Warning: Permanently added '3.80.172.70' (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-44-aarch64 --chroot fedora-44-aarch64


Version: 1.11
PID: 11817
Logging PID: 11819
Task:
{'allow_user_ssh': False,
 'appstream': False,
 'background': False,
 'build_id': 11283289,
 'buildroot_pkgs': [],
 'chroot': 'fedora-44-aarch64',
 '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-44-aarch64/',
            '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-44-aarch64',
 '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-wcmlgyjl/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-wcmlgyjl/python-pyradplan', '--depth', '500', '--no-single-branch', '--recursive']
cwd: .
rc: 0
stdout: 
stderr: Cloning into '/var/lib/copr-rpmbuild/workspace/workdir-wcmlgyjl/python-pyradplan'...

Running: git checkout 8d50c187fa9c88e50d7f946c5f309e0fa9526e08 --

cmd: ['git', 'checkout', '8d50c187fa9c88e50d7f946c5f309e0fa9526e08', '--']
cwd: /var/lib/copr-rpmbuild/workspace/workdir-wcmlgyjl/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-wcmlgyjl/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      0 442.7M      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-wcmlgyjl/python-pyradplan/python-pyradplan.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-wcmlgyjl/python-pyradplan --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1794040424.031981 -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-wcmlgyjl/python-pyradplan/python-pyradplan.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-wcmlgyjl/python-pyradplan --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1794040424.031981 -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-wcmlgyjl/python-pyradplan/python-pyradplan.spec)  Config(fedora-44-aarch64)
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-44-aarch64-bootstrap-1794040424.031981/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:44
INFO: Pulling image: registry.fedoraproject.org/fedora:44
INFO: Tagging container image as mock-bootstrap-605549de-5e63-4865-93d6-b704c5b97bde
INFO: Checking that 7ae77108e07e055ffe24cb699c4ae0b4449f550ac645d185debf3a67c621ab39 image matches host's architecture
INFO: Copy content of container 7ae77108e07e055ffe24cb699c4ae0b4449f550ac645d185debf3a67c621ab39 to /var/lib/mock/fedora-44-aarch64-bootstrap-1794040424.031981/root
INFO: mounting 7ae77108e07e055ffe24cb699c4ae0b4449f550ac645d185debf3a67c621ab39 with podman image mount
INFO: image 7ae77108e07e055ffe24cb699c4ae0b4449f550ac645d185debf3a67c621ab39 as /var/lib/containers/storage/overlay/984fd31702ef95a47582f03380de3842e6594ff420de1593ea8da3ed483f1f53/merged
INFO: umounting image 7ae77108e07e055ffe24cb699c4ae0b4449f550ac645d185debf3a67c621ab39 (/var/lib/containers/storage/overlay/984fd31702ef95a47582f03380de3842e6594ff420de1593ea8da3ed483f1f53/merged) with podman image umount
INFO: Removing image mock-bootstrap-605549de-5e63-4865-93d6-b704c5b97bde
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-44-aarch64-1794040424.031981/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.fc44.aarch64
  rpm-sequoia-1.10.3-1.fc44.aarch64
  dnf5-5.4.6.0-1.fc44.aarch64
  dnf5-plugins-5.4.6.0-1.fc44.aarch64
Start: installing minimal buildroot with dnf5
Updating and loading repositories:
 updates                                100% |  13.4 MiB/s |  12.5 MiB |  00m01s
 Copr repository                        100% | 910.4 KiB/s | 809.3 KiB |  00m01s
 fedora                                 100% |  27.4 MiB/s |  36.3 MiB |  00m01s
Repositories loaded.
Package                            Arch    Version                      Repository      Size
Installing group/module packages:
 bash                              aarch64 0:5.3.9-3.fc44               fedora       8.4 MiB
 bzip2                             aarch64 0:1.0.8-23.fc44              fedora     171.1 KiB
 coreutils                         aarch64 0:9.10-5.fc44                updates      8.2 MiB
 cpio                              aarch64 0:2.15-9.fc44                fedora       1.1 MiB
 diffutils                         aarch64 0:3.12-5.fc44                fedora       1.6 MiB
 fedora-release-common             noarch  0:44-18                      updates     20.6 KiB
 findutils                         aarch64 1:4.10.0-7.fc44              fedora       1.9 MiB
 gawk                              aarch64 0:5.3.2-3.fc44               fedora       2.6 MiB
 glibc-minimal-langpack            aarch64 0:2.43-9.fc44                updates      0.0   B
 grep                              aarch64 0:3.12-3.fc44                fedora       1.0 MiB
 gzip                              aarch64 0:1.14-2.fc44                fedora     437.5 KiB
 info                              aarch64 0:7.2-9.fc44                 fedora     421.5 KiB
 patch                             aarch64 0:2.8-4.fc44                 fedora     262.4 KiB
 redhat-rpm-config                 noarch  0:344-2.fc44                 updates    183.7 KiB
 rpm-build                         aarch64 0:6.0.2-1.fc44               updates    598.7 KiB
 sed                               aarch64 0:4.9-7.fc44                 fedora     873.1 KiB
 shadow-utils                      aarch64 2:4.19.0-7.fc44              updates      4.4 MiB
 tar                               aarch64 2:1.35-9.fc44                updates      3.0 MiB
 unzip                             aarch64 0:6.0-69.fc44                fedora     533.8 KiB
 util-linux                        aarch64 0:2.41.6-1.fc44              updates      6.8 MiB
 which                             aarch64 0:2.25-1.fc44                updates    123.6 KiB
 xz                                aarch64 1:5.8.2-2.fc44               fedora       1.4 MiB
Installing dependencies:
 R-srpm-macros                     noarch  0:1.3.7-2.fc44               updates      3.5 KiB
 add-determinism                   aarch64 0:0.7.3-2.fc44               updates      2.0 MiB
 alternatives                      aarch64 0:1.33-5.fc44                fedora      90.1 KiB
 ansible-srpm-macros               noarch  0:1-20.1.fc44                fedora      35.7 KiB
 audit-libs                        aarch64 0:4.2.1-1.fc44               updates    486.7 KiB
 binutils                          aarch64 0:2.46.1-1.fc44              updates     31.5 MiB
 build-reproducibility-srpm-macros noarch  0:0.7.3-2.fc44               updates      1.2 KiB
 bzip2-libs                        aarch64 0:1.0.8-23.fc44              fedora      72.5 KiB
 ca-certificates                   noarch  0:2026.2.92_v9.0.319-2.fc44  updates      2.7 MiB
 cmake-srpm-macros                 noarch  0:4.3.0-1.fc44               fedora     524.0   B
 coreutils-common                  aarch64 0:9.10-5.fc44                updates     10.7 MiB
 crypto-policies                   noarch  0:20251128-3.git19878fe.fc44 fedora     132.6 KiB
 curl                              aarch64 0:8.18.0-14.fc44             updates    464.2 KiB
 cyrus-sasl-lib                    aarch64 0:2.1.28-35.fc44             fedora       2.4 MiB
 debugedit                         aarch64 0:5.3-2.fc44                 updates    320.9 KiB
 dwz                               aarch64 0:0.16-3.fc44                fedora     322.4 KiB
 ed                                aarch64 0:1.22.5-2.fc44              fedora     157.8 KiB
 efi-srpm-macros                   noarch  0:6-6.fc44                   fedora      40.2 KiB
 elfutils                          aarch64 0:0.197-1.fc44               updates      3.6 MiB
 elfutils-debuginfod-client        aarch64 0:0.197-1.fc44               updates    144.0 KiB
 elfutils-default-yama-scope       noarch  0:0.197-1.fc44               updates      1.8 KiB
 elfutils-libelf                   aarch64 0:0.197-1.fc44               updates      1.6 MiB
 elfutils-libs                     aarch64 0:0.197-1.fc44               updates    746.7 KiB
 erlang-srpm-macros                noarch  0:0.4.0-1.fc44               updates      2.0 KiB
 fedora-gpg-keys                   noarch  0:44-2                       updates    257.6 KiB
 fedora-release                    noarch  0:44-18                      updates      0.0   B
 fedora-release-identity-basic     noarch  0:44-18                      updates    629.0   B
 fedora-repos                      noarch  0:44-2                       updates      4.9 KiB
 file                              aarch64 0:5.48-2.fc44                updates    141.9 KiB
 file-libs                         aarch64 0:5.48-2.fc44                updates     12.4 MiB
 filesystem                        aarch64 0:3.18-52.fc44               fedora     112.0   B
 filesystem-srpm-macros            noarch  0:3.18-52.fc44               fedora      38.2 KiB
 fonts-srpm-macros                 noarch  1:5.0.0-2.fc44               fedora      55.8 KiB
 forge-srpm-macros                 noarch  0:0.4.0-4.fc44               fedora      38.9 KiB
 fpc-srpm-macros                   noarch  0:1.3-16.fc44                fedora     144.0   B
 gap-srpm-macros                   noarch  0:2-2.fc44                   fedora       2.1 KiB
 gdb-minimal                       aarch64 0:18.1-1.fc44                updates     13.8 MiB
 gdbm-libs                         aarch64 1:1.23-11.fc44               fedora     233.7 KiB
 ghc-srpm-macros                   noarch  0:1.10-1.fc44                fedora     792.0   B
 glibc                             aarch64 0:2.43-9.fc44                updates      6.4 MiB
 glibc-common                      aarch64 0:2.43-9.fc44                updates      1.3 MiB
 glibc-gconv-extra                 aarch64 0:2.43-9.fc44                updates     18.5 MiB
 gmp                               aarch64 1:6.3.0-5.fc44               fedora     657.9 KiB
 gnat-srpm-macros                  noarch  0:7-2.fc44                   fedora       1.0 KiB
 gnome-srpm-macros                 noarch  0:1.0-1.fc44                 updates      3.5 KiB
 gnulib-l10n                       noarch  0:20241231-2.fc44            fedora     655.0 KiB
 gnupg2                            aarch64 0:2.4.9-16.fc44              updates      6.4 MiB
 gnupg2-dirmngr                    aarch64 0:2.4.9-16.fc44              updates    646.1 KiB
 gnupg2-gpg-agent                  aarch64 0:2.4.9-16.fc44              updates    846.9 KiB
 gnupg2-gpgconf                    aarch64 0:2.4.9-16.fc44              updates    321.8 KiB
 gnupg2-keyboxd                    aarch64 0:2.4.9-16.fc44              updates    233.3 KiB
 gnupg2-verify                     aarch64 0:2.4.9-16.fc44              updates    364.3 KiB
 gnutls                            aarch64 0:3.8.13-1.fc44              updates      3.7 MiB
 go-srpm-macros                    noarch  0:3.8.0-2.fc44               fedora      61.9 KiB
 gpgverify                         noarch  0:2.2-4.fc44                 fedora       8.7 KiB
 ima-evm-utils-libs                aarch64 0:1.6.2-8.fc44               fedora      92.6 KiB
 jansson                           aarch64 0:2.14-4.fc44                fedora      93.0 KiB
 java-srpm-macros                  noarch  0:1-8.fc44                   fedora     870.0   B
 json-c                            aarch64 0:0.18-8.fc44                fedora     138.5 KiB
 kernel-srpm-macros                noarch  0:1.0-28.fc44                fedora       1.9 KiB
 keyutils-libs                     aarch64 0:1.6.3-7.fc44               fedora      98.2 KiB
 krb5-libs                         aarch64 0:1.22.2-4.fc44              updates      2.5 MiB
 libacl                            aarch64 0:2.4.0-1.fc44               updates     67.9 KiB
 libarchive                        aarch64 0:3.8.7-1.fc44               updates    975.5 KiB
 libassuan                         aarch64 0:2.5.7-5.fc44               fedora     215.8 KiB
 libattr                           aarch64 0:2.6.0-1.fc44               updates     68.4 KiB
 libblkid                          aarch64 0:2.41.6-1.fc44              updates    290.3 KiB
 libbrotli                         aarch64 0:1.2.1-1.fc44               updates    909.2 KiB
 libcap                            aarch64 0:2.78-1.fc44                updates    508.4 KiB
 libcap-ng                         aarch64 0:0.9.6-1.fc44               updates    160.9 KiB
 libcbor                           aarch64 0:0.13.0-2.fc44              fedora     139.5 KiB
 libcom_err                        aarch64 0:1.47.3-4.fc44              fedora     111.0 KiB
 libcurl                           aarch64 0:8.18.0-14.fc44             updates    980.9 KiB
 libeconf                          aarch64 0:0.7.9-3.fc44               fedora      80.8 KiB
 libevent                          aarch64 0:2.1.13-1.fc44              updates      1.1 MiB
 libfdisk                          aarch64 0:2.41.6-1.fc44              updates    418.8 KiB
 libffi                            aarch64 0:3.5.2-2.fc44               fedora     155.7 KiB
 libfido2                          aarch64 0:1.16.0-5.fc44              fedora     278.6 KiB
 libfsverity                       aarch64 0:1.6-4.fc44                 fedora      68.4 KiB
 libgcc                            aarch64 0:16.2.1-3.fc44              updates    222.3 KiB
 libgcrypt                         aarch64 0:1.12.2-1.fc44              updates      1.2 MiB
 libgomp                           aarch64 0:16.2.1-3.fc44              updates    592.9 KiB
 libgpg-error                      aarch64 0:1.58-2.fc44                fedora     989.6 KiB
 libidn2                           aarch64 0:2.3.8-3.fc44               fedora     560.4 KiB
 libksba                           aarch64 0:1.6.7-5.fc44               fedora     398.3 KiB
 liblastlog2                       aarch64 0:2.41.6-1.fc44              updates    137.8 KiB
 libmount                          aarch64 0:2.41.6-1.fc44              updates    420.2 KiB
 libnghttp2                        aarch64 0:1.68.0-5.fc44              updates    197.9 KiB
 libnghttp3                        aarch64 0:1.18.0-1.fc44              updates    206.8 KiB
 libpfm                            aarch64 0:4.14.1-2.fc44              updates    513.2 KiB
 libpkgconf                        aarch64 0:2.5.1-1.fc44               fedora     134.1 KiB
 libpsl                            aarch64 0:0.21.5-7.fc44              fedora     132.3 KiB
 libselinux                        aarch64 0:3.11-2.fc44                updates    265.2 KiB
 libselinux-utils                  aarch64 0:3.11-2.fc44                updates      1.3 MiB
 libsemanage                       aarch64 0:3.11-1.fc44                updates    360.1 KiB
 libsepol                          aarch64 0:3.11-1.fc44                updates    809.9 KiB
 libsmartcols                      aarch64 0:2.41.6-1.fc44              updates    224.4 KiB
 libssh                            aarch64 0:0.12.2-1.fc44              updates    721.3 KiB
 libssh-config                     noarch  0:0.12.2-1.fc44              updates    277.0   B
 libstdc++                         aarch64 0:16.2.1-3.fc44              updates      3.0 MiB
 libtasn1                          aarch64 0:4.21.0-1.fc44              fedora     220.7 KiB
 libtool-ltdl                      aarch64 0:2.5.4-10.fc44              fedora      93.9 KiB
 libunistring                      aarch64 0:1.1-11.fc44                fedora       1.7 MiB
 libusb1                           aarch64 0:1.0.30-1.fc44              updates    179.7 KiB
 libuuid                           aarch64 0:2.41.6-1.fc44              updates     69.3 KiB
 libverto                          aarch64 0:0.3.2-12.fc44              fedora      69.3 KiB
 libxcrypt                         aarch64 0:4.5.2-3.fc44               fedora     273.2 KiB
 libxml2                           aarch64 0:2.12.10-6.fc44             fedora       1.9 MiB
 libzstd                           aarch64 0:1.5.7-5.fc44               fedora     860.2 KiB
 linkdupes                         aarch64 0:0.7.3-2.fc44               updates    713.7 KiB
 lua-libs                          aarch64 0:5.4.8-5.fc44               fedora     329.8 KiB
 lua-srpm-macros                   noarch  0:1-17.fc44                  fedora       1.3 KiB
 lz4-libs                          aarch64 0:1.10.0-4.fc44              fedora     197.3 KiB
 mpfr                              aarch64 0:4.2.2-3.fc44               fedora     755.4 KiB
 ncurses-base                      noarch  0:6.6-1.fc44                 fedora     329.7 KiB
 ncurses-libs                      aarch64 0:6.6-1.fc44                 fedora       1.2 MiB
 nettle                            aarch64 0:3.10.1-3.fc44              fedora     765.0 KiB
 ngtcp2                            aarch64 0:1.22.1-1.fc44              updates    341.1 KiB
 ngtcp2-crypto-ossl                aarch64 0:1.22.1-1.fc44              updates     67.5 KiB
 npth                              aarch64 0:1.8-4.fc44                 fedora      93.3 KiB
 ocaml-srpm-macros                 noarch  0:11-3.fc44                  fedora       1.9 KiB
 openblas-srpm-macros              noarch  0:2-21.fc44                  fedora     112.0   B
 openldap                          aarch64 0:2.6.16-1.fc44              updates    766.1 KiB
 openssl-libs                      aarch64 1:3.5.9-1.fc44               updates      7.5 MiB
 p11-kit                           aarch64 0:0.26.5-1.fc44              updates      2.8 MiB
 p11-kit-trust                     aarch64 0:0.26.5-1.fc44              updates    530.1 KiB
 package-notes-srpm-macros         noarch  0:0.20-1.fc44                updates      1.6 KiB
 pam-libs                          aarch64 0:1.7.2-2.fc44               updates    222.7 KiB
 pcre2                             aarch64 0:10.47-1.fc44.1             fedora     714.3 KiB
 pcre2-syntax                      noarch  0:10.47-1.fc44.1             fedora     281.9 KiB
 perl-srpm-macros                  noarch  0:1-61.fc44                  fedora     861.0   B
 pkgconf                           aarch64 0:2.5.1-1.fc44               fedora     116.8 KiB
 pkgconf-m4                        noarch  0:2.5.1-1.fc44               fedora      14.3 KiB
 pkgconf-pkg-config                aarch64 0:2.5.1-1.fc44               fedora     990.0   B
 policycoreutils                   aarch64 0:3.11-2.fc44                updates      1.5 MiB
 popt                              aarch64 0:1.19-10.fc44               fedora     144.6 KiB
 publicsuffix-list-dafsa           noarch  0:20260116-1.fc44            fedora      70.4 KiB
 pyproject-srpm-macros             noarch  0:1.24.0-1.fc44              updates      3.4 KiB
 python-srpm-macros                noarch  0:3.14-12.fc44               fedora      51.6 KiB
 qt5-srpm-macros                   noarch  0:5.15.18-2.fc44             fedora     500.0   B
 qt6-srpm-macros                   noarch  0:6.11.2-1.fc44              updates    472.0   B
 readline                          aarch64 0:8.3-4.fc44                 fedora     563.5 KiB
 rpm                               aarch64 0:6.0.2-1.fc44               updates      3.3 MiB
 rpm-build-libs                    aarch64 0:6.0.2-1.fc44               updates    264.1 KiB
 rpm-libs                          aarch64 0:6.0.2-1.fc44               updates    995.2 KiB
 rpm-plugin-selinux                aarch64 0:6.0.2-1.fc44               updates     67.9 KiB
 rpm-sequoia                       aarch64 0:1.10.3-1.fc44              updates      2.3 MiB
 rpm-sign-libs                     aarch64 0:6.0.2-1.fc44               updates     67.7 KiB
 rust-srpm-macros                  noarch  0:28.5-1.fc44                updates      5.5 KiB
 selinux-policy                    noarch  0:44.11-1.fc44               updates     32.1 KiB
 selinux-policy-targeted           noarch  0:44.11-1.fc44               updates     18.6 MiB
 setup                             noarch  0:2.15.0-28.fc44             fedora     724.9 KiB
 sqlite-libs                       aarch64 0:3.51.2-2.fc44              updates      1.6 MiB
 systemd-libs                      aarch64 0:259.10-1.fc44              updates      2.5 MiB
 systemd-standalone-sysusers       aarch64 0:259.10-1.fc44              updates    329.6 KiB
 tpm2-tss                          aarch64 0:4.1.5-1.fc44               updates      2.1 MiB
 tree-sitter-srpm-macros           noarch  0:0.4.2-2.fc44               fedora       8.3 KiB
 util-linux-core                   aarch64 0:2.41.6-1.fc44              updates      2.4 MiB
 xxhash-libs                       aarch64 0:0.8.4-2.fc44               updates     85.8 KiB
 xz-libs                           aarch64 1:5.8.2-2.fc44               fedora     265.4 KiB
 zig-srpm-macros                   noarch  0:1-8.fc44                   fedora       1.3 KiB
 zip                               aarch64 0:3.0-45.fc44                fedora     762.0 KiB
 zlib-ng-compat                    aarch64 0:2.3.3-3.fc44               fedora     133.4 KiB
 zstd                              aarch64 0:1.5.7-5.fc44               fedora     562.2 KiB
Installing groups:
 Buildsystem building group                                                                 

