_forward_dose_leaves_the_stf_weights_alone
FAILED test/dose/test_engine_reuse.py::test_keep_rad_depth_cubes - AttributeE...
FAILED test/dose/test_engine_reuse.py::test_reused_engine_resolves_the_bio_model_per_calculation
FAILED test/dose/test_engine_reuse.py::test_reused_engine_resolves_the_lateral_model_per_calculation
FAILED test/dose/test_hongpb.py::test_protons_cd_forward - AttributeError: 'f...
FAILED test/dose/test_hongpb.py::test_protons_cd_forward_bio_model_none - Att...
FAILED test/dose/test_hongpb.py::test_protons_cd_forward_bio_model_WED - Attr...
FAILED test/dose/test_hongpb.py::test_protons_cd_forward_bio_model_MCN - Attr...
FAILED test/dose/test_hongpb.py::test_protons_cd_forward_bio_model_CAR - Attr...
FAILED test/dose/test_hongpb.py::test_protons_cd_forward_bio_model_LSM - Attr...
FAILED test/dose/test_hongpb.py::test_protons_cd_forward_bio_model_const - At...
FAILED test/dose/test_hongpb.py::test_helium_cd_forward - AttributeError: 'fl...
FAILED test/dose/test_hongpb.py::test_helium_cd_forward_bio_model_none - Attr...
FAILED test/dose/test_hongpb.py::test_helium_cd_forward_bio_model_LSM - Attri...
FAILED test/dose/test_hongpb.py::test_carbon_cd_forward - AttributeError: 'fl...
FAILED test/dose/test_hongpb.py::test_carbon_cd_forward_bio_model_none - Attr...
FAILED test/dose/test_hongpb.py::test_oxygen_cd_forward - AttributeError: 'fl...
FAILED test/dose/test_hongpb.py::test_protons_cd_influence - AttributeError: ...
FAILED test/dose/test_hongpb.py::test_protons_cd_influence_bio_model_none - A...
FAILED test/dose/test_hongpb.py::test_protons_cd_influence_bio_model_WED - At...
FAILED test/dose/test_hongpb.py::test_protons_cd_influence_bio_model_MCN - At...
FAILED test/dose/test_hongpb.py::test_protons_cd_influence_bio_model_CAR - At...
FAILED test/dose/test_hongpb.py::test_protons_cd_influence_bio_model_LSM - At...
FAILED test/dose/test_hongpb.py::test_helium_cd_influence - AttributeError: '...
FAILED test/dose/test_hongpb.py::test_helium_cd_influence_bio_model_none - At...
FAILED test/dose/test_hongpb.py::test_helium_cd_influence_bio_model_LSM - Att...
FAILED test/dose/test_hongpb.py::test_carbon_cd_influence_bio_model_none - At...
FAILED test/dose/test_hongpb.py::test_oxygen_cd_influence - AttributeError: '...
FAILED test/dose/test_hongpb.py::test_carbon_cd_influence_bio_model_unavailable_keeps_physical_dose
FAILED test/dose/test_hongpb.py::test_carbon_multi_gaussian_reduces_to_single
FAILED test/dose/test_hongpb.py::test_protons_cd_forward_uses_loaded_plan_machine
FAILED test/dose/test_hongpb_bio.py::test_protons_WED_alpha_beta_matrices - A...
FAILED test/dose/test_hongpb_bio.py::test_protons_MCN_alpha_beta_matrices - A...
FAILED test/dose/test_hongpb_bio.py::test_helium_HEL_alpha_beta_matrices - At...
FAILED test/dose/test_hongpb_bio.py::test_protons_constant_rbe - AttributeErr...
