I: pbuilder: network access will be disabled during build I: Current time: Thu Jan 9 21:33:33 -12 2025 I: pbuilder-time-stamp: 1736501613 I: Building the build Environment I: extracting base tarball [/var/cache/pbuilder/unstable-reproducible-base.tgz] I: copying local configuration W: --override-config is not set; not updating apt.conf Read the manpage for details. I: mounting /proc filesystem I: mounting /sys filesystem I: creating /{dev,run}/shm I: mounting /dev/pts filesystem I: redirecting /dev/ptmx to /dev/pts/ptmx I: policy-rc.d already exists I: Copying source file I: copying [pyusid_0.0.12-2.dsc] I: copying [./pyusid_0.0.12.orig.tar.gz] I: copying [./pyusid_0.0.12-2.debian.tar.xz] I: Extracting source dpkg-source: warning: cannot verify inline signature for ./pyusid_0.0.12-2.dsc: unsupported subcommand dpkg-source: info: extracting pyusid in pyusid-0.0.12 dpkg-source: info: unpacking pyusid_0.0.12.orig.tar.gz dpkg-source: info: unpacking pyusid_0.0.12-2.debian.tar.xz dpkg-source: info: using patch list from debian/patches/series dpkg-source: info: applying 0001-Remove-requirement-for-six-module.patch I: Not using root during the build. I: Installing the build-deps I: user script /srv/workspace/pbuilder/21902/tmp/hooks/D02_print_environment starting I: set BUILDDIR='/build/reproducible-path' BUILDUSERGECOS='first user,first room,first work-phone,first home-phone,first other' BUILDUSERNAME='pbuilder1' BUILD_ARCH='armhf' DEBIAN_FRONTEND='noninteractive' DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=3 ' DISTRIBUTION='unstable' HOME='/root' HOST_ARCH='armhf' IFS=' ' INVOCATION_ID='3540b222f5614431a4dc1019193e5ad6' LANG='C' LANGUAGE='en_US:en' LC_ALL='C' MAIL='/var/mail/root' OPTIND='1' PATH='/usr/sbin:/usr/bin:/sbin:/bin:/usr/games' PBCURRENTCOMMANDLINEOPERATION='build' PBUILDER_OPERATION='build' PBUILDER_PKGDATADIR='/usr/share/pbuilder' PBUILDER_PKGLIBDIR='/usr/lib/pbuilder' PBUILDER_SYSCONFDIR='/etc' PPID='21902' PS1='# ' PS2='> ' PS4='+ ' PWD='/' SHELL='/bin/bash' SHLVL='2' SUDO_COMMAND='/usr/bin/timeout -k 18.1h 18h /usr/bin/ionice -c 3 /usr/bin/nice /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.2hgpVMVZ/pbuilderrc_s7nt --distribution unstable --hookdir /etc/pbuilder/first-build-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/unstable-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.2hgpVMVZ/b1 --logfile b1/build.log pyusid_0.0.12-2.dsc' SUDO_GID='114' SUDO_UID='108' SUDO_USER='jenkins' TERM='unknown' TZ='/usr/share/zoneinfo/Etc/GMT+12' USER='root' _='/usr/bin/systemd-run' http_proxy='http://10.0.0.15:3142/' I: uname -a Linux virt64a 6.1.0-28-arm64 #1 SMP Debian 6.1.119-1 (2024-11-22) aarch64 GNU/Linux I: ls -l /bin lrwxrwxrwx 1 root root 7 Nov 22 14:40 /bin -> usr/bin I: user script /srv/workspace/pbuilder/21902/tmp/hooks/D02_print_environment finished -> Attempting to satisfy build-dependencies -> Creating pbuilder-satisfydepends-dummy package Package: pbuilder-satisfydepends-dummy Version: 0.invalid.0 Architecture: armhf Maintainer: Debian Pbuilder Team Description: Dummy package to satisfy dependencies with aptitude - created by pbuilder This package was created automatically by pbuilder to satisfy the build-dependencies of the package being currently built. Depends: debhelper-compat (= 13), dh-python, python3-all, python3-setuptools, pybuild-plugin-pyproject, python3-h5py, python3-numpy, python3-pil, python3-sidpy dpkg-deb: building package 'pbuilder-satisfydepends-dummy' in '/tmp/satisfydepends-aptitude/pbuilder-satisfydepends-dummy.deb'. Selecting previously unselected package pbuilder-satisfydepends-dummy. (Reading database ... 19562 files and directories currently installed.) Preparing to unpack .../pbuilder-satisfydepends-dummy.deb ... Unpacking pbuilder-satisfydepends-dummy (0.invalid.0) ... dpkg: pbuilder-satisfydepends-dummy: dependency problems, but configuring anyway as you requested: pbuilder-satisfydepends-dummy depends on debhelper-compat (= 13); however: Package debhelper-compat is not installed. pbuilder-satisfydepends-dummy depends on dh-python; however: Package dh-python is not installed. pbuilder-satisfydepends-dummy depends on python3-all; however: Package python3-all is not installed. pbuilder-satisfydepends-dummy depends on python3-setuptools; however: Package python3-setuptools is not installed. pbuilder-satisfydepends-dummy depends on pybuild-plugin-pyproject; however: Package pybuild-plugin-pyproject is not installed. pbuilder-satisfydepends-dummy depends on python3-h5py; however: Package python3-h5py is not installed. pbuilder-satisfydepends-dummy depends on python3-numpy; however: Package python3-numpy is not installed. pbuilder-satisfydepends-dummy depends on python3-pil; however: Package python3-pil is not installed. pbuilder-satisfydepends-dummy depends on python3-sidpy; however: Package python3-sidpy is not installed. Setting up pbuilder-satisfydepends-dummy (0.invalid.0) ... Reading package lists... Building dependency tree... Reading state information... Initializing package states... Writing extended state information... Building tag database... pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0) pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0) The following NEW packages will be installed: autoconf{a} automake{a} autopoint{a} autotools-dev{a} blt{a} bsdextrautils{a} ca-certificates{a} debhelper{a} dh-autoreconf{a} dh-python{a} dh-strip-nondeterminism{a} dwz{a} file{a} fontconfig-config{a} fonts-dejavu-core{a} fonts-dejavu-mono{a} fonts-font-awesome{a} fonts-glyphicons-halflings{a} fonts-lyx{a} gdb{a} gettext{a} gettext-base{a} groff-base{a} intltool-debian{a} libaec0{a} libarchive-zip-perl{a} libbabeltrace1{a} libblas3{a} libbrotli1{a} libcom-err2{a} libcurl3t64-gnutls{a} libcurl4t64{a} libdebhelper-perl{a} libdebuginfod-common{a} libdebuginfod1t64{a} libdeflate0{a} libdw1t64{a} libelf1t64{a} libexpat1{a} libffi8{a} libfile-stripnondeterminism-perl{a} libfontconfig1{a} libfreetype6{a} libfribidi0{a} libgcrypt20{a} libgfortran5{a} libglib2.0-0t64{a} libgnutls30t64{a} libgpg-error0{a} libgraphite2-3{a} libgssapi-krb5-2{a} libharfbuzz0b{a} libhdf5-310{a} libhdf5-hl-310{a} libicu72{a} libidn2-0{a} libimagequant0{a} libjbig0{a} libjpeg62-turbo{a} libjs-bootstrap{a} libjs-jquery{a} libjs-jquery-ui{a} libjson-c5{a} libk5crypto3{a} libkeyutils1{a} libkrb5-3{a} libkrb5support0{a} liblapack3{a} liblbfgsb0{a} liblcms2-2{a} libldap-2.5-0{a} liblerc4{a} liblzf1{a} libmagic-mgc{a} libmagic1t64{a} libnghttp2-14{a} libnghttp3-9{a} libngtcp2-16{a} libngtcp2-crypto-gnutls8{a} libnorm1t64{a} libnsl2{a} libopenjp2-7{a} libp11-kit0{a} libpgm-5.3-0t64{a} libpipeline1{a} libpng16-16t64{a} libproc2-0{a} libpsl5t64{a} libpython3-stdlib{a} libpython3.12-minimal{a} libpython3.12-stdlib{a} libpython3.13{a} libpython3.13-minimal{a} libpython3.13-stdlib{a} libqhull-r8.0{a} libraqm0{a} libreadline8t64{a} librtmp1{a} libsasl2-2{a} libsasl2-modules-db{a} libsharpyuv0{a} libsodium23{a} libsource-highlight-common{a} libsource-highlight4t64{a} libssh2-1t64{a} libsymspg2{a} libsz2{a} libtasn1-6{a} libtcl8.6{a} libtiff6{a} libtirpc-common{a} libtirpc3t64{a} libtk8.6{a} libtool{a} libuchardet0{a} libunistring5{a} libwebp7{a} libwebpdemux2{a} libwebpmux3{a} libx11-6{a} libx11-data{a} libxau6{a} libxcb1{a} libxdmcp6{a} libxext6{a} libxft2{a} libxml2{a} libxrender1{a} libxslt1.1{a} libxss1{a} libyaml-0-2{a} libzmq5{a} m4{a} man-db{a} media-types{a} netbase{a} openssl{a} po-debconf{a} procps{a} pybuild-plugin-pyproject{a} python-matplotlib-data{a} python3{a} python3-all{a} python3-ase{a} python3-asttokens{a} python3-attr{a} python3-autocommand{a} python3-brotli{a} python3-build{a} python3-cffi-backend{a} python3-click{a} python3-cloudpickle{a} python3-comm{a} python3-contourpy{a} python3-coverage{a} python3-cycler{a} python3-cytoolz{a} python3-dask{a} python3-dateutil{a} python3-debugpy{a} python3-decorator{a} python3-distributed{a} python3-executing{a} python3-fonttools{a} python3-fs{a} python3-fsspec{a} python3-h5py{a} python3-h5py-serial{a} python3-inflect{a} python3-installer{a} python3-ipykernel{a} python3-ipython{a} python3-ipywidgets{a} python3-jaraco.context{a} python3-jaraco.functools{a} python3-jaraco.text{a} python3-jedi{a} python3-jinja2{a} python3-joblib{a} python3-jupyter-client{a} python3-jupyter-core{a} python3-kiwisolver{a} python3-locket{a} python3-lxml{a} python3-lz4{a} python3-markupsafe{a} python3-matplotlib{a} python3-matplotlib-inline{a} python3-minimal{a} python3-more-itertools{a} python3-mpmath{a} python3-msgpack{a} python3-nest-asyncio{a} python3-numpy{a} python3-packaging{a} python3-parso{a} python3-partd{a} python3-pexpect{a} python3-pil{a} python3-pil.imagetk{a} python3-pkg-resources{a} python3-platformdirs{a} python3-prompt-toolkit{a} python3-psutil{a} python3-ptyprocess{a} python3-pure-eval{a} python3-pygments{a} python3-pyparsing{a} python3-pyproject-hooks{a} python3-scipy{a} python3-setuptools{a} python3-sidpy{a} python3-sklearn{a} python3-sklearn-lib{a} python3-sortedcontainers{a} python3-spglib{a} python3-stack-data{a} python3-sympy{a} python3-tblib{a} python3-threadpoolctl{a} python3-tk{a} python3-toml{a} python3-toolz{a} python3-tornado{a} python3-traitlets{a} python3-typeguard{a} python3-typeshed{a} python3-typing-extensions{a} python3-tz{a} python3-ufolib2{a} python3-urllib3{a} python3-wcwidth{a} python3-wheel{a} python3-yaml{a} python3-zict{a} python3-zipp{a} python3-zmq{a} python3.12{a} python3.12-minimal{a} python3.12-tk{a} python3.13{a} python3.13-minimal{a} python3.13-tk{a} readline-common{a} sensible-utils{a} tk8.6-blt2.5{a} tzdata{a} ucf{a} unicode-data{a} x11-common{a} The following packages are RECOMMENDED but will NOT be installed: curl git isympy-common javascript-common krb5-locales libarchive-cpio-perl libc6-dbg libglib2.0-data libgpg-error-l10n libldap-common libltdl-dev libmail-sendmail-perl libsasl2-modules linux-sysctl-defaults lynx psmisc publicsuffix python3-babel python3-blosc python3-bs4 python3-cssselect python3-dropbox python3-html5lib python3-libarchive-c python3-olefile python3-pandas python3-paramiko python3-pooch python3-pygit2 python3-pytest python3-requests python3-simplejson shared-mime-info wget xdg-user-dirs 0 packages upgraded, 250 newly installed, 0 to remove and 0 not upgraded. Need to get 132 MB of archives. After unpacking 559 MB will be used. Writing extended state information... Get: 1 http://deb.debian.org/debian unstable/main armhf libproc2-0 armhf 2:4.0.4-6 [56.0 kB] Get: 2 http://deb.debian.org/debian unstable/main armhf procps armhf 2:4.0.4-6 [864 kB] Get: 3 http://deb.debian.org/debian unstable/main armhf sensible-utils all 0.0.24 [24.8 kB] Get: 4 http://deb.debian.org/debian unstable/main armhf ucf all 3.0046 [41.1 kB] Get: 5 http://deb.debian.org/debian unstable/main armhf libdebuginfod-common all 0.192-4 [23.7 kB] Get: 6 http://deb.debian.org/debian unstable/main armhf libpython3.13-minimal armhf 3.13.1-3 [844 kB] Get: 7 http://deb.debian.org/debian unstable/main armhf libexpat1 armhf 2.6.4-1 [83.5 kB] Get: 8 http://deb.debian.org/debian unstable/main armhf python3.13-minimal armhf 3.13.1-3 [1840 kB] Get: 9 http://deb.debian.org/debian unstable/main armhf python3-minimal armhf 3.13.1-2 [27.0 kB] Get: 10 http://deb.debian.org/debian unstable/main armhf media-types all 10.1.0 [26.9 kB] Get: 11 http://deb.debian.org/debian unstable/main armhf netbase all 6.4 [12.8 kB] Get: 12 http://deb.debian.org/debian unstable/main armhf tzdata all 2024b-5 [256 kB] Get: 13 http://deb.debian.org/debian unstable/main armhf libffi8 armhf 3.4.6-1 [20.0 kB] Get: 14 http://deb.debian.org/debian unstable/main armhf readline-common all 8.2-6 [69.4 kB] Get: 15 http://deb.debian.org/debian unstable/main armhf libreadline8t64 armhf 8.2-6 [146 kB] Get: 16 http://deb.debian.org/debian unstable/main armhf libpython3.13-stdlib armhf 3.13.1-3 [1850 kB] Get: 17 http://deb.debian.org/debian unstable/main armhf python3.13 armhf 3.13.1-3 [740 kB] Get: 18 http://deb.debian.org/debian unstable/main armhf libpython3-stdlib armhf 3.13.1-2 [9952 B] Get: 19 http://deb.debian.org/debian unstable/main armhf python3 armhf 3.13.1-2 [28.0 kB] Get: 20 http://deb.debian.org/debian unstable/main armhf libpython3.12-minimal armhf 3.12.8-4 [803 kB] Get: 21 http://deb.debian.org/debian unstable/main armhf python3.12-minimal armhf 3.12.8-4 [1809 kB] Get: 22 http://deb.debian.org/debian unstable/main armhf openssl armhf 3.4.0-2 [1388 kB] Get: 23 http://deb.debian.org/debian unstable/main armhf ca-certificates all 20241223 [164 kB] Get: 24 http://deb.debian.org/debian unstable/main armhf libmagic-mgc armhf 1:5.45-3+b1 [314 kB] Get: 25 http://deb.debian.org/debian unstable/main armhf libmagic1t64 armhf 1:5.45-3+b1 [98.5 kB] Get: 26 http://deb.debian.org/debian unstable/main armhf file armhf 1:5.45-3+b1 [42.3 kB] Get: 27 http://deb.debian.org/debian unstable/main armhf gettext-base armhf 0.22.5-4 [196 kB] Get: 28 http://deb.debian.org/debian unstable/main armhf libuchardet0 armhf 0.0.8-1+b2 [65.6 kB] Get: 29 http://deb.debian.org/debian unstable/main armhf groff-base armhf 1.23.0-7 [1095 kB] Get: 30 http://deb.debian.org/debian unstable/main armhf bsdextrautils armhf 2.40.3-1 [84.6 kB] Get: 31 http://deb.debian.org/debian unstable/main armhf libpipeline1 armhf 1.5.8-1 [35.0 kB] Get: 32 http://deb.debian.org/debian unstable/main armhf man-db armhf 2.13.0-1 [1382 kB] Get: 33 http://deb.debian.org/debian unstable/main armhf m4 armhf 1.4.19-5 [272 kB] Get: 34 http://deb.debian.org/debian unstable/main armhf autoconf all 2.72-3 [493 kB] Get: 35 http://deb.debian.org/debian unstable/main armhf autotools-dev all 20220109.1 [51.6 kB] Get: 36 http://deb.debian.org/debian unstable/main armhf automake all 1:1.16.5-1.3 [823 kB] Get: 37 http://deb.debian.org/debian unstable/main armhf autopoint all 0.22.5-4 [723 kB] Get: 38 http://deb.debian.org/debian unstable/main armhf libtcl8.6 armhf 8.6.16+dfsg-1 [934 kB] Get: 39 http://deb.debian.org/debian unstable/main armhf libbrotli1 armhf 1.1.0-2+b6 [282 kB] Get: 40 http://deb.debian.org/debian unstable/main armhf libpng16-16t64 armhf 1.6.45-1 [263 kB] Get: 41 http://deb.debian.org/debian unstable/main armhf libfreetype6 armhf 2.13.3+dfsg-1 [385 kB] Get: 42 http://deb.debian.org/debian unstable/main armhf fonts-dejavu-mono all 2.37-8 [489 kB] Get: 43 http://deb.debian.org/debian unstable/main armhf fonts-dejavu-core all 2.37-8 [840 kB] Get: 44 http://deb.debian.org/debian unstable/main armhf fontconfig-config armhf 2.15.0-2 [317 kB] Get: 45 http://deb.debian.org/debian unstable/main armhf libfontconfig1 armhf 2.15.0-2 [371 kB] Get: 46 http://deb.debian.org/debian unstable/main armhf libxau6 armhf 1:1.0.11-1 [19.7 kB] Get: 47 http://deb.debian.org/debian unstable/main armhf libxdmcp6 armhf 1:1.1.5-1 [26.4 kB] Get: 48 http://deb.debian.org/debian unstable/main armhf libxcb1 armhf 1.17.0-2+b1 [140 kB] Get: 49 http://deb.debian.org/debian unstable/main armhf libx11-data all 2:1.8.10-2 [337 kB] Get: 50 http://deb.debian.org/debian unstable/main armhf libx11-6 armhf 2:1.8.10-2 [750 kB] Get: 51 http://deb.debian.org/debian unstable/main armhf libxrender1 armhf 1:0.9.10-1.1+b3 [25.0 kB] Get: 52 http://deb.debian.org/debian unstable/main armhf libxft2 armhf 2.3.6-1+b3 [46.5 kB] Get: 53 http://deb.debian.org/debian unstable/main armhf libxext6 armhf 2:1.3.4-1+b3 [45.2 kB] Get: 54 http://deb.debian.org/debian unstable/main armhf x11-common all 1:7.7+23.1 [216 kB] Get: 55 http://deb.debian.org/debian unstable/main armhf libxss1 armhf 1:1.2.3-1+b3 [16.4 kB] Get: 56 http://deb.debian.org/debian unstable/main armhf libtk8.6 armhf 8.6.16-1 [698 kB] Get: 57 http://deb.debian.org/debian unstable/main armhf tk8.6-blt2.5 armhf 2.5.3+dfsg-7 [481 kB] Get: 58 http://deb.debian.org/debian unstable/main armhf blt armhf 2.5.3+dfsg-7 [6024 B] Get: 59 http://deb.debian.org/debian unstable/main armhf libdebhelper-perl all 13.23 [90.6 kB] Get: 60 http://deb.debian.org/debian unstable/main armhf libtool all 2.5.4-2 [539 kB] Get: 61 http://deb.debian.org/debian unstable/main armhf dh-autoreconf all 20 [17.1 kB] Get: 62 http://deb.debian.org/debian unstable/main armhf libarchive-zip-perl all 1.68-1 [104 kB] Get: 63 http://deb.debian.org/debian unstable/main armhf libfile-stripnondeterminism-perl all 1.14.0-1 [19.5 kB] Get: 64 http://deb.debian.org/debian unstable/main armhf dh-strip-nondeterminism all 1.14.0-1 [8448 B] Get: 65 http://deb.debian.org/debian unstable/main armhf libelf1t64 armhf 0.192-4 [184 kB] Get: 66 http://deb.debian.org/debian unstable/main armhf dwz armhf 0.15-1+b2 [106 kB] Get: 67 http://deb.debian.org/debian unstable/main armhf libunistring5 armhf 1.3-1 [444 kB] Get: 68 http://deb.debian.org/debian unstable/main armhf libicu72 armhf 72.1-6 [9086 kB] Get: 69 http://deb.debian.org/debian unstable/main armhf libxml2 armhf 2.12.7+dfsg+really2.9.14-0.2+b1 [605 kB] Get: 70 http://deb.debian.org/debian unstable/main armhf gettext armhf 0.22.5-4 [1489 kB] Get: 71 http://deb.debian.org/debian unstable/main armhf intltool-debian all 0.35.0+20060710.6 [22.9 kB] Get: 72 http://deb.debian.org/debian unstable/main armhf po-debconf all 1.0.21+nmu1 [248 kB] Get: 73 http://deb.debian.org/debian unstable/main armhf debhelper all 13.23 [919 kB] Get: 74 http://deb.debian.org/debian unstable/main armhf python3-autocommand all 2.2.2-3 [13.6 kB] Get: 75 http://deb.debian.org/debian unstable/main armhf python3-more-itertools all 10.5.0-1 [63.8 kB] Get: 76 http://deb.debian.org/debian unstable/main armhf python3-typing-extensions all 4.12.2-2 [73.0 kB] Get: 77 http://deb.debian.org/debian unstable/main armhf python3-typeguard all 4.4.1-1 [37.0 kB] Get: 78 http://deb.debian.org/debian unstable/main armhf python3-inflect all 7.3.1-2 [32.4 kB] Get: 79 http://deb.debian.org/debian unstable/main armhf python3-jaraco.context all 6.0.0-1 [7984 B] Get: 80 http://deb.debian.org/debian unstable/main armhf python3-jaraco.functools all 4.1.0-1 [12.0 kB] Get: 81 http://deb.debian.org/debian unstable/main armhf python3-pkg-resources all 75.6.0-1 [222 kB] Get: 82 http://deb.debian.org/debian unstable/main armhf python3-jaraco.text all 4.0.0-1 [11.4 kB] Get: 83 http://deb.debian.org/debian unstable/main armhf python3-zipp all 3.21.0-1 [10.6 kB] Get: 84 http://deb.debian.org/debian unstable/main armhf python3-setuptools all 75.6.0-1 [720 kB] Get: 85 http://deb.debian.org/debian unstable/main armhf dh-python all 6.20250108 [113 kB] Get: 86 http://deb.debian.org/debian unstable/main armhf fonts-font-awesome all 5.0.10+really4.7.0~dfsg-4.1 [517 kB] Get: 87 http://deb.debian.org/debian unstable/main armhf fonts-glyphicons-halflings all 1.009~3.4.1+dfsg-3 [162 kB] Get: 88 http://deb.debian.org/debian unstable/main armhf fonts-lyx all 2.4.2.1-1 [190 kB] Get: 89 http://deb.debian.org/debian unstable/main armhf libdw1t64 armhf 0.192-4 [232 kB] Get: 90 http://deb.debian.org/debian unstable/main armhf libglib2.0-0t64 armhf 2.82.4-1 [1327 kB] Get: 91 http://deb.debian.org/debian unstable/main armhf libbabeltrace1 armhf 1.5.11-4+b1 [155 kB] Get: 92 http://deb.debian.org/debian unstable/main armhf libidn2-0 armhf 2.3.7-2+b1 [125 kB] Get: 93 http://deb.debian.org/debian unstable/main armhf libp11-kit0 armhf 0.25.5-3 [385 kB] Get: 94 http://deb.debian.org/debian unstable/main armhf libtasn1-6 armhf 4.19.0-3+b3 [43.9 kB] Get: 95 http://deb.debian.org/debian unstable/main armhf libgnutls30t64 