Transaction Summary:
 Installing:       186 packages

Total size of inbound packages is 67 MiB. Need to download 67 MiB.
After this operation, 252 MiB extra will be used (install 252 MiB, remove 0 B).
[  1/186] bzip2-0:1.0.8-23.fc44.aarch64 100% |   4.6 MiB/s |  51.9 KiB |  00m00s
[  2/186] cpio-0:2.15-9.fc44.aarch64    100% |  15.7 MiB/s | 289.7 KiB |  00m00s
[  3/186] diffutils-0:3.12-5.fc44.aarch 100% |  54.7 MiB/s | 391.9 KiB |  00m00s
[  4/186] bash-0:5.3.9-3.fc44.aarch64   100% |  83.7 MiB/s |   1.8 MiB |  00m00s
[  5/186] findutils-1:4.10.0-7.fc44.aar 100% |  89.9 MiB/s | 552.0 KiB |  00m00s
[  6/186] gawk-0:5.3.2-3.fc44.aarch64   100% | 159.6 MiB/s |   1.1 MiB |  00m00s
[  7/186] grep-0:3.12-3.fc44.aarch64    100% |  57.9 MiB/s | 296.3 KiB |  00m00s
[  8/186] gzip-0:1.14-2.fc44.aarch64    100% |  56.2 MiB/s | 172.8 KiB |  00m00s
[  9/186] info-0:7.2-9.fc44.aarch64     100% |  58.3 MiB/s | 179.0 KiB |  00m00s
[ 10/186] patch-0:2.8-4.fc44.aarch64    100% |  36.2 MiB/s | 111.1 KiB |  00m00s
[ 11/186] sed-0:4.9-7.fc44.aarch64      100% | 102.5 MiB/s | 314.8 KiB |  00m00s
[ 12/186] unzip-0:6.0-69.fc44.aarch64   100% |  65.4 MiB/s | 200.9 KiB |  00m00s
[ 13/186] xz-1:5.8.2-2.fc44.aarch64     100% | 148.1 MiB/s | 606.6 KiB |  00m00s
[ 14/186] fedora-release-common-0:44-18 100% |   4.6 MiB/s |  23.6 KiB |  00m00s
[ 15/186] coreutils-0:9.10-5.fc44.aarch 100% | 125.0 MiB/s |   1.1 MiB |  00m00s
[ 16/186] glibc-minimal-langpack-0:2.43 100% |  16.7 MiB/s |  85.4 KiB |  00m00s
[ 17/186] redhat-rpm-config-0:344-2.fc4 100% |  25.4 MiB/s |  78.1 KiB |  00m00s
[ 18/186] rpm-build-0:6.0.2-1.fc44.aarc 100% |  63.8 MiB/s | 130.6 KiB |  00m00s
[ 19/186] tar-2:1.35-9.fc44.aarch64     100% | 119.4 MiB/s | 855.9 KiB |  00m00s
[ 20/186] util-linux-0:2.41.6-1.fc44.aa 100% | 131.8 MiB/s |   1.2 MiB |  00m00s
[ 21/186] shadow-utils-2:4.19.0-7.fc44. 100% |  99.3 MiB/s |   1.3 MiB |  00m00s
[ 22/186] which-0:2.25-1.fc44.aarch64   100% |  10.1 MiB/s |  41.6 KiB |  00m00s
[ 23/186] ncurses-libs-0:6.6-1.fc44.aar 100% |  79.3 MiB/s | 324.8 KiB |  00m00s
[ 24/186] filesystem-0:3.18-52.fc44.aar 100% | 166.7 MiB/s |   1.3 MiB |  00m00s
[ 25/186] bzip2-libs-0:1.0.8-23.fc44.aa 100% |   6.7 MiB/s |  41.2 KiB |  00m00s
[ 26/186] gmp-1:6.3.0-5.fc44.aarch64    100% |  88.2 MiB/s | 271.0 KiB |  00m00s
[ 27/186] mpfr-0:4.2.2-3.fc44.aarch64   100% | 104.2 MiB/s | 320.0 KiB |  00m00s
[ 28/186] readline-0:8.3-4.fc44.aarch64 100% |  72.0 MiB/s | 221.1 KiB |  00m00s
[ 29/186] pcre2-0:10.47-1.fc44.1.aarch6 100% |  81.5 MiB/s | 250.2 KiB |  00m00s
[ 30/186] ed-0:1.22.5-2.fc44.aarch64    100% |  27.2 MiB/s |  83.5 KiB |  00m00s
[ 31/186] xz-libs-1:5.8.2-2.fc44.aarch6 100% |  36.3 MiB/s | 111.7 KiB |  00m00s
[ 32/186] glibc-0:2.43-9.fc44.aarch64   100% | 162.5 MiB/s |   2.0 MiB |  00m00s
[ 33/186] coreutils-common-0:9.10-5.fc4 100% | 133.2 MiB/s |   2.1 MiB |  00m00s
[ 34/186] glibc-common-0:2.43-9.fc44.aa 100% |  23.5 MiB/s | 360.3 KiB |  00m00s
[ 35/186] gnome-srpm-macros-0:1.0-1.fc4 100% |   2.1 MiB/s |   8.7 KiB |  00m00s
[ 36/186] ansible-srpm-macros-0:1-20.1. 100% |   4.9 MiB/s |  20.1 KiB |  00m00s
[ 37/186] cmake-srpm-macros-0:4.3.0-1.f 100% |   3.4 MiB/s |  10.4 KiB |  00m00s
[ 38/186] dwz-0:0.16-3.fc44.aarch64     100% |  32.1 MiB/s | 131.3 KiB |  00m00s
[ 39/186] efi-srpm-macros-0:6-6.fc44.no 100% |   7.3 MiB/s |  22.6 KiB |  00m00s
[ 40/186] fonts-srpm-macros-1:5.0.0-2.f 100% |   8.9 MiB/s |  27.4 KiB |  00m00s
[ 41/186] filesystem-srpm-macros-0:3.18 100% |   6.5 MiB/s |  26.6 KiB |  00m00s
[ 42/186] fpc-srpm-macros-0:1.3-16.fc44 100% |   1.9 MiB/s |   7.9 KiB |  00m00s
[ 43/186] gap-srpm-macros-0:2-2.fc44.no 100% |   2.2 MiB/s |   9.1 KiB |  00m00s
[ 44/186] forge-srpm-macros-0:0.4.0-4.f 100% |   3.3 MiB/s |  20.0 KiB |  00m00s
[ 45/186] ghc-srpm-macros-0:1.10-1.fc44 100% |   4.3 MiB/s |   8.8 KiB |  00m00s
[ 46/186] gnat-srpm-macros-0:7-2.fc44.n 100% |   4.3 MiB/s |   8.7 KiB |  00m00s
[ 47/186] go-srpm-macros-0:3.8.0-2.fc44 100% |  13.8 MiB/s |  28.2 KiB |  00m00s
[ 48/186] java-srpm-macros-0:1-8.fc44.n 100% |   4.0 MiB/s |   8.1 KiB |  00m00s
[ 49/186] kernel-srpm-macros-0:1.0-28.f 100% |   4.4 MiB/s |   9.0 KiB |  00m00s
[ 50/186] lua-srpm-macros-0:1-17.fc44.n 100% |   4.3 MiB/s |   8.9 KiB |  00m00s
[ 51/186] ocaml-srpm-macros-0:11-3.fc44 100% |   4.5 MiB/s |   9.3 KiB |  00m00s
[ 52/186] openblas-srpm-macros-0:2-21.f 100% |   3.8 MiB/s |   7.8 KiB |  00m00s
[ 53/186] perl-srpm-macros-0:1-61.fc44. 100% |   4.1 MiB/s |   8.4 KiB |  00m00s
[ 54/186] qt5-srpm-macros-0:5.15.18-2.f 100% |   8.5 MiB/s |   8.7 KiB |  00m00s
[ 55/186] python-srpm-macros-0:3.14-12. 100% |  11.7 MiB/s |  24.0 KiB |  00m00s
[ 56/186] tree-sitter-srpm-macros-0:0.4 100% |   6.6 MiB/s |  13.5 KiB |  00m00s
[ 57/186] zig-srpm-macros-0:1-8.fc44.no 100% |   8.6 MiB/s |   8.8 KiB |  00m00s
[ 58/186] rpm-0:6.0.2-1.fc44.aarch64    100% | 187.3 MiB/s | 575.3 KiB |  00m00s
[ 59/186] pkgconf-pkg-config-0:2.5.1-1. 100% |   4.6 MiB/s |   9.4 KiB |  00m00s
[ 60/186] zip-0:3.0-45.fc44.aarch64     100% |  51.9 MiB/s | 265.6 KiB |  00m00s
[ 61/186] zstd-0:1.5.7-5.fc44.aarch64   100% |  89.4 MiB/s | 183.1 KiB |  00m00s
[ 62/186] popt-0:1.19-10.fc44.aarch64   100% |  21.2 MiB/s |  65.2 KiB |  00m00s
[ 63/186] libeconf-0:0.7.9-3.fc44.aarch 100% |  17.1 MiB/s |  35.0 KiB |  00m00s
[ 64/186] libxcrypt-0:4.5.2-3.fc44.aarc 100% |  61.1 MiB/s | 125.1 KiB |  00m00s
[ 65/186] libblkid-0:2.41.6-1.fc44.aarc 100% |  39.9 MiB/s | 122.5 KiB |  00m00s
[ 66/186] setup-0:2.15.0-28.fc44.noarch 100% |  38.4 MiB/s | 157.2 KiB |  00m00s
[ 67/186] libfdisk-0:2.41.6-1.fc44.aarc 100% |  50.5 MiB/s | 155.2 KiB |  00m00s
[ 68/186] liblastlog2-0:2.41.6-1.fc44.a 100% |  11.9 MiB/s |  24.4 KiB |  00m00s
[ 69/186] libmount-0:2.41.6-1.fc44.aarc 100% |  52.2 MiB/s | 160.2 KiB |  00m00s
[ 70/186] libsmartcols-0:2.41.6-1.fc44. 100% |  27.1 MiB/s |  83.1 KiB |  00m00s
[ 71/186] libuuid-0:2.41.6-1.fc44.aarch 100% |  12.9 MiB/s |  26.5 KiB |  00m00s
[ 72/186] zlib-ng-compat-0:2.3.3-3.fc44 100% |  35.4 MiB/s |  72.4 KiB |  00m00s
[ 73/186] util-linux-core-0:2.41.6-1.fc 100% | 132.7 MiB/s | 543.7 KiB |  00m00s
[ 74/186] ncurses-base-0:6.6-1.fc44.noa 100% |  28.6 MiB/s |  88.0 KiB |  00m00s
[ 75/186] pcre2-syntax-0:10.47-1.fc44.1 100% |  40.2 MiB/s | 164.7 KiB |  00m00s
[ 76/186] gnulib-l10n-0:20241231-2.fc44 100% |  48.9 MiB/s | 150.3 KiB |  00m00s
[ 77/186] pkgconf-0:2.5.1-1.fc44.aarch6 100% |   9.4 MiB/s |  48.2 KiB |  00m00s
[ 78/186] glibc-gconv-extra-0:2.43-9.fc 100% | 156.4 MiB/s |   1.7 MiB |  00m00s
[ 79/186] pkgconf-m4-0:2.5.1-1.fc44.noa 100% |   1.7 MiB/s |  13.8 KiB |  00m00s
[ 80/186] libzstd-0:1.5.7-5.fc44.aarch6 100% |  80.4 MiB/s | 329.2 KiB |  00m00s
[ 81/186] rpm-libs-0:6.0.2-1.fc44.aarch 100% | 183.9 MiB/s | 376.6 KiB |  00m00s
[ 82/186] libpkgconf-0:2.5.1-1.fc44.aar 100% |   6.6 MiB/s |  40.7 KiB |  00m00s
[ 83/186] rpm-build-libs-0:6.0.2-1.fc44 100% |  37.5 MiB/s | 115.1 KiB |  00m00s
[ 84/186] lua-libs-0:5.4.8-5.fc44.aarch 100% |  21.1 MiB/s | 129.4 KiB |  00m00s
[ 85/186] rpm-sign-libs-0:6.0.2-1.fc44. 100% |  24.4 MiB/s |  25.0 KiB |  00m00s
[ 86/186] ima-evm-utils-libs-0:1.6.2-8. 100% |  14.4 MiB/s |  29.5 KiB |  00m00s
[ 87/186] libfsverity-0:1.6-4.fc44.aarc 100% |   9.2 MiB/s |  18.7 KiB |  00m00s
[ 88/186] libgcc-0:16.2.1-3.fc44.aarch6 100% |  59.2 MiB/s | 121.3 KiB |  00m00s
[ 89/186] libacl-0:2.4.0-1.fc44.aarch64 100% |  25.9 MiB/s |  26.5 KiB |  00m00s
[ 90/186] libselinux-0:3.11-2.fc44.aarc 100% |  48.7 MiB/s |  99.7 KiB |  00m00s
[ 91/186] libstdc++-0:16.2.1-3.fc44.aar 100% | 180.5 MiB/s | 924.0 KiB |  00m00s
[ 92/186] libattr-0:2.6.0-1.fc44.aarch6 100% |   8.6 MiB/s |  17.5 KiB |  00m00s
[ 93/186] libsepol-0:3.11-1.fc44.aarch6 100% |  63.7 MiB/s | 325.9 KiB |  00m00s
[ 94/186] crypto-policies-0:20251128-3. 100% |  32.0 MiB/s |  98.3 KiB |  00m00s
[ 95/186] tpm2-tss-0:4.1.5-1.fc44.aarch 100% |  78.8 MiB/s | 403.3 KiB |  00m00s
[ 96/186] json-c-0:0.18-8.fc44.aarch64  100% |  11.0 MiB/s |  44.9 KiB |  00m00s
[ 97/186] elfutils-libelf-0:0.197-1.fc4 100% |  64.1 MiB/s | 262.4 KiB |  00m00s
[ 98/186] openssl-libs-1:3.5.9-1.fc44.a 100% | 154.7 MiB/s |   2.5 MiB |  00m00s
[ 99/186] elfutils-libs-0:0.197-1.fc44. 100% |  32.5 MiB/s | 265.8 KiB |  00m00s
[100/186] elfutils-0:0.197-1.fc44.aarch 100% |  84.6 MiB/s | 606.2 KiB |  00m00s
[101/186] systemd-libs-0:259.10-1.fc44. 100% | 193.5 MiB/s | 792.4 KiB |  00m00s
[102/186] elfutils-debuginfod-client-0: 100% |   9.0 MiB/s |  45.9 KiB |  00m00s
[103/186] audit-libs-0:4.2.1-1.fc44.aar 100% |  46.1 MiB/s | 141.8 KiB |  00m00s
[104/186] libcap-ng-0:0.9.6-1.fc44.aarc 100% |  17.1 MiB/s |  35.1 KiB |  00m00s
[105/186] pam-libs-0:1.7.2-2.fc44.aarch 100% |  27.6 MiB/s |  56.5 KiB |  00m00s
[106/186] libusb1-0:1.0.30-1.fc44.aarch 100% |  38.6 MiB/s |  79.0 KiB |  00m00s
[107/186] libsemanage-0:3.11-1.fc44.aar 100% |  58.3 MiB/s | 119.3 KiB |  00m00s
[108/186] gpgverify-0:2.2-4.fc44.noarch 100% |   5.5 MiB/s |  11.2 KiB |  00m00s
[109/186] gnupg2-dirmngr-0:2.4.9-16.fc4 100% |  85.6 MiB/s | 262.9 KiB |  00m00s
[110/186] gnupg2-gpg-agent-0:2.4.9-16.f 100% |  64.9 MiB/s | 265.8 KiB |  00m00s
[111/186] gnupg2-0:2.4.9-16.fc44.aarch6 100% | 179.0 MiB/s |   1.6 MiB |  00m00s
[112/186] gnupg2-gpgconf-0:2.4.9-16.fc4 100% |  27.7 MiB/s | 113.5 KiB |  00m00s
[113/186] gnupg2-keyboxd-0:2.4.9-16.fc4 100% |  29.5 MiB/s |  90.6 KiB |  00m00s
[114/186] gnupg2-verify-0:2.4.9-16.fc44 100% |  79.5 MiB/s | 162.8 KiB |  00m00s
[115/186] libassuan-0:2.5.7-5.fc44.aarc 100% |  32.6 MiB/s |  66.9 KiB |  00m00s
[116/186] libgpg-error-0:1.58-2.fc44.aa 100% |  60.6 MiB/s | 248.3 KiB |  00m00s
[117/186] npth-0:1.8-4.fc44.aarch64     100% |  12.2 MiB/s |  25.0 KiB |  00m00s
[118/186] libksba-0:1.6.7-5.fc44.aarch6 100% |  51.4 MiB/s | 157.8 KiB |  00m00s
[119/186] libcap-0:2.78-1.fc44.aarch64  100% |  28.6 MiB/s |  87.8 KiB |  00m00s
[120/186] rpm-sequoia-0:1.10.3-1.fc44.a 100% | 144.0 MiB/s | 884.8 KiB |  00m00s
[121/186] sqlite-libs-0:3.51.2-2.fc44.a 100% | 104.6 MiB/s | 749.6 KiB |  00m00s
[122/186] file-libs-0:5.48-2.fc44.aarch 100% | 122.4 MiB/s | 877.5 KiB |  00m00s
[123/186] file-0:5.48-2.fc44.aarch64    100% |  12.2 MiB/s |  50.1 KiB |  00m00s