FAILED test/dose/test_hongpb_bio.py::test_protons_constant_rbe_parameter_from_plan
FAILED test/dose/test_hongpb_bio.py::test_calc_bio_dose_off_keeps_let_for_on_the_fly_models
FAILED test/dose/test_hongpb_bio.py::test_calc_let_off_still_feeds_let_based_model
FAILED test/dose/test_hongpb_bio.py::test_protons_bio_model_none_has_no_bio_matrices
FAILED test/dose/test_hongpb_bio.py::test_carbon_kernel_based_lq_alpha_beta_matrices
FAILED test/dose/test_hongpb_bio.py::test_carbon_tabulated_alpha_beta_matrices
FAILED test/dose/test_hongpb_bio.py::test_bio_tissue_arrays_use_the_selected_device
FAILED test/dose/test_hongpb_bio.py::test_out_of_domain_reference_parameters_without_bio_request_keep_physical_dose
FAILED test/dose/test_hongpb_bio.py::test_bio_influence_matrices_are_finite
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-integral-single]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-integral-double]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-integral-multi]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-integral-multi_1d]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-integral-singleXY]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-relative-single]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-relative-double]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-relative-multi]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-relative-multi_1d]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-1-relative-singleXY]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-single]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-double]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-multi]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-multi_1d]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-integral-singleXY]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-single]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-double]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-multi]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-multi_1d]
FAILED test/dose/test_particle_cutoff.py::test_cutoff_matches_scalar_reference[array_api_strict-100000-relative-singleXY]
FAILED test/dose/test_particle_cutoff.py::test_quadrature_respects_explicit_and_inferred_device
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-False-empty]
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-False-none]
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-False-all]
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-False-mixed]
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-True-empty]
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-True-none]
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-True-all]
FAILED test/dose/test_particle_indexing.py::test_particle_support_selection[strict2023-table-True-mixed]
FAILED test/dose/test_particle_interpolators.py::test_translated_depths_and_cache[float32-array_api_strict]
FAILED test/dose/test_particle_interpolators.py::test_translated_depths_and_cache[float64-array_api_strict]
FAILED test/dose/test_particle_interpolators.py::test_reused_engine_rebuilds_prepared_state[False]
FAILED test/dose/test_particle_interpolators.py::test_reused_engine_rebuilds_prepared_state[True]
FAILED test/dose/test_particle_lateral_cutoff.py::test_particle_beams_use_their_own_lateral_cutoffs
FAILED test/dose/test_ray_geometry_preparation.py::test_ssd_metadata_transferred_in_batches[array_api_strict-1]
FAILED test/dose/test_ray_geometry_preparation.py::test_ssd_metadata_transferred_in_batches[array_api_strict-7]
FAILED test/dose/test_scenarios_pencilbeam.py::test_nominal_set_has_one_flat_slot[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_nominal_set_has_one_flat_slot[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_scenarios_are_recorded_on_the_dij[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_scenarios_are_recorded_on_the_dij[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_setup_and_range_scenarios[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_setup_and_range_scenarios[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_proton_range_shift_pulls_the_dose_upstream
FAILED test/dose/test_scenarios_pencilbeam.py::test_shifts_do_not_accumulate[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_shifts_do_not_accumulate[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_ct_phase_with_moved_outline_matches_standalone[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_ct_phase_with_moved_outline_matches_standalone[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_ct_phase_selection_bio_single_and_reordered
FAILED test/dose/test_scenarios_pencilbeam.py::test_ct_phase_selection[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_ct_phase_selection[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_two_phase_nominal_model_validates
FAILED test/dose/test_scenarios_pencilbeam.py::test_derived_scenarios_are_not_computed[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_derived_scenarios_are_not_computed[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_explicit_model_is_accepted
FAILED test/dose/test_scenarios_pencilbeam.py::test_public_entry_point_scenario_override
FAILED test/dose/test_scenarios_pencilbeam.py::test_forward_dose_returns_every_scenario
FAILED test/dose/test_scenarios_pencilbeam.py::test_forward_dose_single_scenario_has_no_scenario_lists