armhf 3.8.8-2 [1370 kB] Get: 96 http://deb.debian.org/debian unstable/main armhf libkrb5support0 armhf 1.21.3-3 [30.0 kB] Get: 97 http://deb.debian.org/debian unstable/main armhf libcom-err2 armhf 1.47.2-1 [23.3 kB] Get: 98 http://deb.debian.org/debian unstable/main armhf libk5crypto3 armhf 1.21.3-3 [75.8 kB] Get: 99 http://deb.debian.org/debian unstable/main armhf libkeyutils1 armhf 1.6.3-4 [8096 B] Get: 100 http://deb.debian.org/debian unstable/main armhf libkrb5-3 armhf 1.21.3-3 [283 kB] Get: 101 http://deb.debian.org/debian unstable/main armhf libgssapi-krb5-2 armhf 1.21.3-3 [114 kB] Get: 102 http://deb.debian.org/debian unstable/main armhf libsasl2-modules-db armhf 2.1.28+dfsg1-8 [18.2 kB] Get: 103 http://deb.debian.org/debian unstable/main armhf libsasl2-2 armhf 2.1.28+dfsg1-8 [50.2 kB] Get: 104 http://deb.debian.org/debian unstable/main armhf libldap-2.5-0 armhf 2.5.19+dfsg-1 [163 kB] Get: 105 http://deb.debian.org/debian unstable/main armhf libnghttp2-14 armhf 1.64.0-1 [62.9 kB] Get: 106 http://deb.debian.org/debian unstable/main armhf libnghttp3-9 armhf 1.6.0-2 [56.8 kB] Get: 107 http://deb.debian.org/debian unstable/main armhf libngtcp2-16 armhf 1.9.1-1 [115 kB] Get: 108 http://deb.debian.org/debian unstable/main armhf libngtcp2-crypto-gnutls8 armhf 1.9.1-1 [15.8 kB] Get: 109 http://deb.debian.org/debian unstable/main armhf libpsl5t64 armhf 0.21.2-1.1+b1 [55.8 kB] Get: 110 http://deb.debian.org/debian unstable/main armhf librtmp1 armhf 2.4+20151223.gitfa8646d.1-2+b5 [53.2 kB] Get: 111 http://deb.debian.org/debian unstable/main armhf libssh2-1t64 armhf 1.11.1-1 [227 kB] Get: 112 http://deb.debian.org/debian unstable/main armhf libcurl3t64-gnutls armhf 8.11.1-1 [311 kB] Get: 113 http://deb.debian.org/debian unstable/main armhf libjson-c5 armhf 0.18+ds-1 [42.3 kB] Get: 114 http://deb.debian.org/debian unstable/main armhf libdebuginfod1t64 armhf 0.192-4 [30.7 kB] Get: 115 http://deb.debian.org/debian unstable/main armhf libpython3.13 armhf 3.13.1-3 [1869 kB] Get: 116 http://deb.debian.org/debian unstable/main armhf libsource-highlight-common all 3.1.9-4.3 [77.5 kB] Get: 117 http://deb.debian.org/debian unstable/main armhf libsource-highlight4t64 armhf 3.1.9-4.3+b1 [292 kB] Get: 118 http://deb.debian.org/debian unstable/main armhf gdb armhf 15.2-1+b1 [3825 kB] Get: 119 http://deb.debian.org/debian unstable/main armhf libaec0 armhf 1.1.3-1+b1 [21.9 kB] Get: 120 http://deb.debian.org/debian unstable/main armhf libblas3 armhf 3.12.0-4 [111 kB] Get: 121 http://deb.debian.org/debian unstable/main armhf libcurl4t64 armhf 8.11.1-1 [303 kB] Get: 122 http://deb.debian.org/debian unstable/main armhf libdeflate0 armhf 1.22-1 [36.3 kB] Get: 123 http://deb.debian.org/debian unstable/main armhf libfribidi0 armhf 1.0.16-1 [24.6 kB] Get: 124 http://deb.debian.org/debian unstable/main armhf libgpg-error0 armhf 1.51-3 [71.9 kB] Get: 125 http://deb.debian.org/debian unstable/main armhf libgcrypt20 armhf 1.11.0-7 [727 kB] Get: 126 http://deb.debian.org/debian unstable/main armhf libgfortran5 armhf 14.2.0-12 [263 kB] Get: 127 http://deb.debian.org/debian unstable/main armhf libgraphite2-3 armhf 1.3.14-2+b1 [63.1 kB] Get: 128 http://deb.debian.org/debian unstable/main armhf libharfbuzz0b armhf 10.1.0-2 [420 kB] Get: 129 http://deb.debian.org/debian unstable/main armhf libsz2 armhf 1.1.3-1+b1 [8036 B] Get: 130 http://deb.debian.org/debian unstable/main armhf libhdf5-310 armhf 1.14.5+repack-3 [1272 kB] Get: 131 http://deb.debian.org/debian unstable/main armhf libhdf5-hl-310 armhf 1.14.5+repack-3 [67.3 kB] Get: 132 http://deb.debian.org/debian unstable/main armhf libimagequant0 armhf 2.18.0-1+b2 [30.5 kB] Get: 133 http://deb.debian.org/debian unstable/main armhf libjbig0 armhf 2.1-6.1+b2 [27.3 kB] Get: 134 http://deb.debian.org/debian unstable/main armhf libjpeg62-turbo armhf 1:2.1.5-3+b1 [145 kB] Get: 135 http://deb.debian.org/debian unstable/main armhf libjs-bootstrap all 3.4.1+dfsg-3 [172 kB] Get: 136 http://deb.debian.org/debian unstable/main armhf libjs-jquery all 3.6.1+dfsg+~3.5.14-1 [326 kB] Get: 137 http://deb.debian.org/debian unstable/main armhf libjs-jquery-ui all 1.13.2+dfsg-1 [250 kB] Get: 138 http://deb.debian.org/debian unstable/main armhf liblapack3 armhf 3.12.0-4 [1828 kB] Get: 139 http://deb.debian.org/debian unstable/main armhf liblbfgsb0 armhf 3.0+dfsg.4-1+b2 [25.6 kB] Get: 140 http://deb.debian.org/debian unstable/main armhf liblcms2-2 armhf 2.16-2 [131 kB] Get: 141 http://deb.debian.org/debian unstable/main armhf liblerc4 armhf 4.0.0+ds-5 [146 kB] Get: 142 http://deb.debian.org/debian unstable/main armhf liblzf1 armhf 3.6-4+b3 [9380 B] Get: 143 http://deb.debian.org/debian unstable/main armhf libnorm1t64 armhf 1.5.9+dfsg-3.1+b2 [187 kB] Get: 144 http://deb.debian.org/debian unstable/main armhf libtirpc-common all 1.3.4+ds-1.3 [10.9 kB] Get: 145 http://deb.debian.org/debian unstable/main armhf libtirpc3t64 armhf 1.3.4+ds-1.3+b1 [71.3 kB] Get: 146 http://deb.debian.org/debian unstable/main armhf libnsl2 armhf 1.3.0-3+b3 [35.0 kB] Get: 147 http://deb.debian.org/debian unstable/main armhf libopenjp2-7 armhf 2.5.0-2+b4 [179 kB] Get: 148 http://deb.debian.org/debian unstable/main armhf libpgm-5.3-0t64 armhf 5.3.128~dfsg-2.1+b1 [156 kB] Get: 149 http://deb.debian.org/debian unstable/main armhf libpython3.12-stdlib armhf 3.12.8-4 [1830 kB] Get: 150 http://deb.debian.org/debian unstable/main armhf libqhull-r8.0 armhf 2020.2-6+b2 [220 kB] Get: 151 http://deb.debian.org/debian unstable/main armhf libraqm0 armhf 0.10.2-1 [11.6 kB] Get: 152 http://deb.debian.org/debian unstable/main armhf libsharpyuv0 armhf 1.5.0-0.1 [114 kB] Get: 153 http://deb.debian.org/debian unstable/main armhf libsodium23 armhf 1.0.18-1+b2 [139 kB] Get: 154 http://deb.debian.org/debian unstable/main armhf libsymspg2 armhf 2.5.0-1+b1 [184 kB] Get: 155 http://deb.debian.org/debian unstable/main armhf libwebp7 armhf 1.5.0-0.1 [273 kB] Get: 156 http://deb.debian.org/debian unstable/main armhf libtiff6 armhf 4.5.1+git230720-5 [302 kB] Get: 157 http://deb.debian.org/debian unstable/main armhf libwebpdemux2 armhf 1.5.0-0.1 [112 kB] Get: 158 http://deb.debian.org/debian unstable/main armhf libwebpmux3 armhf 1.5.0-0.1 [123 kB] Get: 159 http://deb.debian.org/debian unstable/main armhf libxslt1.1 armhf 1.1.35-1.1+b1 [212 kB] Get: 160 http://deb.debian.org/debian unstable/main armhf libyaml-0-2 armhf 0.2.5-2 [45.4 kB] Get: 161 http://deb.debian.org/debian unstable/main armhf libzmq5 armhf 4.3.5-1+b3 [239 kB] Get: 162 http://deb.debian.org/debian unstable/main armhf python3-packaging all 24.2-1 [55.3 kB] Get: 163 http://deb.debian.org/debian unstable/main armhf python3-pyproject-hooks all 1.2.0-1 [11.7 kB] Get: 164 http://deb.debian.org/debian unstable/main armhf python3-toml all 0.10.2-1 [16.2 kB] Get: 165 http://deb.debian.org/debian unstable/main armhf python3-wheel all 0.45.1-1 [56.7 kB] Get: 166 http://deb.debian.org/debian unstable/main armhf python3-build all 1.2.2-1 [36.0 kB] Get: 167 http://deb.debian.org/debian unstable/main armhf python3-installer all 0.7.0+dfsg1-3 [18.6 kB] Get: 168 http://deb.debian.org/debian unstable/main armhf pybuild-plugin-pyproject all 6.20250108 [11.6 kB] Get: 169 http://deb.debian.org/debian unstable/main armhf python-matplotlib-data all 3.8.3-3 [2731 kB] Get: 170 http://deb.debian.org/debian unstable/main armhf python3.12 armhf 3.12.8-4 [677 kB] Get: 171 http://deb.debian.org/debian unstable/main armhf python3-all armhf 3.13.1-2 [1056 B] Get: 172 http://deb.debian.org/debian unstable/main armhf python3-numpy armhf 1:1.26.4+ds-12 [3676 kB] Get: 173 http://deb.debian.org/debian unstable/main armhf python3-decorator all 5.1.1-5 [15.1 kB] Get: 174 http://deb.debian.org/debian unstable/main armhf python3-scipy armhf 1.14.1-3 [17.8 MB] Get: 175 http://deb.debian.org/debian unstable/main armhf python3-spglib armhf 2.5.0-1+b1 [51.1 kB] Get: 176 http://deb.debian.org/debian unstable/main armhf python3-dateutil all 2.9.0-3 [79.3 kB] Get: 177 http://deb.debian.org/debian unstable/main armhf python3-pil armhf 10.4.0-1.1 [478 kB] Get: 178 http://deb.debian.org/debian unstable/main armhf python3.12-tk armhf 3.12.8-4 [109 kB] Get: 179 http://deb.debian.org/debian unstable/main armhf python3.13-tk armhf 3.13.1-3 [100.0 kB] Get: 180 http://deb.debian.org/debian unstable/main armhf python3-tk armhf 3.13.1-1 [9464 B] Get: 181 http://deb.debian.org/debian unstable/main armhf python3-pil.imagetk armhf 10.4.0-1.1 [79.2 kB] Get: 182 http://deb.debian.org/debian unstable/main armhf python3-pyparsing all 3.1.2-1 [146 kB] Get: 183 http://deb.debian.org/debian unstable/main armhf python3-contourpy armhf 1.3.1-1 [217 kB] Get: 184 http://deb.debian.org/debian unstable/main armhf python3-cycler all 0.12.1-1 [9496 B] Get: 185 http://deb.debian.org/debian unstable/main armhf python3-brotli armhf 1.1.0-2+b6 [302 kB] Get: 186 http://deb.debian.org/debian unstable/main armhf python3-tz all 2024.2-1 [31.0 kB] Get: 187 http://deb.debian.org/debian unstable/main armhf python3-platformdirs all 4.3.6-1 [16.6 kB] Get: 188 http://deb.debian.org/debian unstable/main armhf python3-fs all 2.4.16-5.1 [95.2 kB] Get: 189 http://deb.debian.org/debian unstable/main armhf python3-lxml armhf 5.3.0-1+b1 [1468 kB] Get: 190 http://deb.debian.org/debian unstable/main armhf python3-lz4 armhf 4.4.0+dfsg-1 [23.9 kB] Get: 191 http://deb.debian.org/debian unstable/main armhf python3-mpmath all 1.3.0-1 [419 kB] Get: 192 http://deb.debian.org/debian unstable/main armhf python3-sympy all 1.13.3-1 [4147 kB] Get: 193 http://deb.debian.org/debian unstable/main armhf python3-attr all 24.2.0-1 [68.4 kB] Get: 194 http://deb.debian.org/debian unstable/main armhf python3-ufolib2 all 0.17.0+dfsg1-1 [33.0 kB] Get: 195 http://deb.debian.org/debian unstable/main armhf unicode-data all 15.1.0-1 [8547 kB] Get: 196 http://deb.debian.org/debian unstable/main armhf python3-fonttools armhf 4.55.0-3 [1529 kB] Get: 197 http://deb.debian.org/debian unstable/main armhf python3-kiwisolver armhf 1.4.7-2+b1 [64.9 kB] Get: 198 http://deb.debian.org/debian unstable/main armhf python3-matplotlib armhf 3.8.3-3+b1 [5579 kB] Get: 199 http://deb.debian.org/debian unstable/main armhf python3-ase all 3.23.0-1 [1698 kB] Get: 200 http://deb.debian.org/debian unstable/main armhf python3-asttokens all 3.0.0-1 [20.1 kB] Get: 201 http://deb.debian.org/debian unstable/main armhf python3-cffi-backend armhf 1.17.1-2+b1 [83.7 kB] Get: 202 http://deb.debian.org/debian unstable/main armhf python3-click all 8.1.8-1 [95.2 kB] Get: 203 http://deb.debian.org/debian unstable/main armhf python3-cloudpickle all 3.1.0-1 [26.4 kB] Get: 204 http://deb.debian.org/debian unstable/main armhf python3-traitlets all 5.14.3+really5.14.3-1 [70.9 kB] Get: 205 http://deb.debian.org/debian unstable/main armhf python3-comm all 0.2.1-1 [8104 B] Get: 206 http://deb.debian.org/debian unstable/main armhf python3-coverage armhf 7.6.0+dfsg1-2+b1 [176 kB] Get: 207 http://deb.debian.org/debian unstable/main armhf python3-toolz all 1.0.0-2 [44.2 kB] Get: 208 http://deb.debian.org/debian unstable/main armhf python3-cytoolz armhf 1.0.1-1 [358 kB] Get: 209 http://deb.debian.org/debian unstable/main armhf python3-fsspec all 2024.10.0-1 [206 kB] Get: 210 http://deb.debian.org/debian unstable/main armhf python3-locket all 1.0.0-2 [5820 B] Get: 211 http://deb.debian.org/debian unstable/main armhf python3-partd all 1.4.2-1 [15.4 kB] Get: 212 http://deb.debian.org/debian unstable/main armhf python3-yaml armhf 6.0.2-1+b1 [158 kB] Get: 213 http://deb.debian.org/debian unstable/main armhf python3-dask all 2024.12.1+dfsg-1 [984 kB] Get: 214 http://deb.debian.org/debian unstable/main armhf python3-debugpy armhf 1.8.8+ds-2 [1004 kB] Get: 215 http://deb.debian.org/debian unstable/main armhf python3-markupsafe armhf 2.1.5-1+b2 [13.4 kB] Get: 216 http://deb.debian.org/debian unstable/main armhf python3-jinja2 all 3.1.3-2 [120 kB] Get: 217 http://deb.debian.org/debian unstable/main armhf python3-msgpack armhf 1.0.3-3+b3 [101 kB] Get: 218 http://deb.debian.org/debian unstable/main armhf python3-psutil armhf 5.9.8-2 [225 kB] Get: 219 http://deb.debian.org/debian unstable/main armhf python3-sortedcontainers all 2.4.0-2 [31.9 kB] Get: 220 http://deb.debian.org/debian unstable/main armhf python3-tblib all 3.0.0-5 [13.7 kB] Get: 221 http://deb.debian.org/debian unstable/main armhf python3-tornado armhf 6.4.2-1 [349 kB] Get: 222 http://deb.debian.org/debian unstable/main armhf python3-urllib3 all 2.2.3-4 [112 kB] Get: 223 http://deb.debian.org/debian unstable/main armhf python3-zict all 3.0.0-2 [29.7 kB] Get: 224 http://deb.debian.org/debian unstable/main armhf python3-distributed all 2024.12.1+ds-1 [1162 kB] Get: 225 http://deb.debian.org/debian unstable/main armhf python3-executing all 2.1.0-0.1 [23.9 kB] Get: 226 http://deb.debian.org/debian unstable/main armhf python3-h5py-serial armhf 3.12.1-1+b1 [1275 kB] Get: 227 http://deb.debian.org/debian unstable/main armhf python3-h5py all 3.12.1-1 [15.0 kB] Get: 228 http://deb.debian.org/debian unstable/main armhf python3-parso all 0.8.4-1 [67.8 kB] Get: 229 http://deb.debian.org/debian unstable/main armhf python3-typeshed all 0.0~git20241223.ea91db2-1 [1530 kB] Get: 230 http://deb.debian.org/debian unstable/main armhf python3-jedi all 0.19.1+ds1-1 [691 kB] Get: 231 http://deb.debian.org/debian unstable/main armhf python3-matplotlib-inline all 0.1.6-2 [8652 B] Get: 232 http://deb.debian.org/debian unstable/main armhf python3-wcwidth all 0.2.13+dfsg1-1 [24.9 kB] Get: 233 http://deb.debian.org/debian unstable/main armhf python3-prompt-toolkit all 3.0.48-1 [277 kB] Get: 234 http://deb.debian.org/debian unstable/main armhf python3-pygments all 2.18.0+dfsg-1 [836 kB] Get: 235 http://deb.debian.org/debian unstable/main armhf python3-pure-eval all 0.2.3-1 [11.2 kB] Get: 236 http://deb.debian.org/debian unstable/main armhf python3-stack-data all 0.6.3-1 [21.6 kB] Get: 237 http://deb.debian.org/debian unstable/main armhf python3-ptyprocess all 0.7.0-6 [14.5 kB] Get: 238 http://deb.debian.org/debian unstable/main armhf python3-pexpect all 4.9-3 [55.6 kB] Get: 239 http://deb.debian.org/debian unstable/main armhf python3-ipython all 8.30.0-2 [568 kB] Get: 240 http://deb.debian.org/debian unstable/main armhf python3-jupyter-core all 5.7.2-5 [35.3 kB] Get: 241 http://deb.debian.org/debian unstable/main armhf python3-zmq armhf 26.2.0-1 [197 kB] Get: 242 http://deb.debian.org/debian unstable/main armhf python3-jupyter-client all 8.6.3-1 [92.0 kB] Get: 243 http://deb.debian.org/debian unstable/main armhf python3-nest-asyncio all 1.5.4-1 [6248 B] Get: 244 http://deb.debian.org/debian unstable/main armhf python3-ipykernel all 6.29.5-2 [102 kB] Get: 245 http://deb.debian.org/debian unstable/main armhf python3-ipywidgets all 8.1.5-3 [105 kB] Get: 246 http://deb.debian.org/debian unstable/main armhf python3-joblib all 1.3.2-5 [216 kB] Get: 247 http://deb.debian.org/debian unstable/main armhf python3-threadpoolctl all 3.1.0-1 [21.2 kB] Get: 248 http://deb.debian.org/debian unstable/main armhf python3-sklearn-lib armhf 1.4.2+dfsg-7 [5340 kB] Get: 249 http://deb.debian.org/debian unstable/main armhf python3-sklearn all 1.4.2+dfsg-7 [2248 kB] Get: 250 http://deb.debian.org/debian unstable/main armhf python3-sidpy all 0.12.3-3 [76.9 kB] Fetched 132 MB in 3s (37.8 MB/s) Preconfiguring packages ... Selecting previously unselected package libproc2-0:armhf. (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 19562 files and directories currently installed.) Preparing to unpack .../0-libproc2-0_2%3a4.0.4-6_armhf.deb ... Unpacking libproc2-0:armhf (2:4.0.4-6) ... Selecting previously unselected package procps. Preparing to unpack .../1-procps_2%3a4.0.4-6_armhf.deb ... Unpacking procps (2:4.0.4-6) ... Selecting previously unselected package sensible-utils. 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Setting up libexpat1:armhf (2.6.4-1) ... Setting up python3.13-minimal (3.13.1-3) ... Selecting previously unselected package python3-minimal. (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 20143 files and directories currently installed.) Preparing to unpack .../0-python3-minimal_3.13.1-2_armhf.deb ... Unpacking python3-minimal (3.13.1-2) ... Selecting previously unselected package media-types. Preparing to unpack .../1-media-types_10.1.0_all.deb ... Unpacking media-types (10.1.0) ... Selecting previously unselected package netbase. Preparing to unpack .../2-netbase_6.4_all.deb ... Unpacking netbase (6.4) ... Selecting previously unselected package tzdata. Preparing to unpack .../3-tzdata_2024b-5_all.deb ... Unpacking tzdata (2024b-5) ... Selecting previously unselected package libffi8:armhf. Preparing to unpack .../4-libffi8_3.4.6-1_armhf.deb ... Unpacking libffi8:armhf (3.4.6-1) ... Selecting previously unselected package readline-common. Preparing to unpack .../5-readline-common_8.2-6_all.deb ... Unpacking readline-common (8.2-6) ... Selecting previously unselected package libreadline8t64:armhf. Preparing to unpack .../6-libreadline8t64_8.2-6_armhf.deb ... Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8 to /lib/arm-linux-gnueabihf/libhistory.so.8.usr-is-merged by libreadline8t64' Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8.2 to /lib/arm-linux-gnueabihf/libhistory.so.8.2.usr-is-merged by libreadline8t64' Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8 to /lib/arm-linux-gnueabihf/libreadline.so.8.usr-is-merged by libreadline8t64' Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8.2 to /lib/arm-linux-gnueabihf/libreadline.so.8.2.usr-is-merged by libreadline8t64' Unpacking libreadline8t64:armhf (8.2-6) ... Selecting previously unselected package libpython3.13-stdlib:armhf. Preparing to unpack .../7-libpython3.13-stdlib_3.13.1-3_armhf.deb ... Unpacking libpython3.13-stdlib:armhf (3.13.1-3) ... Selecting previously unselected package python3.13. Preparing to unpack .../8-python3.13_3.13.1-3_armhf.deb ... Unpacking python3.13 (3.13.1-3) ... 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Setting up dh-python (6.20250108) ... Setting up python3-scipy (1.14.1-3) ... Setting up pybuild-plugin-pyproject (6.20250108) ... Setting up python3-sklearn-lib:armhf (1.4.2+dfsg-7) ... Setting up python3-h5py-serial (3.12.1-1+b1) ... Setting up python3-ipywidgets (8.1.5-3) ... /usr/lib/python3/dist-packages/ipywidgets/widgets/tests/test_traits.py:61: SyntaxWarning: invalid escape sequence '\.' 'var(--my-color-\.)', # CSS variable with escaped characters /usr/lib/python3/dist-packages/ipywidgets/widgets/tests/test_traits.py:61: SyntaxWarning: invalid escape sequence '\.' 'var(--my-color-\.)', # CSS variable with escaped characters Setting up python3-debugpy (1.8.8+ds-2) ... Setting up python3-ipykernel (6.29.5-2) ... Setting up python3-h5py (3.12.1-1) ... Setting up python3-sklearn (1.4.2+dfsg-7) ... Setting up python3-fonttools (4.55.0-3) ... Setting up python3-ufolib2 (0.17.0+dfsg1-1) ... Setting up python3-matplotlib (3.8.3-3+b1) ... Setting up python3-ase (3.23.0-1) ... Setting up python3-sidpy (0.12.3-3) ... Processing triggers for libc-bin (2.40-5) ... Processing triggers for ca-certificates (20241223) ... Updating certificates in /etc/ssl/certs... 