[124/186] libgomp-0:16.2.1-3.fc44.aarch 100% | 125.0 MiB/s | 384.1 KiB |  00m00s
[125/186] alternatives-0:1.33-5.fc44.aa 100% |  19.7 MiB/s |  40.4 KiB |  00m00s
[126/186] jansson-0:2.14-4.fc44.aarch64 100% |  15.1 MiB/s |  46.5 KiB |  00m00s
[127/186] debugedit-0:5.3-2.fc44.aarch6 100% |  28.2 MiB/s |  86.7 KiB |  00m00s
[128/186] libxml2-0:2.12.10-6.fc44.aarc 100% | 133.2 MiB/s | 682.0 KiB |  00m00s
[129/186] libarchive-0:3.8.7-1.fc44.aar 100% |  58.0 MiB/s | 416.0 KiB |  00m00s
[130/186] lz4-libs-0:1.10.0-4.fc44.aarc 100% |  18.7 MiB/s |  76.6 KiB |  00m00s
[131/186] curl-0:8.18.0-14.fc44.aarch64 100% |  57.5 MiB/s | 235.4 KiB |  00m00s
[132/186] R-srpm-macros-0:1.3.7-2.fc44. 100% |   3.6 MiB/s |  11.0 KiB |  00m00s
[133/186] build-reproducibility-srpm-ma 100% |   6.5 MiB/s |  13.3 KiB |  00m00s
[134/186] binutils-0:2.46.1-1.fc44.aarc 100% | 229.6 MiB/s |   6.4 MiB |  00m00s
[135/186] linkdupes-0:0.7.3-2.fc44.aarc 100% |  35.6 MiB/s | 328.1 KiB |  00m00s
[136/186] erlang-srpm-macros-0:0.4.0-1. 100% |   4.9 MiB/s |  10.0 KiB |  00m00s
[137/186] package-notes-srpm-macros-0:0 100% |  10.1 MiB/s |  10.4 KiB |  00m00s
[138/186] add-determinism-0:0.7.3-2.fc4 100% |  60.1 MiB/s | 800.6 KiB |  00m00s
[139/186] pyproject-srpm-macros-0:1.24. 100% |  15.1 MiB/s |  15.4 KiB |  00m00s
[140/186] qt6-srpm-macros-0:6.11.2-1.fc 100% |   8.9 MiB/s |   9.1 KiB |  00m00s
[141/186] rust-srpm-macros-0:28.5-1.fc4 100% |  10.9 MiB/s |  11.2 KiB |  00m00s
[142/186] ca-certificates-0:2026.2.92_v 100% | 319.1 MiB/s | 980.4 KiB |  00m00s
[143/186] libffi-0:3.5.2-2.fc44.aarch64 100% |   9.7 MiB/s |  39.6 KiB |  00m00s
[144/186] libgcrypt-0:1.12.2-1.fc44.aar 100% | 104.7 MiB/s | 535.9 KiB |  00m00s
[145/186] libidn2-0:2.3.8-3.fc44.aarch6 100% |  56.8 MiB/s | 174.6 KiB |  00m00s
[146/186] libtasn1-0:4.21.0-1.fc44.aarc 100% |  14.3 MiB/s |  73.4 KiB |  00m00s
[147/186] gnutls-0:3.8.13-1.fc44.aarch6 100% | 130.1 MiB/s |   1.3 MiB |  00m00s
[148/186] libunistring-0:1.1-11.fc44.aa 100% |  75.1 MiB/s | 538.3 KiB |  00m00s
[149/186] nettle-0:3.10.1-3.fc44.aarch6 100% |  83.1 MiB/s | 425.5 KiB |  00m00s
[150/186] openldap-0:2.6.16-1.fc44.aarc 100% |  82.3 MiB/s | 252.8 KiB |  00m00s
[151/186] cyrus-sasl-lib-0:2.1.28-35.fc 100% | 151.0 MiB/s | 772.9 KiB |  00m00s
[152/186] libtool-ltdl-0:2.5.4-10.fc44. 100% |   8.7 MiB/s |  35.6 KiB |  00m00s
[153/186] gdbm-libs-1:1.23-11.fc44.aarc 100% |  18.3 MiB/s |  56.3 KiB |  00m00s
[154/186] fedora-repos-0:44-2.noarch    100% |   8.9 MiB/s |   9.1 KiB |  00m00s
[155/186] elfutils-default-yama-scope-0 100% |  11.2 MiB/s |  11.5 KiB |  00m00s
[156/186] fedora-gpg-keys-0:44-2.noarch 100% |  91.6 MiB/s | 187.6 KiB |  00m00s
[157/186] libpfm-0:4.14.1-2.fc44.aarch6 100% |  40.5 MiB/s |  82.9 KiB |  00m00s
[158/186] p11-kit-0:0.26.5-1.fc44.aarch 100% | 173.9 MiB/s | 534.2 KiB |  00m00s
[159/186] p11-kit-trust-0:0.26.5-1.fc44 100% |  34.0 MiB/s | 139.4 KiB |  00m00s
[160/186] libevent-0:2.1.13-1.fc44.aarc 100% |  88.7 MiB/s | 272.6 KiB |  00m00s
[161/186] fedora-release-0:44-18.noarch 100% |   4.1 MiB/s |  12.5 KiB |  00m00s
[162/186] systemd-standalone-sysusers-0 100% |  46.0 MiB/s | 141.3 KiB |  00m00s
[163/186] xxhash-libs-0:0.8.4-2.fc44.aa 100% |  11.1 MiB/s |  34.0 KiB |  00m00s
[164/186] fedora-release-identity-basic 100% |   3.2 MiB/s |  13.2 KiB |  00m00s
[165/186] libcurl-0:8.18.0-14.fc44.aarc 100% |  69.2 MiB/s | 424.9 KiB |  00m00s
[166/186] libnghttp3-0:1.18.0-1.fc44.aa 100% |  13.7 MiB/s |  70.1 KiB |  00m00s
[167/186] libssh-0:0.12.2-1.fc44.aarch6 100% |  54.1 MiB/s | 277.1 KiB |  00m00s
[168/186] ngtcp2-0:1.22.1-1.fc44.aarch6 100% |  28.8 MiB/s | 147.2 KiB |  00m00s
[169/186] gdb-minimal-0:18.1-1.fc44.aar 100% | 175.7 MiB/s |   4.4 MiB |  00m00s
[170/186] libssh-config-0:0.12.2-1.fc44 100% |   1.2 MiB/s |   8.9 KiB |  00m00s
[171/186] libpsl-0:0.21.5-7.fc44.aarch6 100% |   7.9 MiB/s |  64.9 KiB |  00m00s
[172/186] libfido2-0:1.16.0-5.fc44.aarc 100% |  47.0 MiB/s |  96.2 KiB |  00m00s
[173/186] publicsuffix-list-dafsa-0:202 100% |  29.4 MiB/s |  60.3 KiB |  00m00s
[174/186] libcbor-0:0.13.0-2.fc44.aarch 100% |  11.2 MiB/s |  34.4 KiB |  00m00s
[175/186] ngtcp2-crypto-ossl-0:1.22.1-1 100% |   5.1 MiB/s |  26.0 KiB |  00m00s
[176/186] selinux-policy-0:44.11-1.fc44 100% |   4.4 MiB/s |  27.0 KiB |  00m00s
[177/186] rpm-plugin-selinux-0:6.0.2-1. 100% |   3.3 MiB/s |  17.1 KiB |  00m00s
[178/186] policycoreutils-0:3.11-2.fc44 100% |  50.9 MiB/s | 260.6 KiB |  00m00s
[179/186] libselinux-utils-0:3.11-2.fc4 100% |  17.2 MiB/s | 123.4 KiB |  00m00s
[180/186] krb5-libs-0:1.22.2-4.fc44.aar 100% |  92.9 MiB/s | 761.3 KiB |  00m00s
[181/186] keyutils-libs-0:1.6.3-7.fc44. 100% |   6.1 MiB/s |  31.5 KiB |  00m00s
[182/186] libcom_err-0:1.47.3-4.fc44.aa 100% |   5.3 MiB/s |  27.0 KiB |  00m00s
[183/186] libverto-0:0.3.2-12.fc44.aarc 100% |   3.3 MiB/s |  20.5 KiB |  00m00s
[184/186] libbrotli-0:1.2.1-1.fc44.aarc 100% |  68.1 MiB/s | 348.6 KiB |  00m00s
[185/186] selinux-policy-targeted-0:44. 100% | 161.6 MiB/s |   6.8 MiB |  00m00s
[186/186] libnghttp2-0:1.68.0-5.fc44.aa 100% |   6.6 MiB/s |  74.0 KiB |  00m00s
--------------------------------------------------------------------------------
[186/186] Total                         100% | 191.5 MiB/s |  66.8 MiB |  00m00s
Running transaction
Importing OpenPGP key 0x6D9F90A6:
 UserID     : "Fedora (44) <fedora-44-primary@fedoraproject.org>"
 Fingerprint: 36F612DCF27F7D1A48A835E4DBFCF71C6D9F90A6
 From       : file:///usr/share/distribution-gpg-keys/fedora/RPM-GPG-KEY-fedora-44-primary
The key was successfully imported.
[  1/188] Verify package files          100% | 771.0   B/s | 186.0   B |  00m00s
[  2/188] Prepare transaction           100% |   2.5 KiB/s | 186.0   B |  00m00s
[  3/188] Installing libgcc-0:16.2.1-3. 100% | 109.4 MiB/s | 224.0 KiB |  00m00s
[  4/188] Installing publicsuffix-list- 100% |  69.4 MiB/s |  71.1 KiB |  00m00s
[  5/188] Installing libssh-config-0:0. 100% |   0.0   B/s | 816.0   B |  00m00s
[  6/188] Installing fedora-release-ide 100% | 867.2 KiB/s | 888.0   B |  00m00s
[  7/188] Installing fedora-gpg-keys-0: 100% |  31.5 MiB/s | 355.3 KiB |  00m00s
[  8/188] Installing fedora-repos-0:44- 100% |   0.0   B/s |   5.7 KiB |  00m00s
[  9/188] Installing fedora-release-com 100% |  24.3 MiB/s |  24.9 KiB |  00m00s
[ 10/188] Installing fedora-release-0:4 100% |  17.3 KiB/s | 124.0   B |  00m00s
>>> Running sysusers scriptlet: setup-0:2.15.0-28.fc44.noarch
>>> Finished sysusers scriptlet: setup-0:2.15.0-28.fc44.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.
>>> 
[ 11/188] Installing setup-0:2.15.0-28. 100% |  37.5 MiB/s | 730.6 KiB |  00m00s
>>> [RPM] /etc/hosts created as /etc/hosts.rpmnew
[ 12/188] Installing filesystem-0:3.18- 100% |   2.0 MiB/s | 212.8 KiB |  00m00s
[ 13/188] Installing rust-srpm-macros-0 100% |   6.2 MiB/s |   6.4 KiB |  00m00s
[ 14/188] Installing qt6-srpm-macros-0: 100% |   0.0   B/s | 748.0   B |  00m00s
[ 15/188] Installing package-notes-srpm 100% |   0.0   B/s |   2.1 KiB |  00m00s
[ 16/188] Installing pkgconf-m4-0:2.5.1 100% |  14.4 MiB/s |  14.7 KiB |  00m00s
[ 17/188] Installing gnulib-l10n-0:2024 100% | 129.3 MiB/s | 661.9 KiB |  00m00s
[ 18/188] Installing coreutils-common-0 100% | 314.3 MiB/s |  10.7 MiB |  00m00s
[ 19/188] Installing pcre2-syntax-0:10. 100% | 138.8 MiB/s | 284.3 KiB |  00m00s
[ 20/188] Installing ncurses-base-0:6.6 100% |  57.8 MiB/s | 355.3 KiB |  00m00s
[ 21/188] Installing bash-0:5.3.9-3.fc4 100% | 216.2 MiB/s |   8.4 MiB |  00m00s
[ 22/188] Installing glibc-common-0:2.4 100% |  60.2 MiB/s |   1.3 MiB |  00m00s
[ 23/188] Installing glibc-gconv-extra- 100% | 396.5 MiB/s |  18.6 MiB |  00m00s
[ 24/188] Installing glibc-0:2.43-9.fc4 100% | 148.5 MiB/s |   6.4 MiB |  00m00s
[ 25/188] Installing ncurses-libs-0:6.6 100% | 249.2 MiB/s |   1.2 MiB |  00m00s
[ 26/188] Installing glibc-minimal-lang 100% |   0.0   B/s | 124.0   B |  00m00s
[ 27/188] Installing zlib-ng-compat-0:2 100% | 131.1 MiB/s | 134.2 KiB |  00m00s
[ 28/188] Installing bzip2-libs-0:1.0.8 100% |  71.9 MiB/s |  73.6 KiB |  00m00s
[ 29/188] Installing libgpg-error-0:1.5 100% |  48.6 MiB/s | 995.5 KiB |  00m00s
[ 30/188] Installing libstdc++-0:16.2.1 100% | 330.1 MiB/s |   3.0 MiB |  00m00s
[ 31/188] Installing libassuan-0:2.5.7- 100% | 212.6 MiB/s | 217.7 KiB |  00m00s
[ 32/188] Installing libgcrypt-0:1.12.2 100% | 240.0 MiB/s |   1.2 MiB |  00m00s
[ 33/188] Installing readline-0:8.3-4.f 100% | 276.2 MiB/s | 565.6 KiB |  00m00s
[ 34/188] Installing gmp-1:6.3.0-5.fc44 100% | 214.9 MiB/s | 660.1 KiB |  00m00s
[ 35/188] Installing xz-libs-1:5.8.2-2. 100% | 260.2 MiB/s | 266.5 KiB |  00m00s
[ 36/188] Installing libuuid-0:2.41.6-1 100% |  68.7 MiB/s |  70.3 KiB |  00m00s
[ 37/188] Installing libzstd-0:1.5.7-5. 100% | 280.4 MiB/s | 861.5 KiB |  00m00s
[ 38/188] Installing elfutils-libelf-0: 100% | 329.6 MiB/s |   1.6 MiB |  00m00s
[ 39/188] Installing libacl-0:2.4.0-1.f 100% |  67.1 MiB/s |  68.8 KiB |  00m00s
[ 40/188] Installing popt-0:1.19-10.fc4 100% |  49.2 MiB/s | 151.3 KiB |  00m00s
[ 41/188] Installing systemd-libs-0:259 100% | 307.7 MiB/s |   2.5 MiB |  00m00s
[ 42/188] Installing npth-0:1.8-4.fc44. 100% |  92.2 MiB/s |  94.4 KiB |  00m00s
[ 43/188] Installing libblkid-0:2.41.6- 100% | 284.5 MiB/s | 291.3 KiB |  00m00s
[ 44/188] Installing libxcrypt-0:4.5.2- 100% | 134.7 MiB/s | 275.9 KiB |  00m00s
[ 45/188] Installing libsepol-0:3.11-1. 100% | 264.0 MiB/s | 810.9 KiB |  00m00s
[ 46/188] Installing sqlite-libs-0:3.51 100% | 261.2 MiB/s |   1.6 MiB |  00m00s
[ 47/188] Installing gnupg2-gpgconf-0:2 100% |  18.6 MiB/s | 323.9 KiB |  00m00s
[ 48/188] Installing pcre2-0:10.47-1.fc 100% | 233.0 MiB/s | 715.7 KiB |  00m00s
[ 49/188] Installing libselinux-0:3.11- 100% | 130.1 MiB/s | 266.4 KiB |  00m00s
[ 50/188] Installing grep-0:3.12-3.fc44 100% |  47.2 MiB/s |   1.0 MiB |  00m00s
[ 51/188] Installing sed-0:4.9-7.fc44.a 100% |  41.0 MiB/s | 881.3 KiB |  00m00s
[ 52/188] Installing findutils-1:4.10.0 100% |  80.0 MiB/s |   1.9 MiB |  00m00s
[ 53/188] Installing json-c-0:0.18-8.fc 100% | 136.5 MiB/s | 139.7 KiB |  00m00s
[ 54/188] Installing libtasn1-0:4.21.0- 100% | 217.3 MiB/s | 222.5 KiB |  00m00s