FAILED test/dose/test_scenarios_pencilbeam.py::test_shift_scenarios_in_biological_results
FAILED test/dose/test_scenarios_pencilbeam.py::test_shift_scenarios_in_biological_quantities
FAILED test/dose/test_scenarios_pencilbeam.py::test_progress_counts_the_computed_scenarios[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_progress_counts_the_computed_scenarios[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_computed_scenarios_share_one_index_array[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_computed_scenarios_share_one_index_array[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_negative_range_shift_and_a_set_without_range_dimension
FAILED test/dose/test_scenarios_pencilbeam.py::test_rays_missing_the_patient_take_the_ssd_of_their_neighbors
FAILED test/dose/test_scenarios_pencilbeam.py::test_public_api_accepts_a_3d_structure_set_on_a_4d_ct
FAILED test/dose/test_scenarios_pencilbeam.py::test_4d_structure_set_gives_the_same_influence_matrices[protons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_4d_structure_set_gives_the_same_influence_matrices[photons]
FAILED test/dose/test_scenarios_pencilbeam.py::test_optimization_runs_on_a_4d_ct
FAILED test/dose/test_scenarios_pencilbeam.py::test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[none]
FAILED test/dose/test_scenarios_pencilbeam.py::test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[STOCH]
FAILED test/dose/test_scenarios_pencilbeam.py::test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[OWC]
FAILED test/dose/test_scenarios_pencilbeam.py::test_scenarios_are_evaluated_on_the_contour_of_their_ct_phase[COWC]
FAILED test/dose/test_scenarios_pencilbeam.py::test_none_robustness_sums_each_phase_on_its_own_contour
FAILED test/dose/test_scenarios_pencilbeam.py::test_voxel_worst_case_uses_the_contour_of_its_selected_phase
FAILED test/dose/test_scenarios_pencilbeam.py::test_image_reference_refused_on_moving_structures
FAILED test/dose/test_scenarios_pencilbeam.py::test_moving_structures_on_a_coarser_dose_grid
FAILED test/dose/test_svdpb.py::test_photons - AttributeError: 'float' object...
FAILED test/dose/test_svdpb.py::test_photons_field_based - AttributeError: 'f...
FAILED test/dose/test_svdpb.py::test_bixel_based_lateral_cutoff_includes_half_diagonal
FAILED test/dose/test_svdpb_bio.py::test_photons_constant_rbe_is_stored_and_applied
FAILED test/dose/test_svdpb_bio.py::test_photons_default_model_is_recorded - ...
FAILED test/dose/test_svdpb_bio.py::test_photon_engine_drops_a_model_it_cannot_compute
FAILED test/dose/test_svdpb_interpolators.py::test_kernel_interpolators_reuse_and_fluence_isolation[array_api_strict-float32]
FAILED test/dose/test_svdpb_interpolators.py::test_kernel_interpolators_reuse_and_fluence_isolation[array_api_strict-float64]
FAILED test/io/test_io_dicom.py::test_import_ct_matches_reference - Assertion...
FAILED test/io/test_io_dicom.py::test_import_cst_matches_reference - Assertio...
FAILED test/io/test_io_dicom.py::test_load_data_from_dicom_folder - assert False
FAILED test/io/test_io_dicom.py::test_dicom_roundtrip_ct - AssertionError: as...
FAILED test/io/test_io_dicom.py::test_dicom_roundtrip_dose - assert False
FAILED test/io/test_io_dicom.py::test_dicom_roundtrip_structures - AssertionE...
FAILED test/optimization/robustness/test_robustness_contract.py::test_expected_value_operator
FAILED test/optimization/robustness/test_robustness_contract.py::test_gradient_follows_the_dtype_of_the_values[expected_value]
FAILED test/optimization/robustness/test_robustness_operators.py::test_expected_quantity
FAILED test/optimization/robustness/test_robustness_operators.py::test_expected_quantity_is_a_mean_for_any_weight_sum[weights0]
FAILED test/optimization/robustness/test_robustness_operators.py::test_expected_quantity_is_a_mean_for_any_weight_sum[weights1]
FAILED test/optimization/robustness/test_robustness_operators.py::test_operators_on_phase_contours_match_equal_contours[expected_value]
FAILED test/optimization/robustness/test_variance_objective.py::test_variance_closed_form_uniform_default
FAILED test/optimization/robustness/test_variance_objective.py::test_variance_weighted_value_and_gradient
FAILED test/optimization/robustness/test_variance_objective.py::test_variance_through_scenario_stack
FAILED test/optimization/test_fluence_optiprob.py::test_solver_dict_configuration_survives
FAILED test/optimization/test_fluence_optiprob.py::test_top_level_overrides_solver_dict
FAILED test/optimization/test_fluence_optiprob.py::test_unset_properties_leave_solver_defaults
FAILED test/optimization/test_fluence_optiprob.py::test_constant_rbe_evaluates_only_the_quantities_it_uses
FAILED test/optimization/test_fluence_optiprob.py::test_objective_jacobian_has_one_row_per_objective[constant]
FAILED test/optimization/test_fluence_optiprob.py::test_objective_jacobian_has_one_row_per_objective[effect]
FAILED test/optimization/test_fluence_optiprob.py::test_multi_scenario_gradient_backprojects_each_scenario_separately[None]
FAILED test/optimization/test_ipax_solver.py::test_backend_arrays_and_vector_bounds[True-strict]
FAILED test/optimization/test_obj_history.py::test_problem_objective_fills_history[scipy]
FAILED test/optimization/test_obj_history.py::test_history_stays_empty_when_disabled[scipy]
FAILED test/optimization/test_objectives.py::test_SquaredMimicking_cache - In...