0 added, 0 removed; done. Running hooks in /etc/ca-certificates/update.d... done. Reading package lists... Building dependency tree... Reading state information... Reading extended state information... Initializing package states... Writing extended state information... Building tag database... -> Finished parsing the build-deps I: Building the package I: Running cd /build/reproducible-path/pyusid-0.0.12/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-genchanges -S > ../pyusid_0.0.12-2_source.changes dpkg-buildpackage: info: source package pyusid dpkg-buildpackage: info: source version 0.0.12-2 dpkg-buildpackage: info: source distribution unstable dpkg-buildpackage: info: source changed by Roland Mas dpkg-source --before-build . dpkg-buildpackage: info: host architecture armhf dpkg-source: info: using options from pyusid-0.0.12/debian/source/options: --extend-diff-ignore=^[^/]+.(egg-info|dist-info)/ debian/rules clean dh clean --with python3 --buildsystem=pybuild dh_auto_clean -O--buildsystem=pybuild dh_autoreconf_clean -O--buildsystem=pybuild dh_clean -O--buildsystem=pybuild debian/rules binary dh binary --with python3 --buildsystem=pybuild dh_update_autotools_config -O--buildsystem=pybuild dh_autoreconf -O--buildsystem=pybuild dh_auto_configure -O--buildsystem=pybuild dh_auto_build -O--buildsystem=pybuild I: pybuild plugin_pyproject:129: Building wheel for python3.12 with "build" module I: pybuild base:311: python3.12 -m build --skip-dependency-check --no-isolation --wheel --outdir /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid * Building wheel... /usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'tests_require' warnings.warn(msg) /usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite' warnings.warn(msg) running bdist_wheel /usr/lib/python3/dist-packages/setuptools/_distutils/cmd.py:111: SetuptoolsDeprecationWarning: bdist_wheel.universal is deprecated !! ******************************************************************************** With Python 2.7 end-of-life, support for building universal wheels (i.e., wheels that support both Python 2 and Python 3) is being obviated. Please discontinue using this option, or if you still need it, file an issue with pypa/setuptools describing your use case. By 2025-Aug-30, you need to update your project and remove deprecated calls or your builds will no longer be supported. ******************************************************************************** !! self.finalize_options() running build running build_py creating build/lib/pyUSID copying pyUSID/__version__.py -> build/lib/pyUSID copying pyUSID/__init__.py -> build/lib/pyUSID creating build/lib/pyUSID/processing copying pyUSID/processing/comp_utils.py -> build/lib/pyUSID/processing copying pyUSID/processing/__init__.py -> build/lib/pyUSID/processing copying pyUSID/processing/process.py -> build/lib/pyUSID/processing creating build/lib/pyUSID/io copying pyUSID/io/usi_data.py -> build/lib/pyUSID/io copying pyUSID/io/anc_build_utils.py -> build/lib/pyUSID/io copying pyUSID/io/__init__.py -> build/lib/pyUSID/io copying pyUSID/io/reg_ref.py -> build/lib/pyUSID/io copying pyUSID/io/image.py -> build/lib/pyUSID/io copying pyUSID/io/dimension.py -> build/lib/pyUSID/io copying pyUSID/io/array_translator.py -> build/lib/pyUSID/io creating build/lib/pyUSID/io/hdf_utils copying pyUSID/io/hdf_utils/model.py -> build/lib/pyUSID/io/hdf_utils copying pyUSID/io/hdf_utils/base.py -> build/lib/pyUSID/io/hdf_utils copying pyUSID/io/hdf_utils/simple.py -> build/lib/pyUSID/io/hdf_utils copying pyUSID/io/hdf_utils/__init__.py -> build/lib/pyUSID/io/hdf_utils running egg_info creating pyUSID.egg-info writing pyUSID.egg-info/PKG-INFO writing dependency_links to pyUSID.egg-info/dependency_links.txt writing requirements to pyUSID.egg-info/requires.txt writing top-level names to pyUSID.egg-info/top_level.txt writing manifest file 'pyUSID.egg-info/SOURCES.txt' reading manifest file 'pyUSID.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*' found under directory 'tests' adding license file 'LICENSE' writing manifest file 'pyUSID.egg-info/SOURCES.txt' installing to build/bdist.linux-armv7l/wheel running install running install_lib creating build/bdist.linux-armv7l/wheel creating build/bdist.linux-armv7l/wheel/pyUSID creating build/bdist.linux-armv7l/wheel/pyUSID/processing copying build/lib/pyUSID/processing/comp_utils.py -> build/bdist.linux-armv7l/wheel/./pyUSID/processing copying build/lib/pyUSID/processing/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID/processing copying build/lib/pyUSID/processing/process.py -> build/bdist.linux-armv7l/wheel/./pyUSID/processing copying build/lib/pyUSID/__version__.py -> build/bdist.linux-armv7l/wheel/./pyUSID creating build/bdist.linux-armv7l/wheel/pyUSID/io copying build/lib/pyUSID/io/usi_data.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io creating build/bdist.linux-armv7l/wheel/pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/model.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/base.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/simple.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/anc_build_utils.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/reg_ref.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/image.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/dimension.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/array_translator.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID running install_egg_info Copying pyUSID.egg-info to build/bdist.linux-armv7l/wheel/./pyUSID-0.0.12.egg-info running install_scripts creating build/bdist.linux-armv7l/wheel/pyUSID-0.0.12.dist-info/WHEEL creating '/build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/.tmp-p4yvv1zi/pyUSID-0.0.12-py2.py3-none-any.whl' and adding 'build/bdist.linux-armv7l/wheel' to it adding 'pyUSID/__init__.py' adding 'pyUSID/__version__.py' adding 'pyUSID/io/__init__.py' adding 'pyUSID/io/anc_build_utils.py' adding 'pyUSID/io/array_translator.py' adding 'pyUSID/io/dimension.py' adding 'pyUSID/io/image.py' adding 'pyUSID/io/reg_ref.py' adding 'pyUSID/io/usi_data.py' adding 'pyUSID/io/hdf_utils/__init__.py' adding 'pyUSID/io/hdf_utils/base.py' adding 'pyUSID/io/hdf_utils/model.py' adding 'pyUSID/io/hdf_utils/simple.py' adding 'pyUSID/processing/__init__.py' adding 'pyUSID/processing/comp_utils.py' adding 'pyUSID/processing/process.py' adding 'pyUSID-0.0.12.dist-info/LICENSE' adding 'pyUSID-0.0.12.dist-info/METADATA' adding 'pyUSID-0.0.12.dist-info/WHEEL' adding 'pyUSID-0.0.12.dist-info/top_level.txt' adding 'pyUSID-0.0.12.dist-info/RECORD' removing build/bdist.linux-armv7l/wheel Successfully built pyUSID-0.0.12-py2.py3-none-any.whl I: pybuild plugin_pyproject:144: Unpacking wheel built for python3.12 with "installer" module I: pybuild plugin_pyproject:129: Building wheel for python3.13 with "build" module I: pybuild base:311: python3.13 -m build --skip-dependency-check --no-isolation --wheel --outdir /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid * Building wheel... /usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'tests_require' warnings.warn(msg) /usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite' warnings.warn(msg) running bdist_wheel /usr/lib/python3/dist-packages/setuptools/_distutils/cmd.py:111: SetuptoolsDeprecationWarning: bdist_wheel.universal is deprecated !! ******************************************************************************** With Python 2.7 end-of-life, support for building universal wheels (i.e., wheels that support both Python 2 and Python 3) is being obviated. Please discontinue using this option, or if you still need it, file an issue with pypa/setuptools describing your use case. By 2025-Aug-30, you need to update your project and remove deprecated calls or your builds will no longer be supported. ******************************************************************************** !! self.finalize_options() running build running build_py running egg_info writing pyUSID.egg-info/PKG-INFO writing dependency_links to pyUSID.egg-info/dependency_links.txt writing requirements to pyUSID.egg-info/requires.txt writing top-level names to pyUSID.egg-info/top_level.txt reading manifest file 'pyUSID.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*' found under directory 'tests' adding license file 'LICENSE' writing manifest file 'pyUSID.egg-info/SOURCES.txt' installing to build/bdist.linux-armv7l/wheel running install running install_lib creating build/bdist.linux-armv7l/wheel creating build/bdist.linux-armv7l/wheel/pyUSID creating build/bdist.linux-armv7l/wheel/pyUSID/processing copying build/lib/pyUSID/processing/comp_utils.py -> build/bdist.linux-armv7l/wheel/./pyUSID/processing copying build/lib/pyUSID/processing/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID/processing copying build/lib/pyUSID/processing/process.py -> build/bdist.linux-armv7l/wheel/./pyUSID/processing copying build/lib/pyUSID/__version__.py -> build/bdist.linux-armv7l/wheel/./pyUSID creating build/bdist.linux-armv7l/wheel/pyUSID/io copying build/lib/pyUSID/io/usi_data.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io creating build/bdist.linux-armv7l/wheel/pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/model.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/base.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/simple.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/hdf_utils/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io/hdf_utils copying build/lib/pyUSID/io/anc_build_utils.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/reg_ref.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/image.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/dimension.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/io/array_translator.py -> build/bdist.linux-armv7l/wheel/./pyUSID/io copying build/lib/pyUSID/__init__.py -> build/bdist.linux-armv7l/wheel/./pyUSID running install_egg_info Copying pyUSID.egg-info to build/bdist.linux-armv7l/wheel/./pyUSID-0.0.12.egg-info running install_scripts creating build/bdist.linux-armv7l/wheel/pyUSID-0.0.12.dist-info/WHEEL creating '/build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/.tmp-pvklggkb/pyUSID-0.0.12-py2.py3-none-any.whl' and adding 'build/bdist.linux-armv7l/wheel' to it adding 'pyUSID/__init__.py' adding 'pyUSID/__version__.py' adding 'pyUSID/io/__init__.py' adding 'pyUSID/io/anc_build_utils.py' adding 'pyUSID/io/array_translator.py' adding 'pyUSID/io/dimension.py' adding 'pyUSID/io/image.py' adding 'pyUSID/io/reg_ref.py' adding 'pyUSID/io/usi_data.py' adding 'pyUSID/io/hdf_utils/__init__.py' adding 'pyUSID/io/hdf_utils/base.py' adding 'pyUSID/io/hdf_utils/model.py' adding 'pyUSID/io/hdf_utils/simple.py' adding 'pyUSID/processing/__init__.py' adding 'pyUSID/processing/comp_utils.py' adding 'pyUSID/processing/process.py' adding 'pyUSID-0.0.12.dist-info/LICENSE' adding 'pyUSID-0.0.12.dist-info/METADATA' adding 'pyUSID-0.0.12.dist-info/WHEEL' adding 'pyUSID-0.0.12.dist-info/top_level.txt' adding 'pyUSID-0.0.12.dist-info/RECORD' removing build/bdist.linux-armv7l/wheel Successfully built pyUSID-0.0.12-py2.py3-none-any.whl I: pybuild plugin_pyproject:144: Unpacking wheel built for python3.13 with "installer" module dh_auto_test -O--buildsystem=pybuild I: pybuild base:311: cd /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build; python3.12 -m unittest discover -v test_legal (tests.io.hdf_utils.test_base.TestFindDataset.test_legal) ... ok test_many (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_many) ... ok test_no_hdf5_datasets (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_no_hdf5_datasets) ... ok test_non_string_names (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_non_string_names) ... ok test_same_name (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_same_name) ... ok test_single (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_single) ... ok test_root_all_dsets (tests.io.hdf_utils.test_base.TestPrintTreeBEPS.test_root_all_dsets) ... ok test_root_main_dsets_only (tests.io.hdf_utils.test_base.TestPrintTreeBEPS.test_root_main_dsets_only) ... ok test_multi_level_rel_paths (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_rel_paths) ... ok test_multi_level_rel_paths_grp_b (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_rel_paths_grp_b) ... ok test_multi_level_tree (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree) ... ok test_multi_level_tree_grp_a (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree_grp_a) ... ok test_multi_level_tree_grp_b (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree_grp_b) ... ok test_multi_level_tree_main_dsets_only (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree_main_dsets_only) ... ok test_not_a_group (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_not_a_group) ... ok test_single_level_rel_paths (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_single_level_rel_paths) ... ok test_single_level_tree (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_single_level_tree) ... ok test_dset (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_dset) ... ok test_file (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_file) ... ok test_group (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_group) ... ok test_invalid (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_invalid) ... ok test_invalid_sort (tests.io.hdf_utils.test_model.TestGetDimensionality.test_invalid_sort) ... ok test_legal_no_sort (tests.io.hdf_utils.test_model.TestGetDimensionality.test_legal_no_sort) ... ok test_legal_w_sort (tests.io.hdf_utils.test_model.TestGetDimensionality.test_legal_w_sort) ... ok test_not_hdf_dset (tests.io.hdf_utils.test_model.TestGetDimensionality.test_not_hdf_dset) ... ok test_invalid_types (tests.io.hdf_utils.test_model.TestGetSortOrder.test_invalid_types) ... ok test_reversed (tests.io.hdf_utils.test_model.TestGetSortOrder.test_reversed) ... ok test_simple (tests.io.hdf_utils.test_model.TestGetSortOrder.test_simple) ... ok test_all_dim_names_not_provided (tests.io.hdf_utils.test_model.TestGetUnitValues.test_all_dim_names_not_provided) ... ok test_dependent_dim (tests.io.hdf_utils.test_model.TestGetUnitValues.test_dependent_dim) ... ok test_illegal_dset (tests.io.hdf_utils.test_model.TestGetUnitValues.test_illegal_dset) ... ok test_illegal_key (tests.io.hdf_utils.test_model.TestGetUnitValues.test_illegal_key) ... ok test_incomp_dim_no_attr (tests.io.hdf_utils.test_model.TestGetUnitValues.test_incomp_dim_no_attr) ... ok test_source_pos_all (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_pos_all) ... ok test_source_pos_single (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_pos_single) ... ok test_source_spec_all (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_spec_all) ... ok test_source_spec_all_explicit (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_spec_all_explicit) ... ok test_source_spec_single (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_spec_single) ... ok test_sparse_samp_no_attr (tests.io.hdf_utils.test_model.TestGetUnitValues.test_sparse_samp_no_attr) ... ok test_sparse_samp_w_attr (tests.io.hdf_utils.test_model.TestGetUnitValues.test_sparse_samp_w_attr) ... ok test_pos_and_spec_may_may_not_be_provided (tests.io.hdf_utils.test_model.TestReshapeFromNDims.test_pos_and_spec_may_may_not_be_provided) ... ok test_pos_and_spec_provided (tests.io.hdf_utils.test_model.TestReshapeFromNDims.test_pos_and_spec_provided) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_model.py:788: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(source_nd, h5_pos=source_pos_data, h5_spec=source_spec_data) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_model.py:799: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(main_pos_sorted, h5_pos=source_pos_data, /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_model.py:811: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(main_spec_sorted, h5_pos=source_pos_data, /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_model.py:823: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(main_not_sorted, h5_pos=source_pos_data, ok test_dask_all (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all) ... ok test_dask_all_both_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all_both_inds_order_flipped) ... ok test_dask_all_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all_ordinary_1d_pos) ... ok test_dask_all_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all_ordinary_1d_spec) ... ok test_dask_in_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_in_ordinary_1d_pos) ... ok test_dask_in_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_in_ordinary_1d_spec) ... ok test_dask_input (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_input) ... ok test_dask_out_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_out_ordinary_1d_pos) ... ok test_dask_out_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_out_ordinary_1d_spec) ... 