[ 55/188] Installing libunistring-0:1.1 100% | 291.0 MiB/s |   1.7 MiB |  00m00s
[ 56/188] Installing libidn2-0:2.3.8-3. 100% |  55.3 MiB/s | 566.6 KiB |  00m00s
[ 57/188] Installing crypto-policies-0: 100% |  22.0 MiB/s | 157.7 KiB |  00m00s
[ 58/188] Installing xz-1:5.8.2-2.fc44. 100% |  57.9 MiB/s |   1.4 MiB |  00m00s
[ 59/188] Installing libmount-0:2.41.6- 100% | 205.7 MiB/s | 421.3 KiB |  00m00s
[ 60/188] Installing gnupg2-verify-0:2. 100% |  21.0 MiB/s | 365.7 KiB |  00m00s
[ 61/188] Installing dwz-0:0.16-3.fc44. 100% |  18.6 MiB/s | 323.8 KiB |  00m00s
[ 62/188] Installing mpfr-0:4.2.2-3.fc4 100% | 184.8 MiB/s | 757.1 KiB |  00m00s
[ 63/188] Installing gawk-0:5.3.2-3.fc4 100% | 107.5 MiB/s |   2.6 MiB |  00m00s
[ 64/188] Installing libksba-0:1.6.7-5. 100% | 195.7 MiB/s | 400.8 KiB |  00m00s
[ 65/188] Installing unzip-0:6.0-69.fc4 100% |  29.2 MiB/s | 537.3 KiB |  00m00s
[ 66/188] Installing file-libs-0:5.48-2 100% | 618.0 MiB/s |  12.4 MiB |  00m00s
[ 67/188] Installing file-0:5.48-2.fc44 100% |   8.2 MiB/s | 143.4 KiB |  00m00s
[ 68/188] Installing diffutils-0:3.12-5 100% |  70.6 MiB/s |   1.6 MiB |  00m00s
[ 69/188] Installing libeconf-0:0.7.9-3 100% |  80.5 MiB/s |  82.5 KiB |  00m00s
[ 70/188] Installing libsmartcols-0:2.4 100% | 220.1 MiB/s | 225.4 KiB |  00m00s
[ 71/188] Installing lua-libs-0:5.4.8-5 100% | 161.7 MiB/s | 331.3 KiB |  00m00s
[ 72/188] Installing libattr-0:2.6.0-1. 100% |  67.7 MiB/s |  69.3 KiB |  00m00s
[ 73/188] Installing libcap-ng-0:0.9.6- 100% | 159.0 MiB/s | 162.8 KiB |  00m00s
[ 74/188] Installing audit-libs-0:4.2.1 100% | 238.9 MiB/s | 489.3 KiB |  00m00s
[ 75/188] Installing pam-libs-0:1.7.2-2 100% | 219.7 MiB/s | 225.0 KiB |  00m00s
[ 76/188] Installing libcap-0:2.78-1.fc 100% |  27.9 MiB/s | 513.5 KiB |  00m00s
[ 77/188] Installing libsemanage-0:3.11 100% | 176.7 MiB/s | 361.8 KiB |  00m00s
[ 78/188] Installing alternatives-0:1.3 100% |   5.3 MiB/s |  91.6 KiB |  00m00s
[ 79/188] Installing libffi-0:3.5.2-2.f 100% | 153.4 MiB/s | 157.1 KiB |  00m00s
[ 80/188] Installing p11-kit-0:0.26.5-1 100% | 100.0 MiB/s |   2.8 MiB |  00m00s
[ 81/188] Installing p11-kit-trust-0:0. 100% |  21.6 MiB/s | 531.9 KiB |  00m00s
[ 82/188] Installing ngtcp2-0:1.22.1-1. 100% | 167.3 MiB/s | 342.7 KiB |  00m00s
[ 83/188] Installing openssl-libs-1:3.5 100% | 325.4 MiB/s |   7.5 MiB |  00m00s
[ 84/188] Installing coreutils-0:9.10-5 100% | 182.7 MiB/s |   8.2 MiB |  00m00s
[ 85/188] Installing ca-certificates-0: 100% |   1.5 MiB/s |   2.5 MiB |  00m02s
[ 86/188] Installing gzip-0:1.14-2.fc44 100% |  22.8 MiB/s | 443.0 KiB |  00m00s
[ 87/188] Installing libfsverity-0:1.6- 100% |  67.7 MiB/s |  69.3 KiB |  00m00s
[ 88/188] Installing rpm-sequoia-0:1.10 100% | 286.5 MiB/s |   2.3 MiB |  00m00s
[ 89/188] Installing libevent-0:2.1.13- 100% | 271.7 MiB/s |   1.1 MiB |  00m00s
[ 90/188] Installing ngtcp2-crypto-ossl 100% |  66.7 MiB/s |  68.3 KiB |  00m00s
[ 91/188] Installing liblastlog2-0:2.41 100% |  22.8 MiB/s | 139.8 KiB |  00m00s
[ 92/188] Installing util-linux-core-0: 100% |  94.8 MiB/s |   2.5 MiB |  00m00s
[ 93/188] Installing zip-0:3.0-45.fc44. 100% |  39.4 MiB/s | 765.8 KiB |  00m00s
[ 94/188] Installing gnupg2-keyboxd-0:2 100% |  38.2 MiB/s | 234.5 KiB |  00m00s
[ 95/188] Installing libpsl-0:0.21.5-7. 100% | 130.3 MiB/s | 133.4 KiB |  00m00s
[ 96/188] Installing tar-2:1.35-9.fc44. 100% | 111.3 MiB/s |   3.0 MiB |  00m00s
[ 97/188] Installing linkdupes-0:0.7.3- 100% |  36.8 MiB/s | 715.1 KiB |  00m00s
[ 98/188] Installing libselinux-utils-0 100% |  64.6 MiB/s |   1.4 MiB |  00m00s
[ 99/188] Installing systemd-standalone 100% |  19.0 MiB/s | 330.2 KiB |  00m00s
[100/188] Installing rpm-libs-0:6.0.2-1 100% | 243.3 MiB/s | 996.7 KiB |  00m00s
[101/188] Installing libfdisk-0:2.41.6- 100% | 136.7 MiB/s | 419.9 KiB |  00m00s
[102/188] Installing util-linux-0:2.41. 100% | 145.9 MiB/s |   6.9 MiB |  00m00s
[103/188] Installing policycoreutils-0: 100% |  45.0 MiB/s |   1.5 MiB |  00m00s
[104/188] Installing selinux-policy-0:4 100% |   1.5 MiB/s |  34.5 KiB |  00m00s
[105/188] Installing selinux-policy-tar 100% | 142.0 MiB/s |  14.9 MiB |  00m00s
[106/188] Installing libusb1-0:1.0.30-1 100% |  19.7 MiB/s | 181.4 KiB |  00m00s
>>> Running sysusers scriptlet: tpm2-tss-0:4.1.5-1.fc44.aarch64
>>> Finished sysusers scriptlet: tpm2-tss-0:4.1.5-1.fc44.aarch64
>>> Scriptlet output:
>>> Creating group 'tss' with GID 59.
>>> Creating user 'tss' (Account used for TPM access) with UID 59 and GID 59.
>>> 
[107/188] Installing tpm2-tss-0:4.1.5-1 100% | 270.0 MiB/s |   2.2 MiB |  00m00s
[108/188] Installing ima-evm-utils-libs 100% |  91.7 MiB/s |  93.9 KiB |  00m00s
[109/188] Installing gnupg2-gpg-agent-0 100% |  32.0 MiB/s | 850.9 KiB |  00m00s
[110/188] Installing zstd-0:1.5.7-5.fc4 100% |  29.1 MiB/s | 565.8 KiB |  00m00s
[111/188] Installing libxml2-0:2.12.10- 100% |  82.2 MiB/s |   1.9 MiB |  00m00s
[112/188] Installing nettle-0:3.10.1-3. 100% | 250.1 MiB/s | 768.2 KiB |  00m00s
[113/188] Installing gnutls-0:3.8.13-1. 100% | 309.1 MiB/s |   3.7 MiB |  00m00s
[114/188] Installing bzip2-0:1.0.8-23.f 100% |  10.1 MiB/s | 175.6 KiB |  00m00s
[115/188] Installing add-determinism-0: 100% |  89.6 MiB/s |   2.0 MiB |  00m00s
[116/188] Installing build-reproducibil 100% |   1.5 MiB/s |   1.5 KiB |  00m00s
[117/188] Installing cpio-0:2.15-9.fc44 100% |  52.2 MiB/s |   1.1 MiB |  00m00s
[118/188] Installing ed-0:1.22.5-2.fc44 100% |   9.2 MiB/s | 160.1 KiB |  00m00s
[119/188] Installing patch-0:2.8-4.fc44 100% |  15.2 MiB/s | 263.9 KiB |  00m00s
[120/188] Installing libpkgconf-0:2.5.1 100% | 132.0 MiB/s | 135.2 KiB |  00m00s
[121/188] Installing pkgconf-0:2.5.1-1. 100% |   6.9 MiB/s | 119.3 KiB |  00m00s
[122/188] Installing pkgconf-pkg-config 100% | 110.8 KiB/s |   1.8 KiB |  00m00s
[123/188] Installing libgomp-0:16.2.1-3 100% | 290.2 MiB/s | 594.3 KiB |  00m00s
[124/188] Installing jansson-0:2.14-4.f 100% |  92.2 MiB/s |  94.4 KiB |  00m00s
[125/188] Installing lz4-libs-0:1.10.0- 100% | 193.7 MiB/s | 198.4 KiB |  00m00s
[126/188] Installing libarchive-0:3.8.7 100% | 238.7 MiB/s | 977.5 KiB |  00m00s
[127/188] Installing libtool-ltdl-0:2.5 100% |  92.8 MiB/s |  95.0 KiB |  00m00s
[128/188] Installing gdbm-libs-1:1.23-1 100% | 229.9 MiB/s | 235.4 KiB |  00m00s
[129/188] Installing cyrus-sasl-lib-0:2 100% | 100.7 MiB/s |   2.4 MiB |  00m00s
[130/188] Installing openldap-0:2.6.16- 100% | 188.0 MiB/s | 769.9 KiB |  00m00s
[131/188] Installing gnupg2-dirmngr-0:2 100% |  26.4 MiB/s | 648.9 KiB |  00m00s
[132/188] Installing gnupg2-0:2.4.9-16. 100% | 174.5 MiB/s |   6.5 MiB |  00m00s
[133/188] Installing rpm-sign-libs-0:6. 100% |  66.9 MiB/s |  68.5 KiB |  00m00s
[134/188] Installing gpgverify-0:2.2-4. 100% |   9.2 MiB/s |   9.4 KiB |  00m00s
[135/188] Installing libpfm-0:4.14.1-2. 100% | 251.3 MiB/s | 514.6 KiB |  00m00s
[136/188] Installing xxhash-libs-0:0.8. 100% |  85.1 MiB/s |  87.2 KiB |  00m00s
[137/188] Installing libnghttp3-0:1.18. 100% | 203.3 MiB/s | 208.2 KiB |  00m00s
[138/188] Installing libcbor-0:0.13.0-2 100% | 137.6 MiB/s | 140.9 KiB |  00m00s
[139/188] Installing libfido2-0:1.16.0- 100% | 136.8 MiB/s | 280.1 KiB |  00m00s
[140/188] Installing keyutils-libs-0:1. 100% |  97.3 MiB/s |  99.6 KiB |  00m00s
[141/188] Installing libcom_err-0:1.47. 100% | 109.5 MiB/s | 112.1 KiB |  00m00s
[142/188] Installing libverto-0:0.3.2-1 100% |  69.4 MiB/s |  71.1 KiB |  00m00s
[143/188] Installing krb5-libs-0:1.22.2 100% | 253.4 MiB/s |   2.5 MiB |  00m00s
[144/188] Installing libssh-0:0.12.2-1. 100% | 235.5 MiB/s | 723.4 KiB |  00m00s
[145/188] Installing libbrotli-0:1.2.1- 100% | 222.5 MiB/s | 911.5 KiB |  00m00s
[146/188] Installing libnghttp2-0:1.68. 100% | 194.4 MiB/s | 199.1 KiB |  00m00s
[147/188] Installing libcurl-0:8.18.0-1 100% | 239.8 MiB/s | 982.0 KiB |  00m00s
[148/188] Installing curl-0:8.18.0-14.f 100% |  16.9 MiB/s | 466.8 KiB |  00m00s
[149/188] Installing rpm-0:6.0.2-1.fc44 100% |  68.1 MiB/s |   2.8 MiB |  00m00s
[150/188] Installing cmake-srpm-macros- 100% | 785.2 KiB/s | 804.0   B |  00m00s
[151/188] Installing efi-srpm-macros-0: 100% |  40.2 MiB/s |  41.2 KiB |  00m00s
[152/188] Installing java-srpm-macros-0 100% |   0.0   B/s |   1.1 KiB |  00m00s
[153/188] Installing lua-srpm-macros-0: 100% |   0.0   B/s |   1.9 KiB |  00m00s
[154/188] Installing tree-sitter-srpm-m 100% |   9.1 MiB/s |   9.3 KiB |  00m00s
[155/188] Installing zig-srpm-macros-0: 100% |   0.0   B/s |   1.9 KiB |  00m00s
[156/188] Installing filesystem-srpm-ma 100% |  38.0 MiB/s |  38.9 KiB |  00m00s
[157/188] Installing elfutils-default-y 100% | 408.6 KiB/s |   2.0 KiB |  00m00s
[158/188] Installing elfutils-libs-0:0. 100% | 182.7 MiB/s | 748.5 KiB |  00m00s
[159/188] Installing elfutils-debuginfo 100% |   7.9 MiB/s | 146.1 KiB |  00m00s
[160/188] Installing elfutils-0:0.197-1 100% | 135.5 MiB/s |   3.7 MiB |  00m00s
[161/188] Installing binutils-0:2.46.1- 100% | 300.0 MiB/s |  31.5 MiB |  00m00s
[162/188] Installing gdb-minimal-0:18.1 100% | 241.9 MiB/s |  13.8 MiB |  00m00s
[163/188] Installing debugedit-0:5.3-2. 100% |  17.6 MiB/s | 324.2 KiB |  00m00s
[164/188] Installing rpm-build-libs-0:6 100% | 129.4 MiB/s | 264.9 KiB |  00m00s
[165/188] Installing qt5-srpm-macros-0: 100% |   0.0   B/s | 776.0   B |  00m00s
[166/188] Installing perl-srpm-macros-0 100% |   0.0   B/s |   1.1 KiB |  00m00s
[167/188] Installing openblas-srpm-macr 100% |   0.0   B/s | 392.0   B |  00m00s
[168/188] Installing ocaml-srpm-macros- 100% |   0.0   B/s |   2.1 KiB |  00m00s
[169/188] Installing kernel-srpm-macros 100% |   0.0   B/s |   2.3 KiB |  00m00s
[170/188] Installing gnat-srpm-macros-0 100% |   0.0   B/s |   1.3 KiB |  00m00s
[171/188] Installing ghc-srpm-macros-0: 100% |   0.0   B/s |   1.0 KiB |  00m00s
[172/188] Installing gap-srpm-macros-0: 100% |   0.0   B/s |   2.7 KiB |  00m00s
[173/188] Installing fpc-srpm-macros-0: 100% |   0.0   B/s | 420.0   B |  00m00s
[174/188] Installing ansible-srpm-macro 100% |   0.0   B/s |  36.2 KiB |  00m00s
[175/188] Installing gnome-srpm-macros- 100% |   4.0 MiB/s |   4.1 KiB |  00m00s
[176/188] Installing redhat-rpm-config- 100% |  92.7 MiB/s | 189.9 KiB |  00m00s