FAILED test/optimization/test_objectives.py::test_MinDVH_compute_objective - ...
FAILED test/optimization/test_objectives.py::test_MinDVH_compute_gradient - A...
FAILED test/optimization/test_objectives.py::test_MaxDVH_compute_objective - ...
FAILED test/optimization/test_objectives.py::test_MaxDVH_compute_gradient - A...
FAILED test/optimization/test_robust_fluence_optiprob.py::test_expected_value_is_the_weighted_sum_of_scenario_values
FAILED test/optimization/test_robust_fluence_optiprob.py::test_mixed_gradient_matches_finite_differences
FAILED test/optimization/test_robust_fluence_optiprob.py::test_jacobian_has_one_row_per_term
FAILED test/optimization/test_robust_fluence_optiprob.py::test_shipped_operators_match_finite_differences[logsumexp]
FAILED test/optimization/test_robust_fluence_optiprob.py::test_shipped_operators_match_finite_differences[pnorm]
FAILED test/optimization/test_robust_fluence_optiprob.py::test_robust_fluence_optimization_end_to_end
FAILED test/optimization/test_robust_fluence_optiprob.py::test_reinitializing_a_problem_with_other_scenarios_resets_the_gradient_caches
FAILED test/optimization/test_scipy_solver.py::test_simple_problem_scipy - At...
FAILED test/optimization/test_scipy_solver.py::test_scipy_emits_status_through_callback
FAILED test/optimization/test_scipy_solver.py::test_allow_keyboard_cancel_is_honoured_after_construction
FAILED test/optimization/test_scipy_solver.py::test_cancel_key_settings_are_honoured_after_construction
FAILED test/optimization/test_scipy_solver.py::test_scipy_status_callback_can_stop
FAILED test/quantities/test_multi_scenario_quantities.py::test_rbe_x_dose_uses_voxel_param_column
FAILED test/quantities/test_multi_scenario_quantities.py::test_alias_scenarios_share_dose_but_not_voxel_params
FAILED test/quantities/test_multi_scenario_quantities.py::test_derived_scenario_is_linear_combination[RBExDose]
FAILED test/quantities/test_multi_scenario_quantities.py::test_extend_scenarios_appends_containers_and_columns
FAILED test/quantities/test_multi_scenario_quantities.py::test_result_arrays_preserve_array_api_backend
FAILED test/quantities/test_rbe_x_dose_quantity.py::test_RBExDose_effect_dense
FAILED test/quantities/test_rbe_x_dose_quantity.py::test_RBExDose_effect_sparse
FAILED test/quantities/test_rbe_x_dose_quantity.py::test_lq_inverse_dose_supports_linear_limit_and_immutable_arrays
FAILED test/quantities/test_rbe_x_dose_quantity.py::test_lq_inverse_dose_is_accurate_in_single_precision_at_low_effect
FAILED test/quantities/test_rbe_x_dose_quantity.py::test_lq_inverse_dose_without_alpha_x_solves_the_quadratic_term
FAILED test/quantities/test_rbe_x_dose_quantity.py::test_rbe_x_dose_effect_evaluates_in_the_matrix_precision
FAILED test/quantities/test_rbe_x_dose_quantity.py::test_RBExDose_effect_gradient_matches_finite_differences
FAILED test/stf/test_stf.py::test_stfgen_to_stf - AttributeError: 'float' obj...