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [120 56] [[120] [ 56]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [134 214] [[134 214]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [49 20] [[49] [20]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [ 76 107] [[ 76 107]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [ 60 152] [[ 60] [152]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [244 132] [[244 132]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2]ok test_dask_output (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_output) ... ok test_h5_already_sorted (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_already_sorted) ... ok test_h5_beps_field (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_beps_field) ... ok test_h5_both_inds_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_both_inds_flipped) ... ok test_h5_manually_provided_anc_dsets_h5 (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_manually_provided_anc_dsets_h5) ... ok test_h5_not_main_dset (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_not_main_dset) ... ok test_numpy_both_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_both_inds_order_flipped) ... ok test_numpy_ordinary (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary) ... ok test_numpy_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_1d_pos) ... ok test_numpy_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_1d_spec) ... ok test_numpy_ordinary_2d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_2d_pos) ... ok test_numpy_ordinary_2d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_2d_spec) ... After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [204 48] [[204] [ 48]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [241 249] [[241 249]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) 2D: True, Spec: False, Flip: False Main data shapes ND: (2, 3), 2D: (6, 1) [[192 175 162] [250 142 53]] [[192] [175] [162] [250] [142] [ 53]] Position dimensions: ['Position Dimension 0' 'Position Dimension 1'] Position sort order: [0 1] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0' 'Position Dimension 1'] Position dimensionality (sort applied): [3, 2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (6, 1) to [2, 3, 1] After reshaping, labels are ['Position Dimension 1' 'Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 3, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0' 'Position Dimension 1'] Position sort order: [0 1] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0' 'Position Dimension 1'] Position dimensionality (sort applied): [3, 2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (6, 1) to [2, 3, 1] After reshaping, labels are ['Position Dimension 1' 'Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 3, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 2] New labels ordering: ['Position Dimension 0' 'Position Dimension 1' 'Spectral Dimension 0'] Dataset now of shape: (3, 2, 1) 2D: True, Spec: True, Flip: False Main data shapes ND: (2, 3), 2D: (1, 6) [[197 40 7] [151 17 15]] [[197 40 7 151 17 15]] Position dimensions: ['Position Dimension 0'] Position sort order:ok test_numpy_pos_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_pos_inds_order_flipped) ... ok test_numpy_spec_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_spec_inds_order_flipped) ... ok test_dask_small (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_dask_small) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_empty_main (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_empty_main) ... ok test_numpy_small (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_numpy_small) ... ok test_prod_sizes_mismatch (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_prod_sizes_mismatch) ... ok test_write_main_both_existing_aux (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_both_existing_aux) ... ok test_write_main_both_existing_aux_diff_file (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_both_existing_aux_diff_file) ... ok test_write_main_existing_pos_aux (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_pos_aux) ... [0] Spectroscopic Dimensions: ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic dimensionality (sort applied): [3, 2] Will attempt to reshape main dataset from: (1, 6) to [1, 2, 3] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 1' 'Spectral Dimension 0'] Data shape is (1, 2, 3) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic dimensionality (sort applied): [3, 2] Will attempt to reshape main dataset from: (1, 6) to [1, 2, 3] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 1' 'Spectral Dimension 0'] Data shape is (1, 2, 3) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 2 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0' 'Spectral Dimension 1'] Dataset now of shape: (1, 3, 2) h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created empty dataset: for writing Dask dataset: dask.array Dask array will be written to HDF5 dataset: "/Test_Main" in file: "test.h5" Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Selected empty dataset creation. OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created empty dataset for Main Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK The shapes of the provided h5 position indices and values are OK Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Position_Indices at: with kwargs: {'shape': (15, 2), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Position_Values at: with kwargs: {'shape': (15, 2), 'dtype': dtype(' to destination dataset: The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Spectroscopic_Indices at: with kwargs: {'shape': (2, 14), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Spectroscopic_Values at: with kwargs: {'shape': (2, 14), 'dtype': dtype(' to destination dataset: Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OKok test_write_main_existing_pos_aux_diff_file (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_pos_aux_diff_file) ... ok test_write_main_existing_spec_aux (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_spec_aux) ... ok test_write_main_existing_spec_aux_diff_file (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_spec_aux_diff_file) ... ok test_existing (tests.io.hdf_utils.test_simple.TestAssignGroupIndex.test_existing) ... ok test_invalid_dtypes (tests.io.hdf_utils.test_simple.TestAssignGroupIndex.test_invalid_dtypes) ... ok test_new (tests.io.hdf_utils.test_simple.TestAssignGroupIndex.test_new) ... ok test_h5_main_non_dset_anc_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_h5_main_non_dset_anc_names) ... ok test_h5_main_not_dset (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_h5_main_not_dset) ... ok test_linking_main_legit_anc_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_linking_main_legit_anc_names) ... ok test_many_objs_to_many_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_many_objs_to_many_names) ... ok test_no_anc_refs_provided (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_no_anc_refs_provided) ... ok test_not_dset (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_not_dset) ... ok test_objs_and_refs_to_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_objs_and_refs_to_names) ... ok test_one_dset_to_name (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_one_dset_to_name) ... ok test_too_many_ancs (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_too_many_ancs) ... ok test_too_many_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_too_many_names) ... ok test_dset_matching_attrs (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_dset_matching_attrs) ... ok test_dset_no_attrs (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_dset_no_attrs) ... ok test_dset_one_mismatched_attrs (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_dset_one_mismatched_attrs) ... ok test_grp (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp) ... ok test_grp_mismatched_types_01 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_01) ... ok test_grp_mismatched_types_02 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_02) ... ok test_grp_mismatched_types_03 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_03) ... ok test_grp_mismatched_types_04 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_04) ... ok test_exact_match (tests.io.hdf_utils.test_simple.TestCheckForOld.test_exact_match) ... ok test_exact_match_02 (tests.io.hdf_utils.test_simple.TestCheckForOld.test_exact_match_02) ... ok test_fail_01 (tests.io.hdf_utils.test_simple.TestCheckForOld.test_fail_01) ... ok test_fail_02 (tests.io.hdf_utils.test_simple.TestCheckForOld.test_fail_02) ... ok test_invalid_target_dset (tests.io.hdf_utils.test_simple.TestCheckForOld.test_invalid_target_dset) ... ok test_invalid_types (tests.io.hdf_utils.test_simple.TestCheckForOld.test_invalid_types) ... ok test_subset_but_match (tests.io.hdf_utils.test_simple.TestCheckForOld.test_subset_but_match) ... ok test_valid_target_dset (tests.io.hdf_utils.test_simple.TestCheckForOld.test_valid_target_dset) ... ok test_anc_not_dsets (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_anc_not_dsets) ... ok test_anc_shapes_not_matching (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_anc_shapes_not_matching) ... ok test_illegal_01 (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_illegal_01) ... ok test_invalid_str_attrs (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_invalid_str_attrs) ... ok test_legal (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_legal) ... ok test_missing_str_attrs (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_missing_str_attrs) ... ok test_no_main_attrs (tests.io.hdf_utils.test_simple.TestCopyMainAttributes.test_no_main_attrs) ... ok test_valid (tests.io.hdf_utils.test_simple.TestCopyMainAttributes.test_valid) ... ok test_wrong_objects (tests.io.hdf_utils.test_simple.TestCopyMainAttributes.test_wrong_objects) ... ok test_diff_file_new_attrs_linked_dsets (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_diff_file_new_attrs_linked_dsets) ... ok test_diff_groups (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_diff_groups) ... ok test_existing_dset_name (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_existing_dset_name) ... ok test_invalid_types (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_invalid_types) ... ok test_same_group_new_attrs (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_same_group_new_attrs) ... ok test_create_indexed_group_invalid_types (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_create_indexed_group_invalid_types) ... ok test_empty_base_name (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_empty_base_name) ... ok test_first_group (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_first_group) ... ok test_second (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_second) ... ok test_w_suffix_ (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_w_suffix_) ... ok test_dash_in_name (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_dash_in_name) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:778: UserWarning: tool_name should not contain the "-" character. Reformatted name from:Some-Tool to Some_Tool warn('tool_name should not contain the "-" character. Reformatted name from:{} to ' ok test_different_file (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_different_file) ... ok test_empty_tool_name (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_empty_tool_name) ... ok test_first (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_first) ... ok test_invalid_types (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_invalid_types) ... ok test_second (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_second) ... ok test_legal (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_legal) ... ok test_no_dset (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_no_dset) ... ok test_no_such_tool (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_no_such_tool) ... ok test_not_string (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_not_string) ... ok test_results_in_diff_file (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_results_in_diff_file) ... ok test_results_in_diff_file_invalid_type (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_results_in_diff_file_invalid_type) ... ok test_invalid_type (tests.io.hdf_utils.test_simple.TestGetAllMain.test_invalid_type) ... ok test_legal (tests.io.hdf_utils.test_simple.TestGetAllMain.test_legal) ... ok test_illegal (tests.io.hdf_utils.test_simple.TestGetSourceDataset.test_illegal) ... ok test_invalid_type (tests.io.hdf_utils.test_simple.TestGetSourceDataset.test_invalid_type) ... ok test_legal (tests.io.hdf_utils.test_simple.TestGetSourceDataset.test_legal) ... ok test_anc_args_not_h5_dset (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_anc_args_not_h5_dset) ... ok test_ind_vals_not_same_shape (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_ind_vals_not_same_shape) ... ok test_pos_args_not_h5_dset (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_pos_args_not_h5_dset) ... ok test_typical_attrs_not_specified (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_typical_attrs_not_specified) ... ok test_typical_attrs_specified (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_typical_attrs_specified) ... ok test_blah (tests.io.hdf_utils.test_simple.TestValidateAncDsetAttrs.test_blah) ... ok test_invalid_dtypes (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_invalid_dtypes) ... ok test_invalid_spec_pos_bool (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_invalid_spec_pos_bool) ... ok test_mismatched_anc_shapes (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_mismatched_anc_shapes) ... ok test_valid_shapes (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_valid_shapes) ... ok test_invalid_dims (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_invalid_dims) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_simple.py:1536: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((1, 25), [Dimension('a', 'b', 5), /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_simple.py:1540: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((25, 5), [Dimension('a', 'b', 5)], ok test_invalid_dtypes (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_invalid_dtypes) ... ok test_invalid_main_shape (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_invalid_main_shape) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_simple.py:1553: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func([-5, 1], Dimension('a', 'b', 5)) ok test_multi_dims (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_multi_dims) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_simple.py:1526: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((1, 15), [Dimension('a', 'b', 5), /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_simple.py:1529: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((15, 5), [Dimension('a', 'b', 5), ok test_single_dim (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_single_dim) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_simple.py:1519: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((1, 5), Dimension('blah', 'meh', np.arange(5)), /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/tests/io/hdf_utils/test_simple.py:1521: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((5, 1), [Dimension('blah', 'meh', np.arange(5))], ok test_illegal (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_illegal) ... ok test_legal_bare_minimum_pos_f2s (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_pos_f2s) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_legal_bare_minimum_pos_s2f (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_pos_s2f) ... ok test_legal_bare_minimum_spec_f2s (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_spec_f2s) ... ok test_legal_bare_minimum_spec_s2f (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_spec_s2f) ... ok test_legal_override_steps_offsets_base_name (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_override_steps_offsets_base_name) ... ok test_3d_to_1d_pos_fastest_n_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_pos_fastest_n_slowest) ... ok test_3d_to_1d_spec_fastest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_spec_fastest) ... ok test_3d_to_1d_spec_fastest_n_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_spec_fastest_n_slowest) ... ok test_3d_to_1d_spec_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_spec_slowest) ... ok test_3d_to_2d_spec_fastest_n_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_2d_spec_fastest_n_slowest) ... ok test_spec_1d_to_0d (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_spec_1d_to_0d) ... ok test_spec_2d_to_1d (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_spec_2d_to_1d) ... ok test_spec_2d_to_1d_new_file (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_spec_2d_to_1d_new_file) ... ok test_all_dsets_as_dask (tests.io.test_array_translator.TestBaseOperations.test_all_dsets_as_dask) ... /usr/lib/python3/dist-packages/sidpy/sid/translator.py:42: FutureWarning: Consider using sidpy.Reader instead of sidpy.Translator if possible and contribute your reader to ScopeReaders warn('Consider using sidpy.Reader instead of sidpy.Translator if ' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/array_translator.py:93: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(raw_data.shape, dimensions, dim_name == 'Spectroscopic') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_preexisting_file_OK (tests.io.test_array_translator.TestBaseOperations.test_preexisting_file_OK) ... /usr/lib/python3/dist-packages/sidpy/sid/translator.py:42: FutureWarning: Consider using sidpy.Reader instead of sidpy.Translator if possible and contribute your reader to ScopeReaders warn('Consider using sidpy.Reader instead of sidpy.Translator if ' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/array_translator.py:93: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(raw_data.shape, dimensions, dim_name == 'Spectroscopic') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_quick_dask_main_translation (tests.io.test_array_translator.TestBaseOperations.test_quick_dask_main_translation) ... ok test_quick_numpy_tranlsation_plus_parms (tests.io.test_array_translator.TestBaseOperations.test_quick_numpy_tranlsation_plus_parms) ... /usr/lib/python3/dist-packages/sidpy/sid/translator.py:42: FutureWarning: Consider using sidpy.Reader instead of sidpy.Translator if possible and contribute your reader to ScopeReaders warn('Consider using sidpy.Reader instead of sidpy.Translator if ' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/array_translator.py:93: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(raw_data.shape, dimensions, dim_name == 'Spectroscopic') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_quick_numpy_translation (tests.io.test_array_translator.TestBaseOperations.test_quick_numpy_translation) ... ok test_empty_name (tests.io.test_array_translator.TestExtraDatasets.test_empty_name) ... ok test_not_arrays (tests.io.test_array_translator.TestExtraDatasets.test_not_arrays) ... ok test_not_dicts (tests.io.test_array_translator.TestExtraDatasets.test_not_dicts) ... ok test_not_str_names (tests.io.test_array_translator.TestExtraDatasets.test_not_str_names) ... ok test_reserved_names (tests.io.test_array_translator.TestExtraDatasets.test_reserved_names) ... ok test_object_single (tests.io.test_array_translator.TestIllegalDimensions.test_object_single) ... ok test_objects (tests.io.test_array_translator.TestIllegalDimensions.test_objects) ... ok test_position (tests.io.test_array_translator.TestIllegalDimensions.test_position) ... ok test_spec (tests.io.test_array_translator.TestIllegalDimensions.test_spec) ... ok test_main_dset_1D (tests.io.test_array_translator.TestIllegalMainDataset.test_main_dset_1D) ... ok test_main_dset_2D (tests.io.test_array_translator.TestIllegalMainDataset.test_main_dset_2D) ... ok test_not_numpy_or_dask_array_main (tests.io.test_array_translator.TestIllegalMainDataset.test_not_numpy_or_dask_array_main) ... ok test_not_strings (tests.io.test_array_translator.TestIllegalStringParms.test_not_strings) ... ok test_dim_type_invalid_comparison (tests.io.test_dimension.TestDimType.test_dim_type_invalid_comparison) ... ok test_dim_type_valid_comparison (tests.io.test_dimension.TestDimType.test_dim_type_valid_comparison) ... ok test_default_mode (tests.io.test_dimension.TestDimension.test_default_mode) ... ok test_equality (tests.io.test_dimension.TestDimension.test_equality) ... ok test_illegal_instantiation (tests.io.test_dimension.TestDimension.test_illegal_instantiation) ... ok test_inequality (tests.io.test_dimension.TestDimension.test_inequality) ... ok test_invalid_mode (tests.io.test_dimension.TestDimension.test_invalid_mode) ... ok test_repr (tests.io.test_dimension.TestDimension.test_repr) ... ok test_values_as_array (tests.io.test_dimension.TestDimension.test_values_as_array) ... ok test_values_as_length (tests.io.test_dimension.TestDimension.test_values_as_length) ... ok test_basic_translate (tests.io.test_image_translator.TestBinning.test_basic_translate) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/image.py:48: FutureWarning: pyUSID.ImageTranslator will be removed shortly. Consider using SciFiReaders.ImageReader instead warn("pyUSID.ImageTranslator will be removed shortly. Consider using " ok test_custom_interp (tests.io.test_image_translator.TestBinning.test_custom_interp) ... ok test_float_parms (tests.io.test_image_translator.TestBinning.test_float_parms) ... ok test_invalid_dtype (tests.io.test_image_translator.TestBinning.test_invalid_dtype) ... ok test_invalid_interp (tests.io.test_image_translator.TestBinning.test_invalid_interp) ... ok