[177/188] Installing forge-srpm-macros- 100% |  39.3 MiB/s |  40.3 KiB |  00m00s
[178/188] Installing rpm-build-0:6.0.2- 100% |  29.7 MiB/s | 607.8 KiB |  00m00s
[179/188] Installing erlang-srpm-macros 100% |   2.5 MiB/s |   2.6 KiB |  00m00s
[180/188] Installing pyproject-srpm-mac 100% | 665.4 KiB/s |   4.0 KiB |  00m00s
[181/188] Installing fonts-srpm-macros- 100% |  55.7 MiB/s |  57.0 KiB |  00m00s
[182/188] Installing go-srpm-macros-0:3 100% |  61.6 MiB/s |  63.0 KiB |  00m00s
[183/188] Installing python-srpm-macros 100% |  51.7 MiB/s |  52.9 KiB |  00m00s
[184/188] Installing R-srpm-macros-0:1. 100% |   4.3 MiB/s |   4.4 KiB |  00m00s
[185/188] Installing rpm-plugin-selinux 100% |  67.3 MiB/s |  68.9 KiB |  00m00s
[186/188] Installing which-0:2.25-1.fc4 100% |   6.5 MiB/s | 125.8 KiB |  00m00s
[187/188] Installing shadow-utils-2:4.1 100% | 109.6 MiB/s |   4.5 MiB |  00m00s
[188/188] Installing info-0:7.2-9.fc44. 100% |  50.4 KiB/s | 421.9 KiB |  00m08s
Complete!
Finish: installing minimal buildroot with dnf5
Start: creating root cache
Finish: creating root cache
Finish: chroot init
INFO: Installed packages:
INFO: R-srpm-macros-1.3.7-2.fc44.noarch
add-determinism-0.7.3-2.fc44.aarch64
alternatives-1.33-5.fc44.aarch64
ansible-srpm-macros-1-20.1.fc44.noarch
audit-libs-4.2.1-1.fc44.aarch64
bash-5.3.9-3.fc44.aarch64
binutils-2.46.1-1.fc44.aarch64
build-reproducibility-srpm-macros-0.7.3-2.fc44.noarch
bzip2-1.0.8-23.fc44.aarch64
bzip2-libs-1.0.8-23.fc44.aarch64
ca-certificates-2026.2.92_v9.0.319-2.fc44.noarch
cmake-srpm-macros-4.3.0-1.fc44.noarch
coreutils-9.10-5.fc44.aarch64
coreutils-common-9.10-5.fc44.aarch64
cpio-2.15-9.fc44.aarch64
crypto-policies-20251128-3.git19878fe.fc44.noarch
curl-8.18.0-14.fc44.aarch64
cyrus-sasl-lib-2.1.28-35.fc44.aarch64
debugedit-5.3-2.fc44.aarch64
diffutils-3.12-5.fc44.aarch64
dwz-0.16-3.fc44.aarch64
ed-1.22.5-2.fc44.aarch64
efi-srpm-macros-6-6.fc44.noarch
elfutils-0.197-1.fc44.aarch64
elfutils-debuginfod-client-0.197-1.fc44.aarch64
elfutils-default-yama-scope-0.197-1.fc44.noarch
elfutils-libelf-0.197-1.fc44.aarch64
elfutils-libs-0.197-1.fc44.aarch64
erlang-srpm-macros-0.4.0-1.fc44.noarch
fedora-gpg-keys-44-2.noarch
fedora-release-44-18.noarch
fedora-release-common-44-18.noarch
fedora-release-identity-basic-44-18.noarch
fedora-repos-44-2.noarch
file-5.48-2.fc44.aarch64
file-libs-5.48-2.fc44.aarch64
filesystem-3.18-52.fc44.aarch64
filesystem-srpm-macros-3.18-52.fc44.noarch
findutils-4.10.0-7.fc44.aarch64
fonts-srpm-macros-5.0.0-2.fc44.noarch
forge-srpm-macros-0.4.0-4.fc44.noarch
fpc-srpm-macros-1.3-16.fc44.noarch
gap-srpm-macros-2-2.fc44.noarch
gawk-5.3.2-3.fc44.aarch64
gdb-minimal-18.1-1.fc44.aarch64
gdbm-libs-1.23-11.fc44.aarch64
ghc-srpm-macros-1.10-1.fc44.noarch
glibc-2.43-9.fc44.aarch64
glibc-common-2.43-9.fc44.aarch64
glibc-gconv-extra-2.43-9.fc44.aarch64
glibc-minimal-langpack-2.43-9.fc44.aarch64
gmp-6.3.0-5.fc44.aarch64
gnat-srpm-macros-7-2.fc44.noarch
gnome-srpm-macros-1.0-1.fc44.noarch
gnulib-l10n-20241231-2.fc44.noarch
gnupg2-2.4.9-16.fc44.aarch64
gnupg2-dirmngr-2.4.9-16.fc44.aarch64
gnupg2-gpg-agent-2.4.9-16.fc44.aarch64
gnupg2-gpgconf-2.4.9-16.fc44.aarch64
gnupg2-keyboxd-2.4.9-16.fc44.aarch64
gnupg2-verify-2.4.9-16.fc44.aarch64
gnutls-3.8.13-1.fc44.aarch64
go-srpm-macros-3.8.0-2.fc44.noarch
gpg-pubkey-36f612dcf27f7d1a48a835e4dbfcf71c6d9f90a6-6786af3b
gpgverify-2.2-4.fc44.noarch
grep-3.12-3.fc44.aarch64
gzip-1.14-2.fc44.aarch64
ima-evm-utils-libs-1.6.2-8.fc44.aarch64
info-7.2-9.fc44.aarch64
jansson-2.14-4.fc44.aarch64
java-srpm-macros-1-8.fc44.noarch
json-c-0.18-8.fc44.aarch64
kernel-srpm-macros-1.0-28.fc44.noarch
keyutils-libs-1.6.3-7.fc44.aarch64
krb5-libs-1.22.2-4.fc44.aarch64
libacl-2.4.0-1.fc44.aarch64
libarchive-3.8.7-1.fc44.aarch64
libassuan-2.5.7-5.fc44.aarch64
libattr-2.6.0-1.fc44.aarch64
libblkid-2.41.6-1.fc44.aarch64
libbrotli-1.2.1-1.fc44.aarch64
libcap-2.78-1.fc44.aarch64
libcap-ng-0.9.6-1.fc44.aarch64
libcbor-0.13.0-2.fc44.aarch64
libcom_err-1.47.3-4.fc44.aarch64
libcurl-8.18.0-14.fc44.aarch64
libeconf-0.7.9-3.fc44.aarch64
libevent-2.1.13-1.fc44.aarch64
libfdisk-2.41.6-1.fc44.aarch64
libffi-3.5.2-2.fc44.aarch64
libfido2-1.16.0-5.fc44.aarch64
libfsverity-1.6-4.fc44.aarch64
libgcc-16.2.1-3.fc44.aarch64
libgcrypt-1.12.2-1.fc44.aarch64
libgomp-16.2.1-3.fc44.aarch64
libgpg-error-1.58-2.fc44.aarch64
libidn2-2.3.8-3.fc44.aarch64
libksba-1.6.7-5.fc44.aarch64
liblastlog2-2.41.6-1.fc44.aarch64
libmount-2.41.6-1.fc44.aarch64
libnghttp2-1.68.0-5.fc44.aarch64
libnghttp3-1.18.0-1.fc44.aarch64
libpfm-4.14.1-2.fc44.aarch64
libpkgconf-2.5.1-1.fc44.aarch64
libpsl-0.21.5-7.fc44.aarch64
libselinux-3.11-2.fc44.aarch64
libselinux-utils-3.11-2.fc44.aarch64
libsemanage-3.11-1.fc44.aarch64
libsepol-3.11-1.fc44.aarch64
libsmartcols-2.41.6-1.fc44.aarch64
libssh-0.12.2-1.fc44.aarch64
libssh-config-0.12.2-1.fc44.noarch
libstdc++-16.2.1-3.fc44.aarch64
libtasn1-4.21.0-1.fc44.aarch64
libtool-ltdl-2.5.4-10.fc44.aarch64
libunistring-1.1-11.fc44.aarch64
libusb1-1.0.30-1.fc44.aarch64
libuuid-2.41.6-1.fc44.aarch64
libverto-0.3.2-12.fc44.aarch64
libxcrypt-4.5.2-3.fc44.aarch64
libxml2-2.12.10-6.fc44.aarch64
libzstd-1.5.7-5.fc44.aarch64
linkdupes-0.7.3-2.fc44.aarch64
lua-libs-5.4.8-5.fc44.aarch64
lua-srpm-macros-1-17.fc44.noarch
lz4-libs-1.10.0-4.fc44.aarch64
mpfr-4.2.2-3.fc44.aarch64
ncurses-base-6.6-1.fc44.noarch
ncurses-libs-6.6-1.fc44.aarch64
nettle-3.10.1-3.fc44.aarch64
ngtcp2-1.22.1-1.fc44.aarch64
ngtcp2-crypto-ossl-1.22.1-1.fc44.aarch64
npth-1.8-4.fc44.aarch64
ocaml-srpm-macros-11-3.fc44.noarch
openblas-srpm-macros-2-21.fc44.noarch
openldap-2.6.16-1.fc44.aarch64
openssl-libs-3.5.9-1.fc44.aarch64
p11-kit-0.26.5-1.fc44.aarch64
p11-kit-trust-0.26.5-1.fc44.aarch64
package-notes-srpm-macros-0.20-1.fc44.noarch
pam-libs-1.7.2-2.fc44.aarch64
patch-2.8-4.fc44.aarch64
pcre2-10.47-1.fc44.1.aarch64
pcre2-syntax-10.47-1.fc44.1.noarch
perl-srpm-macros-1-61.fc44.noarch
pkgconf-2.5.1-1.fc44.aarch64
pkgconf-m4-2.5.1-1.fc44.noarch
pkgconf-pkg-config-2.5.1-1.fc44.aarch64
policycoreutils-3.11-2.fc44.aarch64
popt-1.19-10.fc44.aarch64
publicsuffix-list-dafsa-20260116-1.fc44.noarch
pyproject-srpm-macros-1.24.0-1.fc44.noarch
python-srpm-macros-3.14-12.fc44.noarch
qt5-srpm-macros-5.15.18-2.fc44.noarch
qt6-srpm-macros-6.11.2-1.fc44.noarch
readline-8.3-4.fc44.aarch64
redhat-rpm-config-344-2.fc44.noarch
rpm-6.0.2-1.fc44.aarch64
rpm-build-6.0.2-1.fc44.aarch64
rpm-build-libs-6.0.2-1.fc44.aarch64
rpm-libs-6.0.2-1.fc44.aarch64
rpm-plugin-selinux-6.0.2-1.fc44.aarch64
rpm-sequoia-1.10.3-1.fc44.aarch64
rpm-sign-libs-6.0.2-1.fc44.aarch64
rust-srpm-macros-28.5-1.fc44.noarch
sed-4.9-7.fc44.aarch64
selinux-policy-44.11-1.fc44.noarch
selinux-policy-targeted-44.11-1.fc44.noarch
setup-2.15.0-28.fc44.noarch
shadow-utils-4.19.0-7.fc44.aarch64
sqlite-libs-3.51.2-2.fc44.aarch64
systemd-libs-259.10-1.fc44.aarch64
systemd-standalone-sysusers-259.10-1.fc44.aarch64
tar-1.35-9.fc44.aarch64
tpm2-tss-4.1.5-1.fc44.aarch64
tree-sitter-srpm-macros-0.4.2-2.fc44.noarch
unzip-6.0-69.fc44.aarch64
util-linux-2.41.6-1.fc44.aarch64
util-linux-core-2.41.6-1.fc44.aarch64
which-2.25-1.fc44.aarch64
xxhash-libs-0.8.4-2.fc44.aarch64
xz-5.8.2-2.fc44.aarch64
xz-libs-5.8.2-2.fc44.aarch64
zig-srpm-macros-1-8.fc44.noarch
zip-3.0-45.fc44.aarch64
zlib-ng-compat-2.3.3-3.fc44.aarch64
zstd-1.5.7-5.fc44.aarch64
Start: buildsrpm
Start: rpmbuild -bs
Building target platforms: aarch64
Building for target aarch64
setting SOURCE_DATE_EPOCH=1793923200
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc44.src.rpm
Finish: rpmbuild -bs
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-44-aarch64-1794040424.031981/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-wcmlgyjl/python-pyradplan/python-pyradplan.spec) Config(child) 0 minutes 30 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.fc44.src.rpm)  Config(fedora-44-aarch64)
Start(bootstrap): chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-44-aarch64-bootstrap-1794040424.031981/root.
INFO: reusing tmpfs at /var/lib/mock/fedora-44-aarch64-bootstrap-1794040424.031981/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-44-aarch64-1794040424.031981/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.fc44.aarch64
  rpm-sequoia-1.10.3-1.fc44.aarch64
  dnf5-5.4.6.0-1.fc44.aarch64
  dnf5-plugins-5.4.6.0-1.fc44.aarch64
Finish: chroot init
Start: build phase for python-pyradplan-0.8.0-1.fc44.src.rpm
Start: build setup for python-pyradplan-0.8.0-1.fc44.src.rpm
Building target platforms: aarch64
Building for target aarch64
setting SOURCE_DATE_EPOCH=1793923200
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc44.src.rpm
Updating and loading repositories:
 updates                                100% |   7.5 KiB/s |   5.3 KiB |  00m01s
 fedora                                 100% |  13.2 KiB/s |   9.3 KiB |  00m01s
 Copr repository                        100% |   3.2 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package                 Arch    Version          Repository      Size
Installing:
 pyproject-rpm-macros   noarch  0:1.24.0-1.fc44  updates    169.0 KiB
 python3-devel          aarch64 0:3.14.10-1.fc44 updates      2.0 MiB
 python3-pytest         noarch  0:8.4.2-2.fc44   fedora      22.5 MiB
Installing dependencies:
 expat                  aarch64 0:2.9.0-1.fc44   updates    389.5 KiB
 mpdecimal              aarch64 0:4.0.1-3.fc44   fedora     281.1 KiB
 python-pip-wheel       noarch  0:26.0.1-3.fc44  updates      1.2 MiB
 python-rpm-macros      noarch  0:3.14-12.fc44   fedora      28.0 KiB
 python3                aarch64 0:3.14.10-1.fc44 updates     84.7 KiB
 python3-iniconfig      noarch  0:2.3.0-2.fc44   fedora      49.8 KiB
 python3-libs           aarch64 0:3.14.10-1.fc44 updates     45.8 MiB
 python3-packaging      noarch  0:25.0-8.fc44    fedora     607.4 KiB
 python3-pluggy         noarch  0:1.6.0-5.fc44   fedora     211.5 KiB
 python3-pygments       noarch  0:2.19.1-9.fc44  fedora      11.3 MiB
 python3-rpm-generators noarch  0:14-14.fc44     fedora      81.7 KiB
 python3-rpm-macros     noarch  0:3.14-12.fc44   fedora       6.5 KiB
 tzdata                 noarch  0:2026f-1.fc44   updates      1.3 MiB