FAILED test/stf/test_stf.py::test_from_and_to_matrad - pydantic_core._pydanti...
FAILED test/stf/test_stf_gen.py::test_generate_with_round_tripped_plan_machine[protons-loaded]
FAILED test/stf/test_stf_gen.py::test_generate_with_round_tripped_plan_machine[protons-default-mode]
FAILED test/stf/test_stf_gen.py::test_impt - AttributeError: 'float' object h...
FAILED test/stf/test_stf_gen.py::test_impt_tg119 - AttributeError: 'float' ob...
FAILED test/stf/test_stf_gen.py::test_impt_longitudinal_spot_spacing_thins_energy_layers
FAILED test/stf/test_stf_gen.py::test_impt_zero_spot_spacing_keeps_all_layers
FAILED test/test_raytracersiddon.py::test_raytracer_trace_single_ray - Attrib...
FAILED test/test_raytracersiddon.py::test_raytracer_trace_multiple_rays - Att...
FAILED test/test_raytracersiddon.py::test_raytracer_trace_multiple_cubes - At...
FAILED test/test_raytracersiddon.py::test_raytracer_ray_does_not_hit - Attrib...
FAILED test/test_raytracersiddon.py::test_readonly_sitk_buffer_is_copied_for_backend[array_api_strict]
FAILED test/test_raytracersiddon.py::test_trace_depth_arrays_stay_on_backend[array_api_strict]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-False-empty]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-False-none]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-False-all]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-False-mixed]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-True-empty]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-True-none]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-True-all]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-full-True-mixed]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-False-empty]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-False-none]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-False-all]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-False-mixed]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-True-empty]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-True-none]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-True-all]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-constant-True-mixed]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-False-empty]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-False-none]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-False-all]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-False-mixed]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-True-empty]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-True-none]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-True-all]
ERROR test/dose/test_particle_indexing.py::test_particle_support_selection[strict2025-table-True-mixed]
ERROR test/optimization/test_opt_info.py::test_opt_info_is_populated[scipy]
ERROR test/optimization/test_opt_info.py::test_result_info_is_a_dict[scipy]
ERROR test/optimization/test_opt_info.py::test_num_iter_comes_from_the_solver[scipy]
ERROR test/optimization/test_opt_info.py::test_obj_history_records_every_evaluation[scipy]
= 365 failed, 2380 passed, 645 skipped, 11561 warnings, 28 errors in 123.17s (0:02:03) =
error: Bad exit status from /var/tmp/rpm-tmp.dDG380 (%check)
    Bad exit status from /var/tmp/rpm-tmp.dDG380 (%check)

RPM build errors:
Finish: rpmbuild python-pyradplan-0.8.0-1.fc43.src.rpm
Finish: build phase for python-pyradplan-0.8.0-1.fc43.src.rpm
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-43-x86_64-1794040424.263307/root/var/log/dnf5.log
INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz
/bin/tar: Removing leading `/' from member names
ERROR: Exception(/var/lib/copr-rpmbuild/results/python-pyradplan-0.8.0-1.fc43.src.rpm) Config(fedora-43-x86_64) 2 minutes 38 seconds
INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results
INFO: Cleaning up build root ('cleanup_on_failure=True')
Start: clean chroot
INFO: unmounting tmpfs.
Finish: clean chroot
ERROR: Command failed: 
 # /usr/bin/systemd-nspawn -q -M 8ceb47addbd14b01871c75db1b4f69a2 -D /var/lib/mock/fedora-43-x86_64-1794040424.263307/root -a -u mockbuild --capability=cap_ipc_lock --capability=cap_ipc_lock --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/fuse --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --resolv-conf=off --resolv-conf=off --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin '--setenv=PROMPT_COMMAND=printf "\033]0;<mock-chroot>\007"' '--setenv=PS1=<mock-chroot> \s-\v\$ ' --setenv=LANG=C.UTF-8 bash --login -c '/usr/bin/rpmbuild -ba --noprep --noclean --target x86_64 /builddir/build/originals/python-pyradplan.spec'

Copr build error: Build failed