test_neg_parms (tests.io.test_image_translator.TestBinning.test_neg_parms) ... ok test_single_default_interp (tests.io.test_image_translator.TestBinning.test_single_default_interp) ... ok test_too_many_dims (tests.io.test_image_translator.TestBinning.test_too_many_dims) ... ok test_tuple_default_interp (tests.io.test_image_translator.TestBinning.test_tuple_default_interp) ... ok test_basic_translate (tests.io.test_image_translator.TestFile.test_basic_translate) ... ok test_invalid_h5_path (tests.io.test_image_translator.TestFile.test_invalid_h5_path) ... ok test_output_h5_file_already_exists (tests.io.test_image_translator.TestFile.test_output_h5_file_already_exists) ... ok test_path_does_not_exist (tests.io.test_image_translator.TestFile.test_path_does_not_exist) ... ok test_path_not_str (tests.io.test_image_translator.TestFile.test_path_not_str) ... ok test_valid_h5_path (tests.io.test_image_translator.TestFile.test_valid_h5_path) ... ok test_basic_translate (tests.io.test_image_translator.TestImageTranslator.test_basic_translate) ... ok test_basic_translate (tests.io.test_image_translator.TestNormalization.test_basic_translate) ... ok test_normalize_and_default_interp (tests.io.test_image_translator.TestNormalization.test_normalize_and_default_interp) ... ok test_normalize_only (tests.io.test_image_translator.TestNormalization.test_normalize_only) ... ok test_color (tests.io.test_image_translator.TestReadImage.test_color) ... ok test_color_to_bw_image (tests.io.test_image_translator.TestReadImage.test_color_to_bw_image) ... ok test_text_complex_to_pillow (tests.io.test_image_translator.TestReadImage.test_text_complex_to_pillow) ... ok test_text_to_numpy_complex (tests.io.test_image_translator.TestReadImage.test_text_to_numpy_complex) ... ok test_text_to_numpy_simple (tests.io.test_image_translator.TestReadImage.test_text_to_numpy_simple) ... ok test_to_numpy (tests.io.test_image_translator.TestReadImage.test_to_numpy) ... ok test_to_pillow (tests.io.test_image_translator.TestReadImage.test_to_pillow) ... ok test_copy_reg_ref_reduced_dim (tests.io.test_reg_ref.TestRegRef.test_copy_reg_ref_reduced_dim) ... ok test_correct_USIDataset (tests.io.test_usi_dataset.TestEquality.test_correct_USIDataset) ... ok test_correct_h5_dataset (tests.io.test_usi_dataset.TestEquality.test_correct_h5_dataset) ... ok test_incorrect_USIDataset (tests.io.test_usi_dataset.TestEquality.test_incorrect_USIDataset) ... ok test_incorrect_h5_dataset (tests.io.test_usi_dataset.TestEquality.test_incorrect_h5_dataset) ... ok test_incorrect_object (tests.io.test_usi_dataset.TestEquality.test_incorrect_object) ... ok test_all_dims_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_all_dims_sliced) ... ok test_both_pos_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_both_pos_dim_sliced) ... ok test_both_spec_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_both_spec_dim_sliced) ... ok test_empty (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_empty) ... ok test_one_pos_one_spec_dims_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_one_pos_one_spec_dims_sliced) ... ok test_single_pos_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_pos_dim_sliced) ... ok test_single_pos_dim_truncated (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_pos_dim_truncated) ... ok test_single_spec_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_spec_dim_sliced) ... ok test_single_spec_dim_truncated (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_spec_dim_truncated) ... ok test_sorted_and_unsorted (tests.io.test_usi_dataset.TestGetNDimFormExistsReal.test_sorted_and_unsorted) ... ok test_get_pos_values (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_pos_values) ... ok test_get_pos_values_illegal (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_pos_values_illegal) ... ok test_get_spec_values (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_spec_values) ... ok test_get_spec_values_illegal (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_spec_values_illegal) ... ok test_both_pos_removed (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_both_pos_removed) ... ok test_empty_dict (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_empty_dict) ... ok test_incorrect_type (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_incorrect_type) ... ok test_negative_index (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_negative_index) ... ok test_non_existent_dim (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_non_existent_dim) ... ok test_one_pos_dim_removed (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_one_pos_dim_removed) ... ok test_one_pos_dim_sliced (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_one_pos_dim_sliced) ... ok test_out_of_bounds (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_out_of_bounds) ... ok test_pos_and_spec_sliced_list (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_pos_and_spec_sliced_list) ... ok test_two_pos_dim_sliced (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_two_pos_dim_sliced) ... ok test_two_pos_dim_sliced_list (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_two_pos_dim_sliced_list) ... ok test_one_pos (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_one_pos) ... ok test_one_pos_one_spec (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_one_pos_one_spec) ... ok test_one_spec (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_one_spec) ... ok test_two_pos_simple (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_two_pos_simple) ... ok test_two_spec (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_two_spec) ... ok test_all_but_one_dims_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_2d_dask) ... ok test_all_but_one_dims_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_2d_numpy) ... ok test_all_but_one_dims_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_nd_dask) ... ok test_all_but_one_dims_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_nd_numpy) ... ok test_all_dims_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_2d_dask) ... ok test_all_dims_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_2d_numpy) ... ok test_all_dims_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_nd_dask) ... ok test_all_dims_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_nd_numpy) ... ok test_both_pos_removed_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_2d_dask) ... ok test_both_pos_removed_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_2d_numpy) ... ok test_both_pos_removed_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_nd_dask) ... ok test_both_pos_removed_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_nd_numpy) ... ok test_empty_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_empty_2d_dask) ... ok test_empty_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_empty_2d_numpy) ... ok test_empty_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_empty_nd_dask) ... ok test_empty_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_empty_nd_numpy) ... ok test_incorrect_type (tests.io.test_usi_dataset.TestSliceReal.test_incorrect_type) ... ok test_negative_index_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_negative_index_2d_numpy) ... ok test_negative_index_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_negative_index_nd_dask) ... ok test_negative_index_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_negative_index_nd_numpy) ... ok test_non_existent_dim (tests.io.test_usi_dataset.TestSliceReal.test_non_existent_dim) ... ok test_one_pos_dim_removed_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_2d_dask) ... ok test_one_pos_dim_removed_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_2d_numpy) ... ok test_one_pos_dim_removed_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_nd_dask) ... ok test_one_pos_dim_removed_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_nd_numpy) ... ok test_one_pos_dim_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_2d_dask) ... ok test_one_pos_dim_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_2d_numpy) ... ok test_one_pos_dim_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_nd_dask) ... ok test_one_pos_dim_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_nd_numpy) ... ok test_out_of_bounds (tests.io.test_usi_dataset.TestSliceReal.test_out_of_bounds) ... ok test_pos_and_spec_sliced_list_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_2d_dask) ... ok test_pos_and_spec_sliced_list_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_2d_numpy) ... ok test_pos_and_spec_sliced_list_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_nd_dask) ... ok test_pos_and_spec_sliced_list_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_nd_numpy) ... ok test_two_pos_dim_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_2d_dask) ... ok test_two_pos_dim_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_2d_numpy) ... ok test_two_pos_dim_sliced_list_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_2d_dask) ... ok test_two_pos_dim_sliced_list_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_2d_numpy) ... ok test_two_pos_dim_sliced_list_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_nd_dask) ... ok test_two_pos_dim_sliced_list_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_nd_numpy) ... ok test_two_pos_dim_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_nd_dask) ... ok test_two_pos_dim_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_nd_numpy) ... ok test_get_current_sorting (tests.io.test_usi_dataset.TestSortingReal.test_get_current_sorting) ... ok test_toggle_sorting (tests.io.test_usi_dataset.TestSortingReal.test_toggle_sorting) ... ok test_string_representation (tests.io.test_usi_dataset.TestStringRepr.test_string_representation) ... ok test_1D (tests.io.test_write_utils.TestBuildIndValMatrices.test_1D) ... ok test_1D_pos (tests.io.test_write_utils.TestBuildIndValMatrices.test_1D_pos) ... ok test_3D (tests.io.test_write_utils.TestBuildIndValMatrices.test_3D) ... ok test_empty (tests.io.test_write_utils.TestBuildIndValMatrices.test_empty) ... ok test_invalid_inputs (tests.io.test_write_utils.TestBuildIndValMatrices.test_invalid_inputs) ... ok test_invalid_types (tests.io.test_write_utils.TestCalcChunks.test_invalid_types) ... ok test_no_unit_chunk (tests.io.test_write_utils.TestCalcChunks.test_no_unit_chunk) ... ok test_no_unit_chunk_max_mem (tests.io.test_write_utils.TestCalcChunks.test_no_unit_chunk_max_mem) ... ok test_shape_mismatch (tests.io.test_write_utils.TestCalcChunks.test_shape_mismatch) ... ok test_unit_chunk (tests.io.test_write_utils.TestCalcChunks.test_unit_chunk) ... ok test_unit_chunk_max_mem (tests.io.test_write_utils.TestCalcChunks.test_unit_chunk_max_mem) ... ok test_unit_not_iterable (tests.io.test_write_utils.TestCalcChunks.test_unit_not_iterable) ... ok test_invalid_inputs (tests.io.test_write_utils.TestCreateSpecIndsFromVals.test_invalid_inputs) ... ok test_legal (tests.io.test_write_utils.TestCreateSpecIndsFromVals.test_legal) ... ok test_legal_multi_dim (tests.io.test_write_utils.TestGetAuxDsetSlicing.test_legal_multi_dim) ... ok test_legal_single_dim (tests.io.test_write_utils.TestGetAuxDsetSlicing.test_legal_single_dim) ... ok test_odd_input (tests.io.test_write_utils.TestGetAuxDsetSlicing.test_odd_input) ... ok test_2_dims (tests.io.test_write_utils.TestMakeIndicesMatrix.test_2_dims) ... ok test_3_dims (tests.io.test_write_utils.TestMakeIndicesMatrix.test_3_dims) ... ok test_dim_w_val_1 (tests.io.test_write_utils.TestMakeIndicesMatrix.test_dim_w_val_1) ... ok test_empty_list (tests.io.test_write_utils.TestMakeIndicesMatrix.test_empty_list) ... ok test_just_size_of_one_dim (tests.io.test_write_utils.TestMakeIndicesMatrix.test_just_size_of_one_dim) ... ok test_matrix_1_dims (tests.io.test_write_utils.TestMakeIndicesMatrix.test_matrix_1_dims) ... ok test_non_int_dim_sizes (tests.io.test_write_utils.TestMakeIndicesMatrix.test_non_int_dim_sizes) ... ok test_single_value_dimension_int_input (tests.io.test_write_utils.TestMakeIndicesMatrix.test_single_value_dimension_int_input) ... ok test_single_value_dimension_list_input (tests.io.test_write_utils.TestMakeIndicesMatrix.test_single_value_dimension_list_input) ... ok test_weird_inputs (tests.io.test_write_utils.TestMakeIndicesMatrix.test_weird_inputs) ... ok test_compute (tests.processing.test_process.TestCoreProcessNoTest.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestCoreProcessNoTest.test_tfunc) ... ok test_compute (tests.processing.test_process.TestCoreProcessWDuplicateResultsOverride.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Position_Indices at: with kwargs: {'shape': (15, 2), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Position_Values at: with kwargs: {'shape': (15, 2), 'dtype': dtype(' to destination dataset: Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! The shapes of the provided h5 position indices and values are OK Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Spectroscopic_Indices at: with kwargs: {'shape': (2, 14), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Spectroscopic_Values at: with kwargs: {'shape': (2, 14), 'dtype': dtype(' to destination dataset: Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] ['Meh'] ['Meh'] [[0 0] [1 0] [2 0] [0 1] [1 1] [2 1]] [[0 1 2 0 1 2] [0 0 0 1 1 1]] [[0 1 0 1 0 1] [0 0 1 1 2 2]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] [2 2] [2 2] NEAREST NEAREST False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [ 61 127] [ 62 127] [ 63 127]] [[0]] [2 2] [2 2] BICUBIC BICUBIC False False 28 28 224 224 [[ 0 0] [ 1 0] [ 2 0] ... [ 61 127] [ 62 127] [ 63 127]] [[0]] [1 2] [1 2] BICUBIC BICUBIC False False 0 0 255 255 [[ 0 0] [ 1 0] [ 2 0] ... [125 127] [126 127] [127 127]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] [2 2] [2 2] BICUBIC BICUBIC True True 0.0 0.0 1.0 1.0 [[ 0 0] [ 1 0] [ 2 0] ... [ 61 127] [ 62 127] [ 63 127]] [[0]] True True 0.0 0.0 1.0 1.0 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] (3, 5, 2, 7) Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file/build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWExistResultsDiffParms.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_duplicate_partial_results (tests.processing.test_process.TestCoreProcessWExistResultsDiffParms.test_duplicate_partial_results) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWExistingPartResults.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWExistingResults.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWTest.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestCoreProcessWTest.test_tfunc) ... ok test_invalid_parms_dict (tests.processing.test_process.TestInvalidInitialization.test_invalid_parms_dict) ... ok test_invalid_process_name (tests.processing.test_process.TestInvalidInitialization.test_invalid_process_name) ... ok test_no_map_func (tests.processing.test_process.TestInvalidInitialization.test_no_map_func) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_none_parms_dict (tests.processing.test_process.TestInvalidInitialization.test_none_parms_dict) ... ok test_not_main_dataset (tests.processing.test_process.TestInvalidInitialization.test_not_main_dataset) ... ok test_read_only_h5_main (tests.processing.test_process.TestInvalidInitialization.test_read_only_h5_main) ... ok test_read_only_h5_parent_group (tests.processing.test_process.TestInvalidInitialization.test_read_only_h5_parent_group) ... ok test_compute (tests.processing.test_process.TestMultiBatchCompute.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestMultiBatchCompute.test_tfunc) ... ok test_compute (tests.processing.test_process.TestProcLastPartialResult.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [] Resuming computation in group: /Raw_Measurement/source_main-Mean_Val_000 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] Returned previously computed results at /Raw_Measurement/source_main-Mean_Val_000 Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Creating HDF5 group and datasets to hold results Among the 15 positions in this dataset, the following positions need to be computed: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]. Each rank is required to work on 15 of the 15 (remaining) positions in this dataset. Rank 0 will read positions 0 to 15 of 15 This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank: 0 - with nothing loaded has 2.0 GB free memory Rank 0 will read positions: [0 1 2 3 4 5] Rank 0 will read 672.0 bytes of the SOURCE dataset. Rank: 0 available memory: 2.0 GB. 3 workers on this socket will in total read ~ 1.97 kB. Rank: 0 - with only raw data loaded has 2.0 GB free memory Rank 0 at Process class' default _unit_computation() that will call parallel_compute() Number of CPU free cores set to: 1 given that the CPU has 4 logical cores. 3 cores requested. computational jobs per core = 2. For short computations, each core must have at least 20 jobs to warrant parallel computation. Computations are not lengthy. Not enough jobs per core. Reducing cores to 1 Rank 0 starting computing on 1 cores (requested 3 cores) Rank 0 computing serially ... Rank 0 - computed chunk in 0.0 msec or 0.0 msec per pixel. Average: 0.0 msec per pixel. Rank: 0 - now holding onto raw data + results has 2.0 GB free memory Rank 0 - wrote its 6 pixel chunk in 0.0 msec Rank 0 - 40% complete. Time remaining: 0.0 msec Rank 0 will read positions: [ 6 7 8 9 10 11] Rank 0 will read 672.0 bytes of the SOURCE dataset. Rank: 0 available memory: 2.0 GB. 3 workers on this socket will in total read ~ 1.97 kB. Rank 0 at Process class' default _unit_computation() that will call parallel_compute() Number of CPU free cores set to: 1 given that the CPU has 4 logical cores. 3 cores requested. computational jobs per core = 2. For short computations, each core must have at least 20 jobs to warrant parallel computation. Computations are not lengthy. Not enough jobs per core. Reducing cores to 1 Rank 0 starting computing on 1 cores (requested 3 cores) Rank 0 computing serially ... Rank 0 - computed chunk in 0.0 msec or 0.0 msec per pixel. Average: 0.0 msec per pixel. Rank: 0 - now holding onto raw data + results has 2.0 GB free memory Rank 0 - wrote its 6 pixel chunk in 0.0 msec Rank 0 - 80% complete. Time remaining: 0.0 msec Rank 0 will read positions: [12 13 14] Rank 0 will read 336.0 bytes of the SOURCE dataset. Rank: 0 available memory: 2.0 GB. 3 workers on this socket will in total read ~ 1008.0 bytes. Rank 0 at Process class' default _unit_computation() that will call parallel_compute() Number of CPU free cores set to: 1 given that the CPU has 4 logical cores. 3 cores requested. computational jobs per core = 1. For short computations, each core must have at least 20 jobs to warrant parallel computation. Computations are not lengthy. Not enough jobs per core. Reducing cores to 1 Rank 0 starting computing on 1 cores (requested 3 cores) Rank 0 computing serially ... Rank 0 - computed chunk in 0.0 msec or 0.0 msec per pixel. Average: 0.0 msec per pixel. Rank: 0 - now holding onto raw data + results has 2.0 GB free memory Rank 0 - wrote its 3 pixel chunk in 0.0 msec Rank 0 - 100% complete. Time remaining: 0.0 msec Rank 0 - Finished reading all data! Rank 0 - Finished computing all jobs! Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Resuming computation in group: /Raw_Measurement/source_main-Mean_Val_001 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume ok test_compute (tests.processing.test_process.TestProcReturnCompletedNotPartial.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcWLegacyResultsComplete.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcWLegacyResultsPartial.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcWoStatus.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcessWExistingResultsDiffFile.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestUsePartialComputationIllegal.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestUsePartialComputationLegit.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestWriteResultsToNewH5File.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.12_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestWriteResultsToNewH5File.test_tfunc) ... ok ---------------------------------------------------------------------- Ran 359 tests in 64.867s OK Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [] Returned previously computed results at /Raw_Measurement/source_main-Mean_Val_000 Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] Returned previously computed results at /Raw_Measurement/source_main-Mean_Val_000 Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [] Resuming computation in group: /Raw_Measurement/source_main-Mean_Val_000 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Group: had neither the status HDF5 dataset or the legacy attribute: "last_pixel". This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Resuming computation in group: /source_main-Mean_Val_001 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Resuming computation. 73% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file I: pybuild base:311: cd /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build; python3.13 -m unittest discover -v test_legal (tests.io.hdf_utils.test_base.TestFindDataset.test_legal) ... ok test_many (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_many) ... ok test_no_hdf5_datasets (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_no_hdf5_datasets) ... ok test_non_string_names (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_non_string_names) ... ok test_same_name (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_same_name) ... ok test_single (tests.io.hdf_utils.test_base.TestGetH5ObjRefs.test_single) ... ok test_root_all_dsets (tests.io.hdf_utils.test_base.TestPrintTreeBEPS.test_root_all_dsets) ... ok test_root_main_dsets_only (tests.io.hdf_utils.test_base.TestPrintTreeBEPS.test_root_main_dsets_only) ... ok test_multi_level_rel_paths (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_rel_paths) ... ok test_multi_level_rel_paths_grp_b (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_rel_paths_grp_b) ... ok test_multi_level_tree (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree) ... ok test_multi_level_tree_grp_a (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree_grp_a) ... ok test_multi_level_tree_grp_b (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree_grp_b) ... ok test_multi_level_tree_main_dsets_only (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_multi_level_tree_main_dsets_only) ... ok test_not_a_group (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_not_a_group) ... ok test_single_level_rel_paths (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_single_level_rel_paths) ... ok test_single_level_tree (tests.io.hdf_utils.test_base.TestPrintTreeNoMain.test_single_level_tree) ... ok test_dset (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_dset) ... ok test_file (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_file) ... ok test_group (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_group) ... ok test_invalid (tests.io.hdf_utils.test_base.TestWriteBookKeepingAttrs.test_invalid) ... ok test_invalid_sort (tests.io.hdf_utils.test_model.TestGetDimensionality.test_invalid_sort) ... ok test_legal_no_sort (tests.io.hdf_utils.test_model.TestGetDimensionality.test_legal_no_sort) ... ok test_legal_w_sort (tests.io.hdf_utils.test_model.TestGetDimensionality.test_legal_w_sort) ... ok test_not_hdf_dset (tests.io.hdf_utils.test_model.TestGetDimensionality.test_not_hdf_dset) ... ok test_invalid_types (tests.io.hdf_utils.test_model.TestGetSortOrder.test_invalid_types) ... ok test_reversed (tests.io.hdf_utils.test_model.TestGetSortOrder.test_reversed) ... ok test_simple (tests.io.hdf_utils.test_model.TestGetSortOrder.test_simple) ... ok test_all_dim_names_not_provided (tests.io.hdf_utils.test_model.TestGetUnitValues.test_all_dim_names_not_provided) ... ok test_dependent_dim (tests.io.hdf_utils.test_model.TestGetUnitValues.test_dependent_dim) ... ok test_illegal_dset (tests.io.hdf_utils.test_model.TestGetUnitValues.test_illegal_dset) ... ok test_illegal_key (tests.io.hdf_utils.test_model.TestGetUnitValues.test_illegal_key) ... ok test_incomp_dim_no_attr (tests.io.hdf_utils.test_model.TestGetUnitValues.test_incomp_dim_no_attr) ... ok test_source_pos_all (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_pos_all) ... ok test_source_pos_single (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_pos_single) ... ok test_source_spec_all (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_spec_all) ... ok test_source_spec_all_explicit (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_spec_all_explicit) ... ok test_source_spec_single (tests.io.hdf_utils.test_model.TestGetUnitValues.test_source_spec_single) ... ok test_sparse_samp_no_attr (tests.io.hdf_utils.test_model.TestGetUnitValues.test_sparse_samp_no_attr) ... ok test_sparse_samp_w_attr (tests.io.hdf_utils.test_model.TestGetUnitValues.test_sparse_samp_w_attr) ... ok test_pos_and_spec_may_may_not_be_provided (tests.io.hdf_utils.test_model.TestReshapeFromNDims.test_pos_and_spec_may_may_not_be_provided) ... ok test_pos_and_spec_provided (tests.io.hdf_utils.test_model.TestReshapeFromNDims.test_pos_and_spec_provided) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_model.py:788: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(source_nd, h5_pos=source_pos_data, h5_spec=source_spec_data) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_model.py:799: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(main_pos_sorted, h5_pos=source_pos_data, /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_model.py:811: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(main_spec_sorted, h5_pos=source_pos_data, /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_model.py:823: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. ret_2d, success = hdf_utils.reshape_from_n_dims(main_not_sorted, h5_pos=source_pos_data, ok test_dask_all (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all) ... ok test_dask_all_both_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all_both_inds_order_flipped) ... ok test_dask_all_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all_ordinary_1d_pos) ... ok test_dask_all_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_all_ordinary_1d_spec) ... ok test_dask_in_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_in_ordinary_1d_pos) ... ok test_dask_in_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_in_ordinary_1d_spec) ... ok test_dask_input (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_input) ... ok test_dask_out_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_out_ordinary_1d_pos) ... ok test_dask_out_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_out_ordinary_1d_spec) ... 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [189 253] [[189] [253]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [ 86 172] [[ 86 172]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [231 215] [[231] [215]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [19 15] [[19 15]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [15 40] [[15] [40]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [173 155] [[173 155]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2]ok test_dask_output (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_dask_output) ... ok test_h5_already_sorted (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_already_sorted) ... ok test_h5_beps_field (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_beps_field) ... ok test_h5_both_inds_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_both_inds_flipped) ... ok test_h5_manually_provided_anc_dsets_h5 (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_manually_provided_anc_dsets_h5) ... ok test_h5_not_main_dset (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_h5_not_main_dset) ... ok test_numpy_both_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_both_inds_order_flipped) ... ok test_numpy_ordinary (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary) ... ok test_numpy_ordinary_1d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_1d_pos) ... ok test_numpy_ordinary_1d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_1d_spec) ... ok test_numpy_ordinary_2d_pos (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_2d_pos) ... ok test_numpy_ordinary_2d_spec (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_ordinary_2d_spec) ... After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) Position dimensions: ['X' 'Y'] Position sort order: [0 1] Spectroscopic Dimensions: ['Bias' 'Cycle'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['X' 'Y'] Position dimensionality (sort applied): [5, 3] Spectroscopic dimensions (sort applied): ['Bias' 'Cycle'] Spectroscopic dimensionality (sort applied): [7, 2] Will attempt to reshape main dataset from: (15, 14) to [3, 5, 2, 7] After reshaping, labels are ['Y' 'X' 'Cycle' 'Bias'] Data shape is (3, 5, 2, 7) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 3 2] New labels ordering: ['X' 'Y' 'Bias' 'Cycle'] Dataset now of shape: (5, 3, 7, 2) 2D: False, Spec: False, Flip: False Main data shapes ND: (2,), 2D: (2, 1) [24 7] [[24] [ 7]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (2, 1) to [2, 1] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (2, 1) 2D: False, Spec: True, Flip: False Main data shapes ND: (2,), 2D: (1, 2) [238 155] [[238 155]] Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [2] Will attempt to reshape main dataset from: (1, 2) to [1, 2] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 0'] Data shape is (1, 2) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0'] Dataset now of shape: (1, 2) 2D: True, Spec: False, Flip: False Main data shapes ND: (2, 3), 2D: (6, 1) [[ 65 86 60] [110 114 30]] [[ 65] [ 86] [ 60] [110] [114] [ 30]] Position dimensions: ['Position Dimension 0' 'Position Dimension 1'] Position sort order: [0 1] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0' 'Position Dimension 1'] Position dimensionality (sort applied): [3, 2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (6, 1) to [2, 3, 1] After reshaping, labels are ['Position Dimension 1' 'Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 3, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0' 'Position Dimension 1'] Position sort order: [0 1] Spectroscopic Dimensions: ['Spectral Dimension 0'] Spectroscopic sort order: [0] Position dimensions (sort applied): ['Position Dimension 0' 'Position Dimension 1'] Position dimensionality (sort applied): [3, 2] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0'] Spectroscopic dimensionality (sort applied): [1] Will attempt to reshape main dataset from: (6, 1) to [2, 3, 1] After reshaping, labels are ['Position Dimension 1' 'Position Dimension 0' 'Spectral Dimension 0'] Data shape is (2, 3, 1) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [1 0 2] New labels ordering: ['Position Dimension 0' 'Position Dimension 1' 'Spectral Dimension 0'] Dataset now of shape: (3, 2, 1) 2D: True, Spec: True, Flip: False Main data shapes ND: (2, 3), 2D: (1, 6) [[ 24 51 250] [235 49 126]] [[ 24 51 250 235 49 126]] Position dimensions: ['Position Dimension 0'] Position sort order:ok test_numpy_pos_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_pos_inds_order_flipped) ... ok test_numpy_spec_inds_order_flipped (tests.io.hdf_utils.test_model.TestReshapeToNDims.test_numpy_spec_inds_order_flipped) ... ok test_dask_small (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_dask_small) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_empty_main (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_empty_main) ... ok test_numpy_small (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_numpy_small) ... ok test_prod_sizes_mismatch (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_prod_sizes_mismatch) ... ok test_write_main_both_existing_aux (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_both_existing_aux) ... ok test_write_main_both_existing_aux_diff_file (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_both_existing_aux_diff_file) ... ok test_write_main_existing_pos_aux (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_pos_aux) ... [0] Spectroscopic Dimensions: ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic dimensionality (sort applied): [3, 2] Will attempt to reshape main dataset from: (1, 6) to [1, 2, 3] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 1' 'Spectral Dimension 0'] Data shape is (1, 2, 3) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position dimensions: ['Position Dimension 0'] Position sort order: [0] Spectroscopic Dimensions: ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic sort order: [0 1] Position dimensions (sort applied): ['Position Dimension 0'] Position dimensionality (sort applied): [1] Spectroscopic dimensions (sort applied): ['Spectral Dimension 0' 'Spectral Dimension 1'] Spectroscopic dimensionality (sort applied): [3, 2] Will attempt to reshape main dataset from: (1, 6) to [1, 2, 3] After reshaping, labels are ['Position Dimension 0' 'Spectral Dimension 1' 'Spectral Dimension 0'] Data shape is (1, 2, 3) Going to put dimensions back in the same order as in the file: Axes will permuted in this order: [0 2 1] New labels ordering: ['Position Dimension 0' 'Spectral Dimension 0' 'Spectral Dimension 1'] Dataset now of shape: (1, 3, 2) h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created empty dataset: for writing Dask dataset: dask.array Dask array will be written to HDF5 dataset: "/Test_Main" in file: "test.h5" Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Selected empty dataset creation. OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created empty dataset for Main Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK The shapes of the provided h5 position indices and values are OK Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Position_Indices at: with kwargs: {'shape': (15, 2), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Position_Values at: with kwargs: {'shape': (15, 2), 'dtype': dtype(' to destination dataset: The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Spectroscopic_Indices at: with kwargs: {'shape': (2, 14), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Spectroscopic_Values at: with kwargs: {'shape': (2, 14), 'dtype': dtype(' to destination dataset: Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OKok test_write_main_existing_pos_aux_diff_file (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_pos_aux_diff_file) ... ok test_write_main_existing_spec_aux (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_spec_aux) ... ok test_write_main_existing_spec_aux_diff_file (tests.io.hdf_utils.test_model.TestWriteMainDataset.test_write_main_existing_spec_aux_diff_file) ... ok test_existing (tests.io.hdf_utils.test_simple.TestAssignGroupIndex.test_existing) ... ok test_invalid_dtypes (tests.io.hdf_utils.test_simple.TestAssignGroupIndex.test_invalid_dtypes) ... ok test_new (tests.io.hdf_utils.test_simple.TestAssignGroupIndex.test_new) ... ok test_h5_main_non_dset_anc_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_h5_main_non_dset_anc_names) ... ok test_h5_main_not_dset (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_h5_main_not_dset) ... ok test_linking_main_legit_anc_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_linking_main_legit_anc_names) ... ok test_many_objs_to_many_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_many_objs_to_many_names) ... ok test_no_anc_refs_provided (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_no_anc_refs_provided) ... ok test_not_dset (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_not_dset) ... ok test_objs_and_refs_to_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_objs_and_refs_to_names) ... ok test_one_dset_to_name (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_one_dset_to_name) ... ok test_too_many_ancs (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_too_many_ancs) ... ok test_too_many_names (tests.io.hdf_utils.test_simple.TestCheckAndLinkAncillary.test_too_many_names) ... ok test_dset_matching_attrs (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_dset_matching_attrs) ... ok test_dset_no_attrs (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_dset_no_attrs) ... ok test_dset_one_mismatched_attrs (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_dset_one_mismatched_attrs) ... ok test_grp (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp) ... ok test_grp_mismatched_types_01 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_01) ... ok test_grp_mismatched_types_02 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_02) ... ok test_grp_mismatched_types_03 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_03) ... ok test_grp_mismatched_types_04 (tests.io.hdf_utils.test_simple.TestCheckForMatchingAttrs.test_grp_mismatched_types_04) ... ok test_exact_match (tests.io.hdf_utils.test_simple.TestCheckForOld.test_exact_match) ... ok test_exact_match_02 (tests.io.hdf_utils.test_simple.TestCheckForOld.test_exact_match_02) ... ok test_fail_01 (tests.io.hdf_utils.test_simple.TestCheckForOld.test_fail_01) ... ok test_fail_02 (tests.io.hdf_utils.test_simple.TestCheckForOld.test_fail_02) ... ok test_invalid_target_dset (tests.io.hdf_utils.test_simple.TestCheckForOld.test_invalid_target_dset) ... ok test_invalid_types (tests.io.hdf_utils.test_simple.TestCheckForOld.test_invalid_types) ... ok test_subset_but_match (tests.io.hdf_utils.test_simple.TestCheckForOld.test_subset_but_match) ... ok test_valid_target_dset (tests.io.hdf_utils.test_simple.TestCheckForOld.test_valid_target_dset) ... ok test_anc_not_dsets (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_anc_not_dsets) ... ok test_anc_shapes_not_matching (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_anc_shapes_not_matching) ... ok test_illegal_01 (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_illegal_01) ... ok test_invalid_str_attrs (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_invalid_str_attrs) ... ok test_legal (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_legal) ... ok test_missing_str_attrs (tests.io.hdf_utils.test_simple.TestCheckIfMain.test_missing_str_attrs) ... ok test_no_main_attrs (tests.io.hdf_utils.test_simple.TestCopyMainAttributes.test_no_main_attrs) ... ok test_valid (tests.io.hdf_utils.test_simple.TestCopyMainAttributes.test_valid) ... ok test_wrong_objects (tests.io.hdf_utils.test_simple.TestCopyMainAttributes.test_wrong_objects) ... ok test_diff_file_new_attrs_linked_dsets (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_diff_file_new_attrs_linked_dsets) ... ok test_diff_groups (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_diff_groups) ... ok test_existing_dset_name (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_existing_dset_name) ... ok test_invalid_types (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_invalid_types) ... ok test_same_group_new_attrs (tests.io.hdf_utils.test_simple.TestCreateEmptyDataset.test_same_group_new_attrs) ... ok test_create_indexed_group_invalid_types (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_create_indexed_group_invalid_types) ... ok test_empty_base_name (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_empty_base_name) ... ok test_first_group (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_first_group) ... ok test_second (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_second) ... ok test_w_suffix_ (tests.io.hdf_utils.test_simple.TestCreateIndexedGroup.test_w_suffix_) ... ok test_dash_in_name (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_dash_in_name) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:778: UserWarning: tool_name should not contain the "-" character. Reformatted name from:Some-Tool to Some_Tool warn('tool_name should not contain the "-" character. Reformatted name from:{} to ' ok test_different_file (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_different_file) ... ok test_empty_tool_name (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_empty_tool_name) ... ok test_first (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_first) ... ok test_invalid_types (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_invalid_types) ... ok test_second (tests.io.hdf_utils.test_simple.TestCreateResultsGroup.test_second) ... ok test_legal (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_legal) ... ok test_no_dset (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_no_dset) ... ok test_no_such_tool (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_no_such_tool) ... ok test_not_string (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_not_string) ... ok test_results_in_diff_file (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_results_in_diff_file) ... ok test_results_in_diff_file_invalid_type (tests.io.hdf_utils.test_simple.TestFindResultsGroup.test_results_in_diff_file_invalid_type) ... ok test_invalid_type (tests.io.hdf_utils.test_simple.TestGetAllMain.test_invalid_type) ... ok test_legal (tests.io.hdf_utils.test_simple.TestGetAllMain.test_legal) ... ok test_illegal (tests.io.hdf_utils.test_simple.TestGetSourceDataset.test_illegal) ... ok test_invalid_type (tests.io.hdf_utils.test_simple.TestGetSourceDataset.test_invalid_type) ... ok test_legal (tests.io.hdf_utils.test_simple.TestGetSourceDataset.test_legal) ... ok test_anc_args_not_h5_dset (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_anc_args_not_h5_dset) ... ok test_ind_vals_not_same_shape (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_ind_vals_not_same_shape) ... ok test_pos_args_not_h5_dset (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_pos_args_not_h5_dset) ... ok test_typical_attrs_not_specified (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_typical_attrs_not_specified) ... ok test_typical_attrs_specified (tests.io.hdf_utils.test_simple.TestLinkAsMain.test_typical_attrs_specified) ... ok test_blah (tests.io.hdf_utils.test_simple.TestValidateAncDsetAttrs.test_blah) ... ok test_invalid_dtypes (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_invalid_dtypes) ... ok test_invalid_spec_pos_bool (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_invalid_spec_pos_bool) ... ok test_mismatched_anc_shapes (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_mismatched_anc_shapes) ... ok test_valid_shapes (tests.io.hdf_utils.test_simple.TestValidateAncH5Dsets.test_valid_shapes) ... ok test_invalid_dims (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_invalid_dims) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_simple.py:1536: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((1, 25), [Dimension('a', 'b', 5), /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_simple.py:1540: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((25, 5), [Dimension('a', 'b', 5)], ok test_invalid_dtypes (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_invalid_dtypes) ... ok test_invalid_main_shape (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_invalid_main_shape) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_simple.py:1553: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func([-5, 1], Dimension('a', 'b', 5)) ok test_multi_dims (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_multi_dims) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_simple.py:1526: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((1, 15), [Dimension('a', 'b', 5), /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_simple.py:1529: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((15, 5), [Dimension('a', 'b', 5), ok test_single_dim (tests.io.hdf_utils.test_simple.TestValidateDimsAgainstMAin.test_single_dim) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_simple.py:1519: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((1, 5), Dimension('blah', 'meh', np.arange(5)), /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/tests/io/hdf_utils/test_simple.py:1521: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. func((5, 1), [Dimension('blah', 'meh', np.arange(5))], ok test_illegal (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_illegal) ... ok test_legal_bare_minimum_pos_f2s (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_pos_f2s) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_legal_bare_minimum_pos_s2f (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_pos_s2f) ... ok test_legal_bare_minimum_spec_f2s (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_spec_f2s) ... ok test_legal_bare_minimum_spec_s2f (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_bare_minimum_spec_s2f) ... ok test_legal_override_steps_offsets_base_name (tests.io.hdf_utils.test_simple.TestWriteIndValDsets.test_legal_override_steps_offsets_base_name) ... ok test_3d_to_1d_pos_fastest_n_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_pos_fastest_n_slowest) ... ok test_3d_to_1d_spec_fastest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_spec_fastest) ... ok test_3d_to_1d_spec_fastest_n_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_spec_fastest_n_slowest) ... ok test_3d_to_1d_spec_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_1d_spec_slowest) ... ok test_3d_to_2d_spec_fastest_n_slowest (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_3d_to_2d_spec_fastest_n_slowest) ... ok test_spec_1d_to_0d (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_spec_1d_to_0d) ... ok test_spec_2d_to_1d (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_spec_2d_to_1d) ... ok test_spec_2d_to_1d_new_file (tests.io.hdf_utils.test_simple.TestWriteReducedAncDsets.test_spec_2d_to_1d_new_file) ... ok test_all_dsets_as_dask (tests.io.test_array_translator.TestBaseOperations.test_all_dsets_as_dask) ... /usr/lib/python3/dist-packages/sidpy/sid/translator.py:42: FutureWarning: Consider using sidpy.Reader instead of sidpy.Translator if possible and contribute your reader to ScopeReaders warn('Consider using sidpy.Reader instead of sidpy.Translator if ' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/array_translator.py:93: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(raw_data.shape, dimensions, dim_name == 'Spectroscopic') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_preexisting_file_OK (tests.io.test_array_translator.TestBaseOperations.test_preexisting_file_OK) ... /usr/lib/python3/dist-packages/sidpy/sid/translator.py:42: FutureWarning: Consider using sidpy.Reader instead of sidpy.Translator if possible and contribute your reader to ScopeReaders warn('Consider using sidpy.Reader instead of sidpy.Translator if ' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/array_translator.py:93: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(raw_data.shape, dimensions, dim_name == 'Spectroscopic') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_quick_dask_main_translation (tests.io.test_array_translator.TestBaseOperations.test_quick_dask_main_translation) ... ok test_quick_numpy_tranlsation_plus_parms (tests.io.test_array_translator.TestBaseOperations.test_quick_numpy_tranlsation_plus_parms) ... /usr/lib/python3/dist-packages/sidpy/sid/translator.py:42: FutureWarning: Consider using sidpy.Reader instead of sidpy.Translator if possible and contribute your reader to ScopeReaders warn('Consider using sidpy.Reader instead of sidpy.Translator if ' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/array_translator.py:93: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(raw_data.shape, dimensions, dim_name == 'Spectroscopic') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:867: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, pos_dims, is_spectroscopic=False) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) ok test_quick_numpy_translation (tests.io.test_array_translator.TestBaseOperations.test_quick_numpy_translation) ... ok test_empty_name (tests.io.test_array_translator.TestExtraDatasets.test_empty_name) ... ok test_not_arrays (tests.io.test_array_translator.TestExtraDatasets.test_not_arrays) ... ok test_not_dicts (tests.io.test_array_translator.TestExtraDatasets.test_not_dicts) ... ok test_not_str_names (tests.io.test_array_translator.TestExtraDatasets.test_not_str_names) ... ok test_reserved_names (tests.io.test_array_translator.TestExtraDatasets.test_reserved_names) ... ok test_object_single (tests.io.test_array_translator.TestIllegalDimensions.test_object_single) ... ok test_objects (tests.io.test_array_translator.TestIllegalDimensions.test_objects) ... ok test_position (tests.io.test_array_translator.TestIllegalDimensions.test_position) ... ok test_spec (tests.io.test_array_translator.TestIllegalDimensions.test_spec) ... ok test_main_dset_1D (tests.io.test_array_translator.TestIllegalMainDataset.test_main_dset_1D) ... ok test_main_dset_2D (tests.io.test_array_translator.TestIllegalMainDataset.test_main_dset_2D) ... ok test_not_numpy_or_dask_array_main (tests.io.test_array_translator.TestIllegalMainDataset.test_not_numpy_or_dask_array_main) ... ok test_not_strings (tests.io.test_array_translator.TestIllegalStringParms.test_not_strings) ... ok test_dim_type_invalid_comparison (tests.io.test_dimension.TestDimType.test_dim_type_invalid_comparison) ... ok test_dim_type_valid_comparison (tests.io.test_dimension.TestDimType.test_dim_type_valid_comparison) ... ok test_default_mode (tests.io.test_dimension.TestDimension.test_default_mode) ... ok test_equality (tests.io.test_dimension.TestDimension.test_equality) ... ok test_illegal_instantiation (tests.io.test_dimension.TestDimension.test_illegal_instantiation) ... ok test_inequality (tests.io.test_dimension.TestDimension.test_inequality) ... ok test_invalid_mode (tests.io.test_dimension.TestDimension.test_invalid_mode) ... ok test_repr (tests.io.test_dimension.TestDimension.test_repr) ... ok test_values_as_array (tests.io.test_dimension.TestDimension.test_values_as_array) ... ok test_values_as_length (tests.io.test_dimension.TestDimension.test_values_as_length) ... ok test_basic_translate (tests.io.test_image_translator.TestBinning.test_basic_translate) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/image.py:48: FutureWarning: pyUSID.ImageTranslator will be removed shortly. Consider using SciFiReaders.ImageReader instead warn("pyUSID.ImageTranslator will be removed shortly. Consider using " ok test_custom_interp (tests.io.test_image_translator.TestBinning.test_custom_interp) ... ok test_float_parms (tests.io.test_image_translator.TestBinning.test_float_parms) ... ok test_invalid_dtype (tests.io.test_image_translator.TestBinning.test_invalid_dtype) ... ok test_invalid_interp (tests.io.test_image_translator.TestBinning.test_invalid_interp) ... ok test_neg_parms (tests.io.test_image_translator.TestBinning.test_neg_parms) ... ok test_single_default_interp (tests.io.test_image_translator.TestBinning.test_single_default_interp) ... ok test_too_many_dims (tests.io.test_image_translator.TestBinning.test_too_many_dims) ... ok test_tuple_default_interp (tests.io.test_image_translator.TestBinning.test_tuple_default_interp) ... ok test_basic_translate (tests.io.test_image_translator.TestFile.test_basic_translate) ... ok test_invalid_h5_path (tests.io.test_image_translator.TestFile.test_invalid_h5_path) ... ok test_output_h5_file_already_exists (tests.io.test_image_translator.TestFile.test_output_h5_file_already_exists) ... ok test_path_does_not_exist (tests.io.test_image_translator.TestFile.test_path_does_not_exist) ... ok test_path_not_str (tests.io.test_image_translator.TestFile.test_path_not_str) ... ok test_valid_h5_path (tests.io.test_image_translator.TestFile.test_valid_h5_path) ... ok test_basic_translate (tests.io.test_image_translator.TestImageTranslator.test_basic_translate) ... ok test_basic_translate (tests.io.test_image_translator.TestNormalization.test_basic_translate) ... ok test_normalize_and_default_interp (tests.io.test_image_translator.TestNormalization.test_normalize_and_default_interp) ... ok test_normalize_only (tests.io.test_image_translator.TestNormalization.test_normalize_only) ... ok test_color (tests.io.test_image_translator.TestReadImage.test_color) ... ok test_color_to_bw_image (tests.io.test_image_translator.TestReadImage.test_color_to_bw_image) ... ok test_text_complex_to_pillow (tests.io.test_image_translator.TestReadImage.test_text_complex_to_pillow) ... ok test_text_to_numpy_complex (tests.io.test_image_translator.TestReadImage.test_text_to_numpy_complex) ... ok test_text_to_numpy_simple (tests.io.test_image_translator.TestReadImage.test_text_to_numpy_simple) ... ok test_to_numpy (tests.io.test_image_translator.TestReadImage.test_to_numpy) ... ok test_to_pillow (tests.io.test_image_translator.TestReadImage.test_to_pillow) ... ok test_copy_reg_ref_reduced_dim (tests.io.test_reg_ref.TestRegRef.test_copy_reg_ref_reduced_dim) ... ok test_correct_USIDataset (tests.io.test_usi_dataset.TestEquality.test_correct_USIDataset) ... ok test_correct_h5_dataset (tests.io.test_usi_dataset.TestEquality.test_correct_h5_dataset) ... ok test_incorrect_USIDataset (tests.io.test_usi_dataset.TestEquality.test_incorrect_USIDataset) ... ok test_incorrect_h5_dataset (tests.io.test_usi_dataset.TestEquality.test_incorrect_h5_dataset) ... ok test_incorrect_object (tests.io.test_usi_dataset.TestEquality.test_incorrect_object) ... ok test_all_dims_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_all_dims_sliced) ... ok test_both_pos_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_both_pos_dim_sliced) ... ok test_both_spec_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_both_spec_dim_sliced) ... ok test_empty (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_empty) ... ok test_one_pos_one_spec_dims_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_one_pos_one_spec_dims_sliced) ... ok test_single_pos_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_pos_dim_sliced) ... ok test_single_pos_dim_truncated (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_pos_dim_truncated) ... ok test_single_spec_dim_sliced (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_spec_dim_sliced) ... ok test_single_spec_dim_truncated (tests.io.test_usi_dataset.TestGetDimsForSliceReal.test_single_spec_dim_truncated) ... ok test_sorted_and_unsorted (tests.io.test_usi_dataset.TestGetNDimFormExistsReal.test_sorted_and_unsorted) ... ok test_get_pos_values (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_pos_values) ... ok test_get_pos_values_illegal (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_pos_values_illegal) ... ok test_get_spec_values (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_spec_values) ... ok test_get_spec_values_illegal (tests.io.test_usi_dataset.TestGetUnitValuesReal.test_get_spec_values_illegal) ... ok test_both_pos_removed (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_both_pos_removed) ... ok test_empty_dict (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_empty_dict) ... ok test_incorrect_type (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_incorrect_type) ... ok test_negative_index (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_negative_index) ... ok test_non_existent_dim (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_non_existent_dim) ... ok test_one_pos_dim_removed (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_one_pos_dim_removed) ... ok test_one_pos_dim_sliced (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_one_pos_dim_sliced) ... ok test_out_of_bounds (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_out_of_bounds) ... ok test_pos_and_spec_sliced_list (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_pos_and_spec_sliced_list) ... ok test_two_pos_dim_sliced (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_two_pos_dim_sliced) ... ok test_two_pos_dim_sliced_list (tests.io.test_usi_dataset.TestPosSpecSlicesReal.test_two_pos_dim_sliced_list) ... ok test_one_pos (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_one_pos) ... ok test_one_pos_one_spec (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_one_pos_one_spec) ... ok test_one_spec (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_one_spec) ... ok test_two_pos_simple (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_two_pos_simple) ... ok test_two_spec (tests.io.test_usi_dataset.TestSimpleStaticVisualizationReal.test_two_spec) ... ok test_all_but_one_dims_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_2d_dask) ... ok test_all_but_one_dims_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_2d_numpy) ... ok test_all_but_one_dims_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_nd_dask) ... ok test_all_but_one_dims_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_but_one_dims_sliced_nd_numpy) ... ok test_all_dims_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_2d_dask) ... ok test_all_dims_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_2d_numpy) ... ok test_all_dims_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_nd_dask) ... ok test_all_dims_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_all_dims_sliced_nd_numpy) ... ok test_both_pos_removed_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_2d_dask) ... ok test_both_pos_removed_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_2d_numpy) ... ok test_both_pos_removed_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_nd_dask) ... ok test_both_pos_removed_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_both_pos_removed_nd_numpy) ... ok test_empty_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_empty_2d_dask) ... ok test_empty_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_empty_2d_numpy) ... ok test_empty_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_empty_nd_dask) ... ok test_empty_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_empty_nd_numpy) ... ok