Transaction Summary:
 Installing:        16 packages

Total size of inbound packages is 18 MiB. Need to download 18 MiB.
After this operation, 86 MiB extra will be used (install 86 MiB, remove 0 B).
[ 1/16] pyproject-rpm-macros-0:1.24.0-1 100% |   4.4 MiB/s |  58.1 KiB |  00m00s
[ 2/16] python3-iniconfig-0:2.3.0-2.fc4 100% |   8.6 MiB/s |  26.3 KiB |  00m00s
[ 3/16] python3-packaging-0:25.0-8.fc44 100% |  22.5 MiB/s | 161.2 KiB |  00m00s
[ 4/16] python3-pytest-0:8.4.2-2.fc44.n 100% |  74.7 MiB/s |   2.3 MiB |  00m00s
[ 5/16] python3-pluggy-0:1.6.0-5.fc44.n 100% |  10.0 MiB/s |  61.6 KiB |  00m00s
[ 6/16] python-rpm-macros-0:3.14-12.fc4 100% |   9.7 MiB/s |  20.0 KiB |  00m00s
[ 7/16] python3-rpm-macros-0:3.14-12.fc 100% |   6.1 MiB/s |  12.4 KiB |  00m00s
[ 8/16] python3-0:3.14.10-1.fc44.aarch6 100% |   9.7 MiB/s |  29.8 KiB |  00m00s
[ 9/16] python3-libs-0:3.14.10-1.fc44.a 100% | 261.2 MiB/s |   9.9 MiB |  00m00s
[10/16] expat-0:2.9.0-1.fc44.aarch64    100% |  64.0 MiB/s | 131.1 KiB |  00m00s
[11/16] mpdecimal-0:4.0.1-3.fc44.aarch6 100% |  46.6 MiB/s |  95.4 KiB |  00m00s
[12/16] python-pip-wheel-0:26.0.1-3.fc4 100% | 191.6 MiB/s |   1.1 MiB |  00m00s
[13/16] tzdata-0:2026f-1.fc44.noarch    100% | 174.2 MiB/s | 713.6 KiB |  00m00s
[14/16] python3-rpm-generators-0:14-14. 100% |   9.3 MiB/s |  28.6 KiB |  00m00s
[15/16] python3-pygments-0:2.19.1-9.fc4 100% |  28.4 MiB/s |   2.7 MiB |  00m00s
[16/16] python3-devel-0:3.14.10-1.fc44. 100% |   3.2 MiB/s | 448.1 KiB |  00m00s
--------------------------------------------------------------------------------
[16/16] Total                           100% | 130.0 MiB/s |  17.8 MiB |  00m00s
Running transaction
[ 1/18] Verify package files            100% | 280.0   B/s |  16.0   B |  00m00s
[ 2/18] Prepare transaction             100% | 313.0   B/s |  16.0   B |  00m00s
[ 3/18] Installing python-rpm-macros-0: 100% |  28.2 MiB/s |  28.9 KiB |  00m00s
[ 4/18] Installing python3-rpm-macros-0 100% |   6.6 MiB/s |   6.8 KiB |  00m00s
[ 5/18] Installing pyproject-rpm-macros 100% |  27.9 MiB/s | 171.5 KiB |  00m00s
[ 6/18] Installing tzdata-0:2026f-1.fc4 100% |  31.1 MiB/s |   1.5 MiB |  00m00s
[ 7/18] Installing python-pip-wheel-0:2 100% | 407.8 MiB/s |   1.2 MiB |  00m00s
[ 8/18] Installing mpdecimal-0:4.0.1-3. 100% | 138.0 MiB/s | 282.6 KiB |  00m00s
[ 9/18] Installing expat-0:2.9.0-1.fc44 100% |  15.9 MiB/s | 391.6 KiB |  00m00s
[10/18] Installing python3-libs-0:3.14. 100% | 266.8 MiB/s |  46.2 MiB |  00m00s
[11/18] Installing python3-0:3.14.10-1. 100% |   5.0 MiB/s |  86.5 KiB |  00m00s
[12/18] Installing python3-packaging-0: 100% | 151.3 MiB/s | 619.9 KiB |  00m00s
[13/18] Installing python3-rpm-generato 100% |  81.0 MiB/s |  82.9 KiB |  00m00s
[14/18] Installing python3-iniconfig-0: 100% |  26.4 MiB/s |  54.1 KiB |  00m00s
[15/18] Installing python3-pluggy-0:1.6 100% |  53.2 MiB/s | 217.9 KiB |  00m00s
[16/18] Installing python3-pygments-0:2 100% | 161.8 MiB/s |  11.5 MiB |  00m00s
[17/18] Installing python3-pytest-0:8.4 100% | 283.5 MiB/s |  22.7 MiB |  00m00s
[18/18] Installing python3-devel-0:3.14 100% |  35.3 MiB/s |   2.0 MiB |  00m00s
Complete!
Building target platforms: aarch64
Building for target aarch64
setting SOURCE_DATE_EPOCH=1793923200
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc44.src.rpm
Updating and loading repositories:
 updates                                100% |  20.4 KiB/s |   5.3 KiB |  00m00s
 fedora                                 100% |  35.8 KiB/s |   9.3 KiB |  00m00s
 Copr repository                        100% |   8.7 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.24.0-1.fc44.noarch" is already installed.
Package "python3-devel-3.14.10-1.fc44.aarch64" is already installed.
Package "python3-pytest-8.4.2-2.fc44.noarch" is already installed.

Nothing to do.
Finish: build setup for python-pyradplan-0.8.0-1.fc44.src.rpm
Start: rpmbuild python-pyradplan-0.8.0-1.fc44.src.rpm
Building target platforms: aarch64
Building for target aarch64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%mkbuilddir): /bin/sh -e /var/tmp/rpm-tmp.CBaJfg
Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.uMEISt
+ 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.bMiHWY
+ 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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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=44
+ 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.fc44.aarch64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc44.aarch64-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.fc44.aarch64-pyproject-buildrequires
+ rm -rfv '*.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-pyradplan-0.8.0-1.fc44.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 updates                                100% |  22.8 KiB/s |   5.3 KiB |  00m00s
 fedora                                 100% |  40.0 KiB/s |   9.3 KiB |  00m00s
 Copr repository                        100% |  11.3 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.24.0-1.fc44.noarch" is already installed.
Package "python3-devel-3.14.10-1.fc44.aarch64" is already installed.
Package "python3-pytest-8.4.2-2.fc44.noarch" is already installed.
Package "python3-packaging-25.0-8.fc44.noarch" is already installed.

Package                 Arch   Version          Repository      Size
Installing:
 python3-pip            noarch 0:26.0.1-3.fc44  updates     11.5 MiB
 python3-setuptools     noarch 0:80.10.2-3.fc44 fedora       7.5 MiB
 python3-setuptools_scm noarch 0:9.2.2-5.fc44   fedora     502.5 KiB

Transaction Summary:
 Installing:         3 packages

Total size of inbound packages is 5 MiB. Need to download 5 MiB.
After this operation, 19 MiB extra will be used (install 19 MiB, remove 0 B).
[1/3] python3-setuptools_scm-0:9.2.2-5. 100% |   8.6 MiB/s | 159.4 KiB |  00m00s
[2/3] python3-setuptools-0:80.10.2-3.fc 100% |  70.2 MiB/s |   1.8 MiB |  00m00s
[3/3] python3-pip-0:26.0.1-3.fc44.noarc 100% |  91.8 MiB/s |   2.7 MiB |  00m00s
--------------------------------------------------------------------------------
[3/3] Total                             100% | 152.4 MiB/s |   4.6 MiB |  00m00s
Running transaction
[1/5] Verify package files              100% | 200.0   B/s |   3.0   B |  00m00s
[2/5] Prepare transaction               100% |  93.0   B/s |   3.0   B |  00m00s
[3/5] Installing python3-setuptools-0:8 100% | 139.1 MiB/s |   7.7 MiB |  00m00s
[4/5] Installing python3-setuptools_scm 100% |  19.0 MiB/s | 526.4 KiB |  00m00s
[5/5] Installing python3-pip-0:26.0.1-3 100% |  99.5 MiB/s |  11.7 MiB |  00m00s
Complete!
Building target platforms: aarch64
Building for target aarch64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.uqEUdV
+ 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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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=44
+ 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.fc44.aarch64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc44.aarch64-pyproject-dep-overrides
Handling setuptools>=64 from build-system.requires
Requirement satisfied: setuptools>=64
   (installed: setuptools 80.10.2)
Handling setuptools-scm>=8 from build-system.requires
Requirement satisfied: setuptools-scm>=8
   (installed: setuptools-scm 9.2.2)
toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
/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:765: 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'
toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
/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:765: 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.fc44.aarch64-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.fc44.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 updates                                100% |  15.7 KiB/s |   5.3 KiB |  00m00s
 fedora                                 100% |  27.5 KiB/s |   9.3 KiB |  00m00s
 Copr repository                        100% |   6.6 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.24.0-1.fc44.noarch" is already installed.
Package "python3-devel-3.14.10-1.fc44.aarch64" is already installed.
Package "python3-pytest-8.4.2-2.fc44.noarch" is already installed.
Package "python3-packaging-25.0-8.fc44.noarch" is already installed.
Package "python3-pip-26.0.1-3.fc44.noarch" is already installed.
Package "python3-setuptools-80.10.2-3.fc44.noarch" is already installed.
Package "python3-setuptools_scm-9.2.2-5.fc44.noarch" is already installed.

Package                        Arch    Version                           Repository      Size
Installing:
 python3-array-api-compat      noarch  0:1.15.0-1.fc44                   copr_base  506.6 KiB
 python3-array-api-extra       noarch  0:0.11.5-1.fc44                   copr_base    1.4 MiB
 python3-array-api-strict      noarch  0:2.0.1-8.fc44                    fedora     694.1 KiB
 python3-h5py                  aarch64 0:3.15.1-3.fc44                   fedora       4.5 MiB
 python3-huggingface-hub       noarch  0:1.24.0-1.fc44                   updates      6.5 MiB
 python3-ipax                  noarch  0:0.13.0-1.fc44                   copr_base    3.0 MiB
 python3-jinja2                noarch  0:3.1.6-7.fc44                    fedora       3.1 MiB
 python3-matplotlib            aarch64 0:3.10.8-2.fc44                   fedora      31.8 MiB
 python3-numpy                 aarch64 1:2.4.6-1.fc44                    updates     36.3 MiB
 python3-numpydantic           noarch  0:2.0.0-1.fc44                    copr_base  621.5 KiB
 python3-pint                  noarch  0:0.25.2-2.fc44                   fedora       3.4 MiB
 python3-pydantic              noarch  0:2.12.5-4.fc44                   updates      5.1 MiB
 python3-pydantic-settings     noarch  0:2.15.0-1.fc44                   updates    557.0 KiB
 python3-pydicom               noarch  0:3.0.2-1.fc44                    updates     13.9 MiB
 python3-pymatreader           noarch  0:1.3.1-1.fc44                    copr_base   65.9 KiB
 python3-regex                 aarch64 0:2026.9.10-1.fc44                updates      2.1 MiB
 python3-safetensors           aarch64 0:0.7.0-1.fc44                    updates    926.2 KiB
 python3-scipy                 aarch64 0:1.16.2-3.fc44                   fedora      63.3 MiB
 python3-simpleitk             aarch64 0:2.5.5-12.fc44                   copr_base   15.0 MiB
 python3-tqdm                  noarch  0:4.69.0-2.fc44                   updates    578.4 KiB
Installing dependencies:
 CharLS                        aarch64 0:2.4.3-2.fc44                    fedora     346.8 KiB
 InsightToolkit5               aarch64 0:5.4.7-24.fc44                   copr_base   32.6 MiB
 abattis-cantarell-vf-fonts    noarch  0:0.301-17.fc44                   fedora     192.7 KiB
 abseil-cpp                    aarch64 0:20260107.1-1.fc44               fedora       6.5 MiB
 armadillo12                   aarch64 0:12.8.1-1.fc44                   copr_base  146.0 KiB
 arpack                        aarch64 0:3.9.1-9.fc44                    fedora     553.6 KiB
 blosc                         aarch64 0:1.21.6-10.fc44                  fedora     129.8 KiB
 boost-container               aarch64 0:1.90.0-7.fc44                   fedora      69.3 KiB
 boost-program-options         aarch64 0:1.90.0-7.fc44                   fedora     267.1 KiB
 cairo                         aarch64 0:1.18.6-1.fc44                   updates      1.8 MiB
 cfitsio                       aarch64 0:4.6.3-2.fc44                    fedora       1.7 MiB
 dcmtk                         aarch64 0:3.7.0^20260922git8d68392-1.fc44 copr_base   43.1 MiB
 default-fonts-core-sans       noarch  0:4.3-1.fc44                      fedora      11.9 KiB
 dejavu-sans-fonts             noarch  0:2.37-29.fc44                    fedora       5.5 MiB
 double-conversion             aarch64 0:3.4.0-3.fc44                    fedora     141.1 KiB
 expatpp                       aarch64 0:0-0.39.20120624svn6.fc44        fedora      77.8 KiB
 fftw-libs-double              aarch64 0:3.3.10-17.fc44                  fedora       1.8 MiB
 fftw-libs-single              aarch64 0:3.3.10-17.fc44                  fedora       1.9 MiB
 flexiblas                     aarch64 0:3.5.0-2.fc44                    fedora      38.0 KiB
 flexiblas-netlib              aarch64 0:3.5.0-2.fc44                    fedora      14.2 MiB
 flexiblas-netlib64            aarch64 0:3.5.0-2.fc44                    fedora      14.0 MiB
 flexiblas-openblas-openmp     aarch64 0:3.5.0-2.fc44                    fedora      67.2 KiB
 flexiblas-openblas-openmp64   aarch64 0:3.5.0-2.fc44                    fedora      67.2 KiB
 fmt                           aarch64 0:11.2.0-4.fc44                   fedora     262.3 KiB
 fontconfig                    aarch64 0:2.17.0-4.fc44                   fedora       1.2 MiB
 fonts-filesystem              noarch  1:5.0.0-2.fc44                    fedora       0.0   B
 freetype                      aarch64 0:2.14.3-1.fc44                   updates    878.6 KiB
 freexl                        aarch64 0:2.0.0-20.fc44                   fedora      93.3 KiB
 fribidi                       aarch64 0:1.0.16-4.fc44                   fedora     246.0 KiB
 gdal-libs                     aarch64 0:3.12.4-1.fc44                   updates     30.3 MiB
 gdcm                          aarch64 0:3.0.24-17.fc44                  fedora       8.2 MiB
 geos                          aarch64 0:3.14.1-2.fc44                   fedora       4.0 MiB
 giflib                        aarch64 0:6.1.3-2.fc44                    updates    138.2 KiB
 glib2                         aarch64 0:2.88.4-1.fc44                   updates     15.9 MiB
 google-noto-fonts-common      noarch  0:20251201-2.fc44                 fedora      17.7 KiB
 google-noto-sans-vf-fonts     noarch  0:20251201-2.fc44                 fedora       1.4 MiB
 gpgme                         aarch64 0:2.0.1-5.fc44                    updates    761.4 KiB
 gpgmepp                       aarch64 0:2.0.1-5.fc44                    updates    458.5 KiB
 graphite2                     aarch64 0:1.3.14-20.fc44                  fedora     239.3 KiB
 harfbuzz                      aarch64 0:14.1.0-2.fc44                   updates      2.7 MiB
 hdf-libs                      aarch64 0:4.3.0-5.fc44                    fedora     722.6 KiB
 hdf5                          aarch64 0:1.14.6-7.fc44                   fedora       7.0 MiB
 imath                         aarch64 0:3.1.12-6.fc44                   fedora     387.6 KiB
 jbigkit-libs                  aarch64 0:2.1-33.fc44                     fedora     181.2 KiB
 jsoncpp                       aarch64 0:1.9.6-3.fc44                    fedora     271.7 KiB
 lcms2                         aarch64 0:2.16-7.fc44                     fedora     484.7 KiB
 libX11                        aarch64 0:1.8.13-3.fc44                   updates      1.3 MiB
 libX11-common                 noarch  0:1.8.13-3.fc44                   updates      1.1 MiB
 libXau                        aarch64 0:1.0.12-4.fc44                   fedora     120.8 KiB
 libXext                       aarch64 0:1.3.6-5.fc44                    fedora     145.7 KiB
 libXrender                    aarch64 0:0.9.12-4.fc44                   fedora      69.9 KiB
 libaec                        aarch64 0:1.1.5-1.fc44                    fedora     154.3 KiB
 libarrow                      aarch64 0:23.0.1-1.fc44                   fedora      11.4 MiB
 libarrow-acero-libs           aarch64 0:23.0.1-1.fc44                   fedora       1.3 MiB
 libarrow-compute-libs         aarch64 0:23.0.1-1.fc44                   fedora      11.0 MiB
 libarrow-dataset-libs         aarch64 0:23.0.1-1.fc44                   fedora       1.4 MiB
 libarrow-doc                  noarch  0:23.0.1-1.fc44                   fedora     119.1 KiB
 libdc1394                     aarch64 0:2.2.7-9.fc44                    fedora     378.7 KiB
 libdeflate                    aarch64 0:1.26-1.fc44                     updates    163.9 KiB
 libgeotiff                    aarch64 0:1.7.4-5.fc44                    fedora     517.6 KiB
 libgfortran                   aarch64 0:16.2.1-3.fc44                   updates      1.7 MiB
 libgta                        aarch64 0:1.2.1-17.fc44                   fedora      94.0 KiB
 libharu                       aarch64 0:2.4.5-2.fc44                    fedora       1.7 MiB
 libicu                        aarch64 0:77.1-3.fc44                     updates     36.5 MiB
 libimagequant                 aarch64 0:4.1.0-2.fc44                    fedora     672.1 KiB
 libjpeg-turbo                 aarch64 0:3.1.3-1.fc44                    fedora     677.7 KiB
 libkml                        aarch64 0:1.3.0-58.fc44                   updates      1.2 MiB
 liblerc                       aarch64 0:4.3.0-1.fc44                    updates    547.7 KiB
 liblzf                        aarch64 0:3.6-34.fc44                     fedora     153.3 KiB
 libminc                       aarch64 0:2.4.03-31.fc44                  updates    668.9 KiB
 libogg                        aarch64 2:1.3.6-2.fc44                    fedora      77.7 KiB
 liborc2                       aarch64 0:2.2.2-2.fc44                    fedora       1.9 MiB
 libpng                        aarch64 2:1.6.59-1.fc44                   updates    269.8 KiB
 libpq                         aarch64 0:18.0-4.fc44                     fedora       1.0 MiB
 libqhull_r                    aarch64 1:8.0.2-8.fc44                    fedora     455.3 KiB
 libraqm                       aarch64 0:0.10.1-4.fc44                   fedora      68.5 KiB
 libraw1394                    aarch64 0:2.1.2-25.fc44                   fedora     310.2 KiB
 librttopo                     aarch64 0:1.1.0-19.fc44                   fedora     544.5 KiB
 libspatialite                 aarch64 0:5.1.0-12.fc44                   fedora      15.0 MiB
 libtheora                     aarch64 1:1.1.1-41.fc44                   fedora     468.3 KiB
 libtiff                       aarch64 0:4.7.2-3.fc44                    updates    659.4 KiB
 libtirpc                      aarch64 0:1.3.8-0.fc44                    updates    212.0 KiB
 libwebp                       aarch64 0:1.6.0-3.fc44                    fedora     816.8 KiB
 libxcb                        aarch64 0:1.17.0-7.fc44                   fedora       2.1 MiB
 libyaml                       aarch64 0:0.2.5-18.fc44                   fedora     134.3 KiB
 libzip                        aarch64 0:1.11.4-3.fc44                   fedora     135.6 KiB
 lzo                           aarch64 0:2.10-16.fc44                    fedora     162.8 KiB
 mariadb-connector-c           aarch64 0:3.4.9-2.fc44                    updates    993.9 KiB
 mariadb-connector-c-config    noarch  0:3.4.9-2.fc44                    updates     26.4 KiB
 minizip-ng-compat             aarch64 0:4.1.0-1.fc44                    fedora     198.4 KiB
 muParser                      aarch64 0:2.3.5-5.fc44                    fedora     552.6 KiB
 netcdf                        aarch64 0:4.9.3-2.fc44                    fedora       3.1 MiB
 nspr                          aarch64 0:4.39.0-7.fc44                   updates    419.4 KiB
 nss                           aarch64 0:3.131.0-1.fc44                  updates      2.0 MiB
 nss-softokn                   aarch64 0:3.131.0-1.fc44                  updates      2.3 MiB
 nss-softokn-freebl            aarch64 0:3.131.0-1.fc44                  updates      1.0 MiB
 nss-sysinit                   aarch64 0:3.131.0-1.fc44                  updates     70.0 KiB
 nss-util                      aarch64 0:3.131.0-1.fc44                  updates    216.8 KiB
 openblas                      aarch64 0:0.3.29-2.fc43                   fedora     111.7 KiB
 openblas-openmp               aarch64 0:0.3.29-2.fc43                   fedora      20.4 MiB
 openblas-openmp64             aarch64 0:0.3.29-2.fc43                   fedora      20.2 MiB
 openexr-libs                  aarch64 0:3.2.4-7.fc44                    fedora       6.6 MiB
 openjpeg                      aarch64 0:2.5.4-5.fc44                    updates    412.3 KiB
 parquet-libs                  aarch64 0:23.0.1-1.fc44                   fedora       3.5 MiB
 pixman                        aarch64 0:0.46.2-3.fc44                   fedora     654.3 KiB
 poppler                       aarch64 0:26.01.0-3.fc44                  fedora       3.6 MiB
 poppler-data                  noarch  0:0.4.11-11.fc44                  fedora      12.3 MiB
 proj                          aarch64 0:9.8.1-1.fc44                    updates      4.3 MiB
 proj-data                     noarch  0:9.8.1-1.fc44                    updates     10.0 MiB
 protobuf                      aarch64 0:3.19.6-20.fc44                  fedora       3.1 MiB
 pugixml                       aarch64 0:1.16-1.fc44                     updates    265.3 KiB
 python3-annotated-types       noarch  0:0.7.0-9.fc44                    fedora      85.1 KiB
 python3-anyio                 noarch  0:4.13.0-2.fc44                   updates      1.5 MiB
 python3-cached_property       noarch  0:2.0.1-7.fc44                    fedora      35.2 KiB
 python3-cairo                 aarch64 0:1.28.0-5.fc44                   fedora     516.6 KiB
 python3-certifi               noarch  0:2026.01.04-1.fc44               fedora       7.4 KiB
 python3-charset-normalizer    noarch  0:3.4.4-2.fc44                    fedora     356.2 KiB
 python3-click                 noarch  1:8.3.3-1.fc44                    updates      1.2 MiB
 python3-contourpy             aarch64 0:1.4.0-1.fc44                    updates    860.5 KiB
 python3-cycler                noarch  0:0.11.0-20.fc44                  fedora      37.8 KiB
 python3-dateutil              noarch  1:2.9.0.post0-7.fc44              fedora     878.0 KiB
 python3-dotenv                noarch  0:1.2.2-1.fc44                    updates    149.4 KiB
 python3-filelock              noarch  0:3.15.4-11.fc44                  fedora      97.5 KiB
 python3-flexcache             noarch  0:0.3-11.fc44                     fedora      57.0 KiB
 python3-flexparser            noarch  0:0.4-7.fc44                      fedora     263.5 KiB
 python3-fonttools             aarch64 0:4.62.1-1.fc44                   updates     19.4 MiB
 python3-fsspec                noarch  0:2026.9.0-1.fc44                 updates      2.0 MiB
 python3-h11                   noarch  0:0.16.0-6.fc44                   fedora     279.9 KiB
 python3-httpcore              noarch  0:1.0.9-6.fc44                    fedora     853.9 KiB
 python3-httpx                 noarch  0:0.28.1-11.fc44                  fedora     992.1 KiB
 python3-idna                  noarch  0:3.18-1.fc44                     updates    522.3 KiB
 python3-kiwisolver            aarch64 0:1.5.1-1.fc44                    updates    161.1 KiB
 python3-markupsafe            aarch64 0:3.0.2-7.fc44                    fedora     117.4 KiB
 python3-matplotlib-data-fonts aarch64 0:3.10.8-2.fc44                   fedora       7.9 MiB
 python3-numpy-f2py            aarch64 1:2.4.6-1.fc44                    updates      2.1 MiB
 python3-olefile               noarch  0:0.47-13.fc44                    fedora     346.5 KiB
 python3-pillow                aarch64 0:12.3.0-7.fc44                   updates      4.6 MiB
 python3-platformdirs          noarch  0:4.9.1-1.fc44                    fedora     236.5 KiB
 python3-pooch                 noarch  0:1.9.0-1.fc44                    fedora     657.1 KiB
 python3-pydantic-core         aarch64 0:2.41.5-11.fc44                  updates      4.9 MiB
 python3-pyparsing             noarch  0:3.1.2-15.fc44                   fedora       1.0 MiB
 python3-pyyaml                aarch64 0:6.0.3-3.fc44                    fedora     797.1 KiB
 python3-requests              noarch  0:2.33.1-1.fc44                   updates    477.3 KiB
 python3-six                   noarch  0:1.17.0-8.fc44                   fedora     118.0 KiB
 python3-typing-extensions     noarch  0:4.16.0-4.fc44                   updates    552.0 KiB
 python3-typing-inspection     noarch  0:0.4.4-1.fc44                    updates     93.4 KiB
 python3-urllib3               noarch  0:2.8.0-3.fc44                    updates      1.2 MiB
 python3-xmltodict             noarch  0:0.14.2-5.fc44                   fedora      56.0 KiB
 re2                           aarch64 2:20251105-4.fc44                 fedora     598.8 KiB
 rply                          aarch64 0:1.1.4-11.fc44                   updates    315.7 KiB
 simpleitk                     aarch64 0:2.5.5-12.fc44                   copr_base  278.8 MiB
 snappy                        aarch64 0:1.2.2-4.fc44                    fedora      83.6 KiB
 thrift                        aarch64 0:0.20.0-9.fc44                   fedora       4.8 MiB
 unixODBC                      aarch64 0:2.3.14-3.fc44                   fedora       1.3 MiB
 uriparser                     aarch64 0:1.0.3-1.fc44                    updates    243.3 KiB
 utf8proc                      aarch64 0:2.11.3-2.fc44                   fedora     413.5 KiB
 viskores                      aarch64 0:1.2.0-3.fc44                    copr_base  117.8 MiB
 vtk                           aarch64 0:9.7.0-6.fc44                    copr_base  242.4 MiB
 vxl                           aarch64 0:3.5.0-20.fc44                   updates     56.3 MiB
 xerces-c                      aarch64 0:3.3.0-9.fc44                    fedora       3.8 MiB
 xml-common                    noarch  0:0.6.3-68.fc44                   fedora      78.4 KiB