test_incorrect_type (tests.io.test_usi_dataset.TestSliceReal.test_incorrect_type) ... ok test_negative_index_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_negative_index_2d_numpy) ... ok test_negative_index_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_negative_index_nd_dask) ... ok test_negative_index_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_negative_index_nd_numpy) ... ok test_non_existent_dim (tests.io.test_usi_dataset.TestSliceReal.test_non_existent_dim) ... ok test_one_pos_dim_removed_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_2d_dask) ... ok test_one_pos_dim_removed_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_2d_numpy) ... ok test_one_pos_dim_removed_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_nd_dask) ... ok test_one_pos_dim_removed_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_removed_nd_numpy) ... ok test_one_pos_dim_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_2d_dask) ... ok test_one_pos_dim_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_2d_numpy) ... ok test_one_pos_dim_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_nd_dask) ... ok test_one_pos_dim_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_one_pos_dim_sliced_nd_numpy) ... ok test_out_of_bounds (tests.io.test_usi_dataset.TestSliceReal.test_out_of_bounds) ... ok test_pos_and_spec_sliced_list_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_2d_dask) ... ok test_pos_and_spec_sliced_list_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_2d_numpy) ... ok test_pos_and_spec_sliced_list_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_nd_dask) ... ok test_pos_and_spec_sliced_list_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_pos_and_spec_sliced_list_nd_numpy) ... ok test_two_pos_dim_sliced_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_2d_dask) ... ok test_two_pos_dim_sliced_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_2d_numpy) ... ok test_two_pos_dim_sliced_list_2d_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_2d_dask) ... ok test_two_pos_dim_sliced_list_2d_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_2d_numpy) ... ok test_two_pos_dim_sliced_list_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_nd_dask) ... ok test_two_pos_dim_sliced_list_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_list_nd_numpy) ... ok test_two_pos_dim_sliced_nd_dask (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_nd_dask) ... ok test_two_pos_dim_sliced_nd_numpy (tests.io.test_usi_dataset.TestSliceReal.test_two_pos_dim_sliced_nd_numpy) ... ok test_get_current_sorting (tests.io.test_usi_dataset.TestSortingReal.test_get_current_sorting) ... ok test_toggle_sorting (tests.io.test_usi_dataset.TestSortingReal.test_toggle_sorting) ... ok test_string_representation (tests.io.test_usi_dataset.TestStringRepr.test_string_representation) ... ok test_1D (tests.io.test_write_utils.TestBuildIndValMatrices.test_1D) ... ok test_1D_pos (tests.io.test_write_utils.TestBuildIndValMatrices.test_1D_pos) ... ok test_3D (tests.io.test_write_utils.TestBuildIndValMatrices.test_3D) ... ok test_empty (tests.io.test_write_utils.TestBuildIndValMatrices.test_empty) ... ok test_invalid_inputs (tests.io.test_write_utils.TestBuildIndValMatrices.test_invalid_inputs) ... ok test_invalid_types (tests.io.test_write_utils.TestCalcChunks.test_invalid_types) ... ok test_no_unit_chunk (tests.io.test_write_utils.TestCalcChunks.test_no_unit_chunk) ... ok test_no_unit_chunk_max_mem (tests.io.test_write_utils.TestCalcChunks.test_no_unit_chunk_max_mem) ... ok test_shape_mismatch (tests.io.test_write_utils.TestCalcChunks.test_shape_mismatch) ... ok test_unit_chunk (tests.io.test_write_utils.TestCalcChunks.test_unit_chunk) ... ok test_unit_chunk_max_mem (tests.io.test_write_utils.TestCalcChunks.test_unit_chunk_max_mem) ... ok test_unit_not_iterable (tests.io.test_write_utils.TestCalcChunks.test_unit_not_iterable) ... ok test_invalid_inputs (tests.io.test_write_utils.TestCreateSpecIndsFromVals.test_invalid_inputs) ... ok test_legal (tests.io.test_write_utils.TestCreateSpecIndsFromVals.test_legal) ... ok test_legal_multi_dim (tests.io.test_write_utils.TestGetAuxDsetSlicing.test_legal_multi_dim) ... ok test_legal_single_dim (tests.io.test_write_utils.TestGetAuxDsetSlicing.test_legal_single_dim) ... ok test_odd_input (tests.io.test_write_utils.TestGetAuxDsetSlicing.test_odd_input) ... ok test_2_dims (tests.io.test_write_utils.TestMakeIndicesMatrix.test_2_dims) ... ok test_3_dims (tests.io.test_write_utils.TestMakeIndicesMatrix.test_3_dims) ... ok test_dim_w_val_1 (tests.io.test_write_utils.TestMakeIndicesMatrix.test_dim_w_val_1) ... ok test_empty_list (tests.io.test_write_utils.TestMakeIndicesMatrix.test_empty_list) ... ok test_just_size_of_one_dim (tests.io.test_write_utils.TestMakeIndicesMatrix.test_just_size_of_one_dim) ... ok test_matrix_1_dims (tests.io.test_write_utils.TestMakeIndicesMatrix.test_matrix_1_dims) ... ok test_non_int_dim_sizes (tests.io.test_write_utils.TestMakeIndicesMatrix.test_non_int_dim_sizes) ... ok test_single_value_dimension_int_input (tests.io.test_write_utils.TestMakeIndicesMatrix.test_single_value_dimension_int_input) ... ok test_single_value_dimension_list_input (tests.io.test_write_utils.TestMakeIndicesMatrix.test_single_value_dimension_list_input) ... ok test_weird_inputs (tests.io.test_write_utils.TestMakeIndicesMatrix.test_weird_inputs) ... ok test_compute (tests.processing.test_process.TestCoreProcessNoTest.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestCoreProcessNoTest.test_tfunc) ... ok test_compute (tests.processing.test_process.TestCoreProcessWDuplicateResultsOverride.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Position_Indices at: with kwargs: {'shape': (15, 2), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Position_Values at: with kwargs: {'shape': (15, 2), 'dtype': dtype(' to destination dataset: Passed all pre-tests for creating spectroscopic datasets Indices: [[0 0 0 0 0 0 0 1 1 1 1 1 1 1] [0 1 2 3 4 5 6 0 1 2 3 4 5 6]] Values: [[0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 2. 3. 4. 5. 6. 0. 1. 2. 3. 4. 5. 6.]] Created Spectroscopic datasets Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! The shapes of the provided h5 position indices and values are OK Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] h5 group and file OK quantity, units, main_data_name all OK Provided numpy or Dask array for main_data OK so far Passed all pre-tests for creating position datasets Indices: [[0 0] [0 1] [0 2] [0 3] [0 4] [1 0] [1 1] [1 2] [1 3] [1 4] [2 0] [2 1] [2 2] [2 3] [2 4]] Values: [[0. 0.] [0. 1.] [0. 2.] [0. 3.] [0. 4.] [1. 0.] [1. 1.] [1. 2.] [1. 3.] [1. 4.] [2. 0.] [2. 1.] [2. 2.] [2. 3.] [2. 4.]] Created position datasets! The shapes of the provided h5 position indices and values are OK Need to copy over ancillary datasets: and to destination group: which is in a different HDF5 file Creating new HDF5 dataset named: Spectroscopic_Indices at: with kwargs: {'shape': (2, 14), 'dtype': dtype('uint32'), 'compression': None, 'chunks': None} dask.array will copy data from source dataset to new dataset Copying simple attributes of original dataset: to destination dataset: Creating new HDF5 dataset named: Spectroscopic_Values at: with kwargs: {'shape': (2, 14), 'dtype': dtype(' to destination dataset: Created main dataset with provided data Wrote quantity and units attributes to main dataset Successfully linked datasets - dataset should be main now [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] ['Meh'] ['Meh'] [[0 0] [1 0] [2 0] [0 1] [1 1] [2 1]] [[0 1 2 0 1 2] [0 0 0 1 1 1]] [[0 1 0 1 0 1] [0 0 1 1 2 2]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0 0] [1 0] [2 0] [3 0] [4 0] [0 1] [1 1] [2 1] [3 1] [4 1] [0 2] [1 2] [2 2] [3 2] [4 2]] [[0 1 2 3 4 5 6 0 1 2 3 4 5 6] [0 0 0 0 0 0 0 1 1 1 1 1 1 1]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] [2 2] [2 2] NEAREST NEAREST False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [ 61 127] [ 62 127] [ 63 127]] [[0]] [2 2] [2 2] BICUBIC BICUBIC False False 26 26 223 223 [[ 0 0] [ 1 0] [ 2 0] ... [ 61 127] [ 62 127] [ 63 127]] [[0]] [1 2] [1 2] BICUBIC BICUBIC False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 127] [126 127] [127 127]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] False False 0 0 254 254 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] [[0]] [2 2] [2 2] BICUBIC BICUBIC True True 0.0 0.0 1.0 1.0 [[ 0 0] [ 1 0] [ 2 0] ... [ 61 127] [ 62 127] [ 63 127]] [[0]] True True 0.0 0.0 1.0 1.0 [[ 0 0] [ 1 0] [ 2 0] ... [125 255] [126 255] [127 255]] (3, 5, 2, 7) Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file/build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWExistResultsDiffParms.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_duplicate_partial_results (tests.processing.test_process.TestCoreProcessWExistResultsDiffParms.test_duplicate_partial_results) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWExistingPartResults.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWExistingResults.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestCoreProcessWTest.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestCoreProcessWTest.test_tfunc) ... ok test_invalid_parms_dict (tests.processing.test_process.TestInvalidInitialization.test_invalid_parms_dict) ... ok test_invalid_process_name (tests.processing.test_process.TestInvalidInitialization.test_invalid_process_name) ... ok test_no_map_func (tests.processing.test_process.TestInvalidInitialization.test_no_map_func) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_none_parms_dict (tests.processing.test_process.TestInvalidInitialization.test_none_parms_dict) ... ok test_not_main_dataset (tests.processing.test_process.TestInvalidInitialization.test_not_main_dataset) ... ok test_read_only_h5_main (tests.processing.test_process.TestInvalidInitialization.test_read_only_h5_main) ... ok test_read_only_h5_parent_group (tests.processing.test_process.TestInvalidInitialization.test_read_only_h5_parent_group) ... ok test_compute (tests.processing.test_process.TestMultiBatchCompute.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestMultiBatchCompute.test_tfunc) ... ok test_compute (tests.processing.test_process.TestProcLastPartialResult.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [] Resuming computation in group: /Raw_Measurement/source_main-Mean_Val_000 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] Returned previously computed results at /Raw_Measurement/source_main-Mean_Val_000 Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Creating HDF5 group and datasets to hold results Among the 15 positions in this dataset, the following positions need to be computed: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]. Each rank is required to work on 15 of the 15 (remaining) positions in this dataset. Rank 0 will read positions 0 to 15 of 15 This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank: 0 - with nothing loaded has 2.0 GB free memory Rank 0 will read positions: [0 1 2 3 4 5] Rank 0 will read 672.0 bytes of the SOURCE dataset. Rank: 0 available memory: 2.0 GB. 3 workers on this socket will in total read ~ 1.97 kB. Rank: 0 - with only raw data loaded has 2.0 GB free memory Rank 0 at Process class' default _unit_computation() that will call parallel_compute() Number of CPU free cores set to: 1 given that the CPU has 4 logical cores. 3 cores requested. computational jobs per core = 2. For short computations, each core must have at least 20 jobs to warrant parallel computation. Computations are not lengthy. Not enough jobs per core. Reducing cores to 1 Rank 0 starting computing on 1 cores (requested 3 cores) Rank 0 computing serially ... Rank 0 - computed chunk in 0.0 msec or 0.0 msec per pixel. Average: 0.0 msec per pixel. Rank: 0 - now holding onto raw data + results has 2.0 GB free memory Rank 0 - wrote its 6 pixel chunk in 0.0 msec Rank 0 - 40% complete. Time remaining: 0.0 msec Rank 0 will read positions: [ 6 7 8 9 10 11] Rank 0 will read 672.0 bytes of the SOURCE dataset. Rank: 0 available memory: 2.0 GB. 3 workers on this socket will in total read ~ 1.97 kB. Rank 0 at Process class' default _unit_computation() that will call parallel_compute() Number of CPU free cores set to: 1 given that the CPU has 4 logical cores. 3 cores requested. computational jobs per core = 2. For short computations, each core must have at least 20 jobs to warrant parallel computation. Computations are not lengthy. Not enough jobs per core. Reducing cores to 1 Rank 0 starting computing on 1 cores (requested 3 cores) Rank 0 computing serially ... Rank 0 - computed chunk in 0.0 msec or 0.0 msec per pixel. Average: 0.0 msec per pixel. Rank: 0 - now holding onto raw data + results has 2.0 GB free memory Rank 0 - wrote its 6 pixel chunk in 0.0 msec Rank 0 - 80% complete. Time remaining: 0.0 msec Rank 0 will read positions: [12 13 14] Rank 0 will read 336.0 bytes of the SOURCE dataset. Rank: 0 available memory: 2.0 GB. 3 workers on this socket will in total read ~ 1008.0 bytes. Rank 0 at Process class' default _unit_computation() that will call parallel_compute() Number of CPU free cores set to: 1 given that the CPU has 4 logical cores. 3 cores requested. computational jobs per core = 1. For short computations, each core must have at least 20 jobs to warrant parallel computation. Computations are not lengthy. Not enough jobs per core. Reducing cores to 1 Rank 0 starting computing on 1 cores (requested 3 cores) Rank 0 computing serially ... Rank 0 - computed chunk in 0.0 msec or 0.0 msec per pixel. Average: 0.0 msec per pixel. Rank: 0 - now holding onto raw data + results has 2.0 GB free memory Rank 0 - wrote its 3 pixel chunk in 0.0 msec Rank 0 - 100% complete. Time remaining: 0.0 msec Rank 0 - Finished reading all data! Rank 0 - Finished computing all jobs! Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Resuming computation in group: /Raw_Measurement/source_main-Mean_Val_001 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume ok test_compute (tests.processing.test_process.TestProcReturnCompletedNotPartial.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcWLegacyResultsComplete.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcWLegacyResultsPartial.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcWoStatus.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestProcessWExistingResultsDiffFile.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestUsePartialComputationIllegal.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestUsePartialComputationLegit.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_compute (tests.processing.test_process.TestWriteResultsToNewH5File.test_compute) ... /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/model.py:886: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead. validate_dims_against_main(main_shape, spec_dims, is_spectroscopic=True) /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1072: UserWarning: In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying warn('In the future write_ind_val_dsets will default to requiring dimensions to be arranged from slowest to fastest varying') /build/reproducible-path/pyusid-0.0.12/.pybuild/cpython3_3.13_pyusid/build/pyUSID/io/hdf_utils/simple.py:1129: UserWarning: pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer createsregion references for each dimension. Please use pyUSID.io.reg_ref.write_region_references to manually create region references warn('pyUSID.io.hdf_utils.simple.write_ind_val_dsets no longer creates' ok test_tfunc (tests.processing.test_process.TestWriteResultsToNewH5File.test_tfunc) ... ok ---------------------------------------------------------------------- Ran 359 tests in 64.241s OK Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [] Returned previously computed results at /Raw_Measurement/source_main-Mean_Val_000 Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation [] Returned previously computed results at /Raw_Measurement/source_main-Mean_Val_000 Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [] Resuming computation in group: /Raw_Measurement/source_main-Mean_Val_000 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Group: had neither the status HDF5 dataset or the legacy attribute: "last_pixel". This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Resuming computation in group: /source_main-Mean_Val_001 Resuming computation. 46% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file Note: Mean_Val has already been performed PARTIALLY with the same parameters. compute() will resuming computation in the last group below. To choose a different group call use_patial_computation()Set override to True to force fresh computation or resume from a data group besides the last in the list. [, ] Resuming computation. 73% completed already This class (likely) supports interruption and resuming of computations! If you are operating in a python console, press Ctrl+C or Cmd+C to abort If you are in a Jupyter notebook, click on "Kernel">>"Interrupt" If you are operating on a cluster and your job gets killed, re-run the job to resume Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file This class does NOT support interruption and resuming of computations. In order to enable this feature, simply implement the _get_existing_datasets() function Rank 0 - 100% complete. Time remaining: 0.0 msec Finished processing the entire dataset! Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file create-stamp debian/debhelper-build-stamp dh_testroot -O--buildsystem=pybuild dh_prep -O--buildsystem=pybuild dh_auto_install --destdir=debian/python3-pyusid/ -O--buildsystem=pybuild I: pybuild plugin_pyproject:178: Copying package built for python3.12 to destdir I: pybuild plugin_pyproject:178: Copying package built for python3.13 to destdir dh_installdocs -O--buildsystem=pybuild dh_installchangelogs -O--buildsystem=pybuild dh_python3 -O--buildsystem=pybuild dh_installsystemduser -O--buildsystem=pybuild dh_perl -O--buildsystem=pybuild dh_link -O--buildsystem=pybuild dh_strip_nondeterminism -O--buildsystem=pybuild dh_compress -O--buildsystem=pybuild dh_fixperms -O--buildsystem=pybuild dh_missing -O--buildsystem=pybuild dh_installdeb -O--buildsystem=pybuild dh_gencontrol -O--buildsystem=pybuild dh_md5sums -O--buildsystem=pybuild dh_builddeb -O--buildsystem=pybuild dpkg-deb: building package 'python3-pyusid' in '../python3-pyusid_0.0.12-2_all.deb'. dpkg-genbuildinfo --build=binary -O../pyusid_0.0.12-2_armhf.buildinfo dpkg-genchanges --build=binary -O../pyusid_0.0.12-2_armhf.changes dpkg-genchanges: info: binary-only upload (no source code included) dpkg-source --after-build . dpkg-source: info: using options from pyusid-0.0.12/debian/source/options: --extend-diff-ignore=^[^/]+.(egg-info|dist-info)/ dpkg-buildpackage: info: binary-only upload (no source included) dpkg-genchanges: info: not including original source code in upload I: copying local configuration I: unmounting dev/ptmx filesystem I: unmounting dev/pts filesystem I: unmounting dev/shm filesystem I: unmounting proc filesystem I: unmounting sys filesystem I: cleaning the build env I: removing directory /srv/workspace/pbuilder/21902 and its subdirectories I: Current time: Thu Jan 9 21:45:24 -12 2025 I: pbuilder-time-stamp: 1736502324