Transaction Summary:
 Installing:       181 packages

Total size of inbound packages is 254 MiB. Need to download 254 MiB.
After this operation, 1 GiB extra will be used (install 1 GiB, remove 0 B).
[  1/181] python3-array-api-strict-0:2. 100% |   9.4 MiB/s | 164.1 KiB |  00m00s
[  2/181] python3-h5py-0:3.15.1-3.fc44. 100% |  94.9 MiB/s |   1.0 MiB |  00m00s
[  3/181] python3-array-api-extra-0:0.1 100% | 985.9 KiB/s | 272.1 KiB |  00m00s
[  4/181] python3-jinja2-0:3.1.6-7.fc44 100% |  71.9 MiB/s | 515.5 KiB |  00m00s
[  5/181] python3-array-api-compat-0:1. 100% | 338.3 KiB/s | 123.1 KiB |  00m00s
[  6/181] python3-ipax-0:0.13.0-1.fc44. 100% |   1.6 MiB/s | 574.2 KiB |  00m00s
[  7/181] python3-pint-0:0.25.2-2.fc44. 100% |  48.2 MiB/s | 740.8 KiB |  00m00s
[  8/181] python3-matplotlib-0:3.10.8-2 100% |  43.9 MiB/s |   7.9 MiB |  00m00s
[  9/181] python3-numpydantic-0:2.0.0-1 100% | 754.9 KiB/s | 186.5 KiB |  00m00s
[ 10/181] python3-pydantic-0:2.12.5-4.f 100% |  66.1 MiB/s |   1.0 MiB |  00m00s
[ 11/181] python3-scipy-0:1.16.2-3.fc44 100% |  61.0 MiB/s |  15.2 MiB |  00m00s
[ 12/181] python3-pydantic-settings-0:2 100% |  10.0 MiB/s | 173.8 KiB |  00m00s
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Warning: skipped OpenPGP checks for 12 packages from repository: copr_base
Complete!
Building target platforms: aarch64
Building for target aarch64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.PGHMMx
+ 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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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=44
+ 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.fc44.aarch64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc44.aarch64-pyproject-dep-overrides
Handling setuptools>=64 from build-system.requires
Requirement satisfied: setuptools>=64
   (installed: setuptools 80.10.2)
Handling setuptools-scm>=8 from build-system.requires
Requirement satisfied: setuptools-scm>=8
   (installed: setuptools-scm 9.2.2)
toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
/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:765: 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'
toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
/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:765: 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 2.4.6)
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 3.15.1)
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 3.10.8)
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 4.69.0)
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.fc44.aarch64-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.fc44.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 updates                                100% |  24.7 KiB/s |   5.3 KiB |  00m00s
 fedora                                 100% |  43.3 KiB/s |   9.3 KiB |  00m00s
 Copr repository                        100% |   8.3 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.24.0-1.fc44.noarch" is already installed.
Package "python3-devel-3.14.10-1.fc44.aarch64" is already installed.
Package "python3-pytest-8.4.2-2.fc44.noarch" is already installed.
Package "python3-array-api-compat-1.15.0-1.fc44.noarch" is already installed.
Package "python3-array-api-extra-0.11.5-1.fc44.noarch" is already installed.
Package "python3-array-api-strict-2.0.1-8.fc44.noarch" is already installed.
Package "python3-h5py-3.15.1-3.fc44.aarch64" is already installed.
Package "python3-huggingface-hub-1.24.0-1.fc44.noarch" is already installed.
Package "python3-ipax-0.13.0-1.fc44.noarch" is already installed.
Package "python3-jinja2-3.1.6-7.fc44.noarch" is already installed.
Package "python3-matplotlib-3.10.8-2.fc44.aarch64" is already installed.
Package "python3-numpy-1:2.4.6-1.fc44.aarch64" is already installed.
Package "python3-numpydantic-2.0.0-1.fc44.noarch" is already installed.
Package "python3-packaging-25.0-8.fc44.noarch" is already installed.
Package "python3-pint-0.25.2-2.fc44.noarch" is already installed.
Package "python3-pip-26.0.1-3.fc44.noarch" is already installed.
Package "python3-pydicom-3.0.2-1.fc44.noarch" is already installed.
Package "python3-pymatreader-1.3.1-1.fc44.noarch" is already installed.
Package "python3-regex-2026.9.10-1.fc44.aarch64" is already installed.
Nothing to do.
Package "python3-safetensors-0.7.0-1.fc44.aarch64" is already installed.
Package "python3-scipy-1.16.2-3.fc44.aarch64" is already installed.
Package "python3-setuptools-80.10.2-3.fc44.noarch" is already installed.
Package "python3-setuptools_scm-9.2.2-5.fc44.noarch" is already installed.
Package "python3-simpleitk-2.5.5-12.fc44.aarch64" is already installed.
Package "python3-tqdm-4.69.0-2.fc44.noarch" is already installed.

Building target platforms: aarch64
Building for target aarch64
setting SOURCE_DATE_EPOCH=1793923200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.CUFj9A
+ 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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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=44
+ 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.fc44.aarch64-pyproject-buildrequires --dep-overrides-file /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc44.aarch64-pyproject-dep-overrides
Handling setuptools>=64 from build-system.requires
Requirement satisfied: setuptools>=64
   (installed: setuptools 80.10.2)
Handling setuptools-scm>=8 from build-system.requires
Requirement satisfied: setuptools-scm>=8
   (installed: setuptools-scm 9.2.2)
toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
/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:765: 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'
toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
/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:765: 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 2.4.6)
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 3.15.1)
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 3.10.8)
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 4.69.0)
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.fc44.aarch64-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.N9MhiU
+ 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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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 ./.
  Preparing metadata (pyproject.toml): started
  Running command Preparing metadata (pyproject.toml)
  toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
  /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:765: 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-_xj5nvr3/pyRadPlan.egg-info
  writing /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/pyRadPlan.egg-info/PKG-INFO
  writing dependency_links to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/pyRadPlan.egg-info/dependency_links.txt
  writing entry points to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/pyRadPlan.egg-info/entry_points.txt
  writing requirements to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/pyRadPlan.egg-info/requires.txt
  writing top-level names to /builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/pyRadPlan.egg-info/top_level.txt
  writing manifest file '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/pyRadPlan.egg-info/SOURCES.txt'
  reading manifest file '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/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-_xj5nvr3/pyRadPlan.egg-info/SOURCES.txt'
  creating '/builddir/build/BUILD/python-pyradplan-0.8.0-build/.pyproject-builddir/pip-modern-metadata-_xj5nvr3/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)
  toml section missing PosixPath('pyproject.toml') does not contain a tool.setuptools_scm section
  /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:765: 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-aarch64/wheel
  running install
  running install_lib
  creating build/bdist.linux-aarch64/wheel
  creating build/bdist.linux-aarch64/wheel/pyRadPlan
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/ai_models
  copying build/lib/pyRadPlan/data/ai_models/model_config.template.json -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/ai_models
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/protons_Generic.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/photons_Generic.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/oxygen_Generic.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/helium_Generic.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/carbon_Generic.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/VHEE_Generic.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/machines
  copying build/lib/pyRadPlan/data/machines/VHEE_Focused.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/machines
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/phantoms
  copying build/lib/pyRadPlan/data/phantoms/TG119.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/phantoms
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/physics
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/physics/stopping
  copying build/lib/pyRadPlan/data/physics/stopping/SPtable.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/physics/stopping
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/rbe
  copying build/lib/pyRadPlan/data/rbe/RBEtable_LEMI_Scholz06_AX01_BX005.mat -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/rbe
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/topas
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/topas/input
  creating build/bdist.linux-aarch64/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-aarch64/wheel/./pyRadPlan/data/topas/input/beam_setup
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/topas/input/material_converter
  copying build/lib/pyRadPlan/data/topas/input/material_converter/TOPAS_schneider_converter.txt.in -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/topas/input/material_converter
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/topas/input/scorer
  copying build/lib/pyRadPlan/data/topas/input/scorer/TOPAS_subscorer_LET.txt.in -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/topas/input/scorer
  copying build/lib/pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToWater.txt.in -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/topas/input/scorer
  copying build/lib/pyRadPlan/data/topas/input/scorer/TOPAS_scorer_doseToMedium.txt.in -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/topas/input/scorer
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/data/topas/input/template
  copying build/lib/pyRadPlan/data/topas/input/template/pyRadPlan_run_template.txt -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/topas/input/template
  copying build/lib/pyRadPlan/data/topas/input/template/patient_template.txt -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/topas/input/template
  copying build/lib/pyRadPlan/data/topas/input/template/beamSetup_template.txt -> build/bdist.linux-aarch64/wheel/./pyRadPlan/data/topas/input/template
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/ai
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_cst_agents.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_models.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_plan_agents.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_settings.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/agents
  copying build/lib/pyRadPlan/ai/agents/_usage.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/agents
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_load_model.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_preprocessor.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_registry.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_resolve.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/modelhub/_settings.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai/modelhub
  copying build/lib/pyRadPlan/ai/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ai
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_dvh.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_dvh_plot.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_qi.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/analysis
  copying build/lib/pyRadPlan/analysis/_units.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/analysis
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/biology
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/biology/models
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/const_rbe.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/kernel_based_lq_model.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/let_based_lq_models.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/lq_models.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/tabulated_rbe_models.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/_models/zstar_based_lq_model.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models/_models
  copying build/lib/pyRadPlan/biology/models/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_engine.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_evaluator.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_influence.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_kernel_sets.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_options.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_resolution.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  copying build/lib/pyRadPlan/biology/models/_tissue_lookup.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/models
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_conversion.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_presets.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_provenance.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_reference.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/tissues/_sources.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology/tissues
  copying build/lib/pyRadPlan/biology/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/biology
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/core
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_backends.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_image_interpolation.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_interpolation.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_interpolation1d.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/_reductions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/compat.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/helpers.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/jittable.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/xp_utils/typing.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core/xp_utils
  copying build/lib/pyRadPlan/core/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_base_data.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_configurable.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_exceptions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_grids.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_paths.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_progress.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/_units.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/datamodel.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/np2sitk.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  copying build/lib/pyRadPlan/core/resample.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/core
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/cst
  copying build/lib/pyRadPlan/cst/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/cst
  copying build/lib/pyRadPlan/cst/_cst.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/cst
  copying build/lib/pyRadPlan/cst/_voi.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/cst
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/ct
  copying build/lib/pyRadPlan/ct/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ct
  copying build/lib/pyRadPlan/ct/_ct.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ct
  copying build/lib/pyRadPlan/ct/_hlut.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/ct
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/dij
  copying build/lib/pyRadPlan/dij/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dij
  copying build/lib/pyRadPlan/dij/_compose_beam_dijs.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dij
  copying build/lib/pyRadPlan/dij/_dij.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dij
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/dose
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/dose/engines
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/dose/engines/kernels
  copying build/lib/pyRadPlan/dose/engines/kernels/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines/kernels
  copying build/lib/pyRadPlan/dose/engines/kernels/_calc_geo_dists_kernel.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines/kernels
  copying build/lib/pyRadPlan/dose/engines/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base_montecarlo.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base_pencilbeam.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_base_pencilbeam_particle.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_fredmc.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_hongpb.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_particle_cutoff.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_sparse_output.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_svdpb.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/engines/_topasmc.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose/engines
  copying build/lib/pyRadPlan/dose/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose
  copying build/lib/pyRadPlan/dose/_calc_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/dose
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/geometry
  copying build/lib/pyRadPlan/geometry/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/geometry
  copying build/lib/pyRadPlan/geometry/lps.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/geometry
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/assets
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/pyradplan_logo_skull_square.svg -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/pyradplan_logo_full_landscape.svg -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/dkfz_logo_white.svg -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/logos/dkfz_logo_blue.svg -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/assets/logos
  copying build/lib/pyRadPlan/gui/assets/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/assets
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_base_data.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_edit.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_file.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_help.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_settings.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/menus
  copying build/lib/pyRadPlan/gui/menus/_view.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/menus
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/widgets
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/widgets/ai
  copying build/lib/pyRadPlan/gui/widgets/ai/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/ai
  copying build/lib/pyRadPlan/gui/widgets/ai/_ai_task_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/ai
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_dvh.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_gamma.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_qi.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/analysis
  copying build/lib/pyRadPlan/gui/widgets/analysis/_qi_point_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/analysis
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/widgets/optimization
  copying build/lib/pyRadPlan/gui/widgets/optimization/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/optimization
  copying build/lib/pyRadPlan/gui/widgets/optimization/_optimization_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/optimization
  copying build/lib/pyRadPlan/gui/widgets/optimization/_status_window.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/optimization
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/_optimization_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/_plan_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/plan
  copying build/lib/pyRadPlan/gui/widgets/plan/_tissue_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/plan
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/_labels.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/_orientation_marker.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/_save_result_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/quantity_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/viewer_options_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/visualization_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  copying build/lib/pyRadPlan/gui/widgets/result/vois_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/result
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_binary_import_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_dicom_import_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_image_import_dialog.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/workflow/_workflow_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets/workflow
  copying build/lib/pyRadPlan/gui/widgets/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_analysis_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_config_form.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_display_names.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_info_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_log_console.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_logo_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_plot_theme.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_quantity_selector.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_result_widget.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_tissue_params.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  copying build/lib/pyRadPlan/gui/widgets/_ui_mode.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/widgets
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_analysis_win.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_main_win.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_result_win.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/windows/_splash_win.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui/windows
  copying build/lib/pyRadPlan/gui/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui
  copying build/lib/pyRadPlan/gui/apps.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui
  copying build/lib/pyRadPlan/gui/workspace.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/gui
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io
  copying build/lib/pyRadPlan/io/.gitkeep -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/base
  copying build/lib/pyRadPlan/io/base/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/base
  copying build/lib/pyRadPlan/io/base/_exporter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/base
  copying build/lib/pyRadPlan/io/base/_importer.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/base
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_common.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_cst.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_ct.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_export_seg.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_exporter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_handler.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_helpers.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_import_cst.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_import_ct.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_import_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  copying build/lib/pyRadPlan/io/dicom/_importer.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/dicom
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_exporter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_handler.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_importer.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/matlab
  copying build/lib/pyRadPlan/io/matlab/_matfile.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/matlab
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_exporter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_handler.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_importer.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/npz
  copying build/lib/pyRadPlan/io/npz/_serialize.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/npz
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/_exporter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/_handler.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/pickle
  copying build/lib/pyRadPlan/io/pickle/_importer.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/pickle
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/sitk_based
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/_exporter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/_importer.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based/base
  copying build/lib/pyRadPlan/io/sitk_based/base/_serialize.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based/base
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/sitk_based/meta_image
  copying build/lib/pyRadPlan/io/sitk_based/meta_image/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based/meta_image
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/sitk_based/nifti
  copying build/lib/pyRadPlan/io/sitk_based/nifti/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based/nifti
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/io/sitk_based/nrrd
  copying build/lib/pyRadPlan/io/sitk_based/nrrd/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based/nrrd
  copying build/lib/pyRadPlan/io/sitk_based/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based
  copying build/lib/pyRadPlan/io/sitk_based/_binary_import.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io/sitk_based
  copying build/lib/pyRadPlan/io/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io
  copying build/lib/pyRadPlan/io/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io
  copying build/lib/pyRadPlan/io/_helpers.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io
  copying build/lib/pyRadPlan/io/_load_save.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/io
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/machines
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_beam_modifier.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_external_beam.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/base
  copying build/lib/pyRadPlan/machines/base/_internal_beam.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/base
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/machines/particles
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/_ions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/kernel/_vhee.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles/kernel
  copying build/lib/pyRadPlan/machines/particles/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_cutoff.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_emittance.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_energy.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_focus.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_beam_fragment_spectrum.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_ions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  copying build/lib/pyRadPlan/machines/particles/_vhee.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/particles
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/machines/photons
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  copying build/lib/pyRadPlan/machines/photons/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/_calculate_machine_scale.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/_linac.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/photons/_svd_kernel.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines/photons
  copying build/lib/pyRadPlan/machines/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines
  copying build/lib/pyRadPlan/machines/_load.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines
  copying build/lib/pyRadPlan/machines/_validate.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/machines
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/.gitkeep -> build/bdist.linux-aarch64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/_engine.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/_matlab.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/matRad
  copying build/lib/pyRadPlan/matRad/_octave.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/matRad
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/optimization
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_eud.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_max_dvh.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_mean.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_min_dvh.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_objective.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_dev.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_mimicking.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_over.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_squared_under.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_std.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  copying build/lib/pyRadPlan/optimization/objectives/_variance.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/objectives
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_nonlin_fluence.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_optiprob.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/problems
  copying build/lib/pyRadPlan/optimization/problems/_simple_least_squares.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/problems
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_composite_worst_case.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_expected_quantity.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_expected_value.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_none.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_objective_worst_case.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_smooth_max.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_terms.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_utils.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  copying build/lib/pyRadPlan/optimization/robustness/_voxel_worst_case.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/robustness
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_base_solvers.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_ipax_solver.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_ipopt.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/solvers/_scipy_solver.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization/solvers
  copying build/lib/pyRadPlan/optimization/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization
  copying build/lib/pyRadPlan/optimization/_fluence_optimization.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/optimization
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/plan
  copying build/lib/pyRadPlan/plan/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/plan
  copying build/lib/pyRadPlan/plan/_plans.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/plan
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_alpha_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_effect.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_let.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_let_x_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_rbe_x_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_resolver.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  copying build/lib/pyRadPlan/quantities/_sqrt_beta_dose.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/quantities
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_kernels.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_kernels_numba.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/raytracer
  copying build/lib/pyRadPlan/raytracer/_siddon.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/raytracer
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_dimensions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_distributions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_explicit.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_gridded.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_importance.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_legacy.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_nominal.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_random.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_set.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  copying build/lib/pyRadPlan/scenarios/_worst_case.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/scenarios
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/stf
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/stf/hlutLibrary
  copying build/lib/pyRadPlan/stf/hlutLibrary/matRad_default.hlut -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/hlutLibrary
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_base.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_externalbeam.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_factory.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_ions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_photons.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/generators/_vhee.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf/generators
  copying build/lib/pyRadPlan/stf/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_beam.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_beamlet.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_exceptions.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_generate_stf.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_presets.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_rangeshifter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_ray.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  copying build/lib/pyRadPlan/stf/_steeringinformation.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/stf
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/util
  copying build/lib/pyRadPlan/util/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/_jupyter.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/helpers.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/keyboard_listener.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/logging_utils.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/util
  copying build/lib/pyRadPlan/util/openmp.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/util
  creating build/bdist.linux-aarch64/wheel/pyRadPlan/visualization
  copying build/lib/pyRadPlan/visualization/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/visualization
  copying build/lib/pyRadPlan/visualization/_plot_multiple_slices.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/visualization
  copying build/lib/pyRadPlan/visualization/_plot_slice.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan/visualization
  copying build/lib/pyRadPlan/__init__.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan
  copying build/lib/pyRadPlan/_lazy.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan
  copying build/lib/pyRadPlan/_settings.py -> build/bdist.linux-aarch64/wheel/./pyRadPlan
  running install_egg_info
  Copying pyRadPlan.egg-info to build/bdist.linux-aarch64/wheel/./pyRadPlan-0.8.0-py3.14.egg-info
  running install_scripts
  creating build/bdist.linux-aarch64/wheel/pyradplan-0.8.0.dist-info/WHEEL
  creating '/builddir/.cache/pip/wheels/bf/c6/d9/519a10de0a2de160c5e3070733236db255354cd2b9dc1b2561/tmpo33wq5zx/.tmp-9wdfhmv3/pyradplan-0.8.0-py3-none-any.whl' and adding 'build/bdist.linux-aarch64/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-aarch64/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=32327552 sha256=3e87038af4e10eba3cd2e519fe3afd87c2215ee4c1b90cffccaed142619c7f36
  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.4iqfBb
+ 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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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
++ ls /builddir/build/BUILD/python-pyradplan-0.8.0-build/pyproject-wheeldir/pyradplan-0.8.0-py3-none-any.whl
++ xargs basename --multiple
++ 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 26.0.1 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.fc44.aarch64-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.fc44.aarch64-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.fc44.aarch64-pyproject-dep-overrides ']'
++ wc -l /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc44.aarch64-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.fc44.aarch64-pyproject-files --output-modules /builddir/build/BUILD/python-pyradplan-0.8.0-build/python-pyradplan-0.8.0-1.fc44.aarch64-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.fc44.aarch64-pyproject-record --prefix /usr pyRadPlan
+ /usr/lib/rpm/check-buildroot
+ /usr/lib/rpm/redhat/brp-ldconfig
+ COMPRESS='gzip -9 -n'
+ COMPRESS_EXT=.gz
+ /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-det --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__/__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/util/__pycache__/keyboard_listener.cpython-314.opt-1.pyc: rewriting with normalized contents
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/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__/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/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/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/__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/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/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/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__/_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__/_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__/_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__/_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__/_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__/_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/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/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/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/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/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/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/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__/_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/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__/_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/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__/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__/_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/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/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__/_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__/_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__/__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__/_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__/_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__/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__/_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__/_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__/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/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/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/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/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/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/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/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__/_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__/_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/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/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/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__/_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__/_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/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__/_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/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__/_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/__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__/_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__/_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/_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/_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__/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/__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__/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/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.cpython-314.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/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/__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__/_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/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/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/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__/__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__/_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__/_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__/_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__/__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
+ /usr/bin/linkdupes --brp /builddir/build/BUILD/python-pyradplan-0.8.0-build/BUILDROOT/usr
Scanned 140 directories and 972 files,
    considered 972 files, read 57 files, linked 1 files, 0 errors
    sum of sizes of linked files: 0 bytes
Executing(%check): /bin/sh -e /var/tmp/rpm-tmp.KtJa7r
+ 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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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.fc44.aarch64-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.fc44.aarch64-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  -mbranch-protection=standard -fasynchronous-unwind-tables -fstack-clash-protection -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 3.14.10, pytest-8.4.2, 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 0xffff8c8f7390>
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 0xffff8cd8b8a0>
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 0xffff8d863150>

    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 0xffff8a37d6d0>

    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 0xffff8811c7d0>

    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 0xffff8a33d6d0>

    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 0xffff8d725250>

    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 0xffff8cd3bcb0>
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 0xffff8cd3a270>
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 0xffff8a35c7c0>

    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 0xffffac546510>
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 0xffffb8d75450>
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 0xffff8cd69f30>
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 0xffff8cd68d60>
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 0xffff8a307650>
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 0xffff8a35ee00>

    @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 0xffff8a35f790>

    @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 0xffff88168e50>

    @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 0xffff88123b50>

    @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 0xffff83750b90>

    @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 0xffff821367b0>

    @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 0xffff82924590>

    @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 0xffff82165630>

    @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 0xffff82019370>

    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 0xffff8292e040>

    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 0xffff825494e0>

    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 0xffffb8d75810>
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0xffff829f3070>
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 0xffffb8d75590>
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0xffff82a9c910>
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 0xffff821adef0>

    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 0xffff821afe00>

    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 0xffff825875d0>

    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 0xffffac763db0>((15.0, 10.0, 5.0), (10.0, 15.0, 5.0))
E        +    where <function allclose at 0xffffac763db0> = 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 0xffff820de2f0> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff820de2f0> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff820de2f0> >, 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 0xffff81f53d30> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff81f53d30> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff81f53d30> >, 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 0xffffac763db0>(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 0xffffac763db0> = 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 0xffffac763db0>((10.0, 15.0, 5.0), (15.0, 10.0, 5.0))
E        +    where <function allclose at 0xffffac763db0> = 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 0xffff8227fcb0> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff8227fcb0> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff8227fcb0> >, 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 0xffff82203df0> >.GetSpacing
E        +        where <SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff82203df0> > = CT(cube_hu=<SimpleITK.SimpleITK.Image; proxy of <Swig Object of type 'itk::simple::Image *' at 0xffff82203df0> >, 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 0xffffac763db0>(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 0xffffac763db0> = 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 0xffff82ab70f0>()
E        +        where <built-in method max of numpy.ndarray object at 0xffff82ab70f0> = 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 0xffff81af81a0>

    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 0xffff8d2a3a00>, 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 0xffff8d2a3a00>, 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 0xffff8d2a3a00>, 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 0xffff8d2a3ab0>

    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 0xffff824fc7d0>

    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 0xffff7b7c3930>

    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 0xffff80909e50>

    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 0xffff8090aad0>

    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 0xffff7b7eccd0>

    @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 0xffff7b7ec190>

    @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([ 9.238581 , 10.549352 , 11.840601 ,
       10.603649 , 11.9677725,  5.453381 ,
       12.007432 , 10.00074  ,  ...  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([10.521754 ,  7.7439156, 11.06683  ,
       10.885352 , 11.675577 , 15.50958  ,
        7.965419 ,  9.880358 , 1... 10.343923 ,
        9.865118 ,  6.355603 , 12.273873 ,
       12.153697 , 10.404238 ], 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 ,  6.6663136,  9.0304165,
        7.7798643,  6.5125647, 1... 11.938342 ,
       11.604478 , 10.413534 ,  8.711118 ,
        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([11.840265 , 12.323431 ,  8.011889 ,
       12.554123 ,  8.772327 , 10.576969 ,
        7.7545686,  7.588301 , 1... 12.832273 ,
        9.58289  ,  8.894093 , 12.701789 ,
        8.755135 , 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([ 9.238581 , 10.549352 , 11.840601 ,
       10.603649 , 11.9677725,  5.453381 ,
       12.007432 , 10.00074  ,  ...  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([92.,  2.,  2.,  2.,  2.,  2.,  2.,
        2.,  2., 92.,  2., 92.,  2.,  2.,
        2.,  2.,  2.,  2.,  2.,  2....,  2.,
        2.,  2.,  2., 92.,  2.,  2.,  2.,
       92.,  2.,  2., 92.,  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      , 0.2      ,
       0.2      , 0.2      , 0.2      ,
       0.2      , 0.2      , 0.2      ...     , 4.7914815,
       0.2      , 4.7914815, 4.7914815,
       4.7914815, 4.7914815], 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 0xffff6a6c6670> >, 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 0xffff6ac6ac30> >, 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 0xffff6a867530> >, 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 0xffff7abd0af0> >

    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 0xffff6a7710b0> >

    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 0xffff6a7f8ef0> >

    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 0xffff7ab888f0> >

    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 0xffff7ac21eb0> >
monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0xffff8c80f0e0>
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 0xffff7af4fc40>
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/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:1529: 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, 11559 warnings, 28 errors in 152.07s (0:02:32) =
error: Bad exit status from /var/tmp/rpm-tmp.KtJa7r (%check)
    Bad exit status from /var/tmp/rpm-tmp.KtJa7r (%check)

RPM build errors:
Finish: rpmbuild python-pyradplan-0.8.0-1.fc44.src.rpm
Finish: build phase for python-pyradplan-0.8.0-1.fc44.src.rpm
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-44-aarch64-1794040424.031981/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.fc44.src.rpm) Config(fedora-44-aarch64) 3 minutes 14 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 310bb156f4bc4c24b2e8920eaa022d49 -D /var/lib/mock/fedora-44-aarch64-1794040424.031981/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 aarch64 /builddir/build/originals/python-pyradplan.spec'

Copr build error: Build failed