Mock Version: 3.5
Mock Version: 3.5
Mock Version: 3.5
ENTER ['do_with_status'](['bash', '--login', '-c', '/usr/bin/rpmbuild -bs --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'], chrootPath='/var/lib/mock/f38-build-side-42-init-devel-1090490-30049/root'env={'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'}shell=Falselogger=<mockbuild.trace_decorator.getLog object at 0xffffff737fbed0>timeout=864000uid=996gid=135user='mockbuild'nspawn_args=[]unshare_net=TrueprintOutput=False)
Executing command: ['bash', '--login', '-c', '/usr/bin/rpmbuild -bs --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'] with env {'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'} and shell False
Building target platforms: noarch
Building for target noarch
setting SOURCE_DATE_EPOCH=1691798400
Wrote: /builddir/build/SRPMS/python-xarray-2023.1.0-1.fc38.src.rpm
Child return code was: 0
ENTER ['do_with_status'](['bash', '--login', '-c', '/usr/bin/rpmbuild -br --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'], chrootPath='/var/lib/mock/f38-build-side-42-init-devel-1090490-30049/root'env={'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'}shell=Falselogger=<mockbuild.trace_decorator.getLog object at 0xffffff737fbed0>timeout=864000uid=996gid=135user='mockbuild'nspawn_args=[]unshare_net=TrueraiseExc=FalseprintOutput=False)
Executing command: ['bash', '--login', '-c', '/usr/bin/rpmbuild -br --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'] with env {'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'} and shell False
Building target platforms: noarch
Building for target noarch
setting SOURCE_DATE_EPOCH=1691798400
Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.fXhZW9
+ umask 022
+ cd /builddir/build/BUILD
+ cd /builddir/build/BUILD
+ rm -rf xarray-2023.1.0
+ /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/xarray-2023.1.0.tar.gz
+ STATUS=0
+ '[' 0 -ne 0 ']'
+ cd xarray-2023.1.0
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w .
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0001-DOC-Don-t-download-RGB.byte.tif-during-build.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0002-DOC-Skip-examples-using-unpackaged-dependencies.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0003-DOC-Don-t-print-out-conda-pip-environment.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.ViqNVw
+ umask 022
+ cd /builddir/build/BUILD
+ cd xarray-2023.1.0
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(pip) >= 19'
+ echo 'python3dist(packaging)'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(toml) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ TMPDIR=/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ RPM_TOXENV=py311
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir -r
Handling setuptools>=42 from build-system.requires
Requirement satisfied: setuptools>=42
   (installed: setuptools 65.5.1)
Handling setuptools-scm>=7 from build-system.requires
Requirement not satisfied: setuptools-scm>=7
Exiting dependency generation pass: build backend
+ rm -rfv '*.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-xarray-2023.1.0-1.fc38.buildreqs.nosrc.rpm
Child return code was: 11
Dynamic buildrequires detected
Going to install missing buildrequires. See root.log for details.
ENTER ['do_with_status'](['bash', '--login', '-c', '/usr/bin/rpmbuild -br --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'], chrootPath='/var/lib/mock/f38-build-side-42-init-devel-1090490-30049/root'env={'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'}shell=Falselogger=<mockbuild.trace_decorator.getLog object at 0xffffff737fbed0>timeout=864000uid=996gid=135user='mockbuild'nspawn_args=[]unshare_net=TrueraiseExc=FalseprintOutput=False)
Executing command: ['bash', '--login', '-c', '/usr/bin/rpmbuild -br --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'] with env {'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'} and shell False
Building target platforms: noarch
Building for target noarch
setting SOURCE_DATE_EPOCH=1691798400
Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.g5gedW
+ umask 022
+ cd /builddir/build/BUILD
+ cd /builddir/build/BUILD
+ rm -rf xarray-2023.1.0
+ /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/xarray-2023.1.0.tar.gz
+ STATUS=0
+ '[' 0 -ne 0 ']'
+ cd xarray-2023.1.0
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w .
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0001-DOC-Don-t-download-RGB.byte.tif-during-build.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0002-DOC-Skip-examples-using-unpackaged-dependencies.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0003-DOC-Don-t-print-out-conda-pip-environment.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.nlJF6I
+ umask 022
+ cd /builddir/build/BUILD
+ cd xarray-2023.1.0
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(pip) >= 19'
+ echo 'python3dist(packaging)'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(toml) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ TMPDIR=/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ RPM_TOXENV=py311
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir -r
Handling setuptools>=42 from build-system.requires
Requirement satisfied: setuptools>=42
   (installed: setuptools 65.5.1)
Handling setuptools-scm>=7 from build-system.requires
Requirement satisfied: setuptools-scm>=7
   (installed: setuptools-scm 7.1.0)
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:295: UserWarning: git archive did not support describe output
  warnings.warn("git archive did not support describe output")
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:312: UserWarning: unexported git archival found
  warnings.warn("unexported git archival found")
HOOK STDOUT: running egg_info
HOOK STDOUT: writing xarray.egg-info/PKG-INFO
HOOK STDOUT: writing dependency_links to xarray.egg-info/dependency_links.txt
HOOK STDOUT: writing requirements to xarray.egg-info/requires.txt
HOOK STDOUT: writing top-level names to xarray.egg-info/top_level.txt
HOOK STDOUT: reading manifest file 'xarray.egg-info/SOURCES.txt'
HOOK STDOUT: adding license file 'LICENSE'
HOOK STDOUT: writing manifest file 'xarray.egg-info/SOURCES.txt'
Handling wheel from get_requires_for_build_wheel
Requirement not satisfied: wheel
Exiting dependency generation pass: get_requires_for_build_wheel
+ rm -rfv '*.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-xarray-2023.1.0-1.fc38.buildreqs.nosrc.rpm
Child return code was: 11
Dynamic buildrequires detected
Going to install missing buildrequires. See root.log for details.
ENTER ['do_with_status'](['bash', '--login', '-c', '/usr/bin/rpmbuild -br --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'], chrootPath='/var/lib/mock/f38-build-side-42-init-devel-1090490-30049/root'env={'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'}shell=Falselogger=<mockbuild.trace_decorator.getLog object at 0xffffff737fbed0>timeout=864000uid=996gid=135user='mockbuild'nspawn_args=[]unshare_net=TrueraiseExc=FalseprintOutput=False)
Executing command: ['bash', '--login', '-c', '/usr/bin/rpmbuild -br --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'] with env {'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'} and shell False
Building target platforms: noarch
Building for target noarch
setting SOURCE_DATE_EPOCH=1691798400
Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.UAmP82
+ umask 022
+ cd /builddir/build/BUILD
+ cd /builddir/build/BUILD
+ rm -rf xarray-2023.1.0
+ /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/xarray-2023.1.0.tar.gz
+ STATUS=0
+ '[' 0 -ne 0 ']'
+ cd xarray-2023.1.0
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w .
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0001-DOC-Don-t-download-RGB.byte.tif-during-build.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0002-DOC-Skip-examples-using-unpackaged-dependencies.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0003-DOC-Don-t-print-out-conda-pip-environment.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.YocfGr
+ umask 022
+ cd /builddir/build/BUILD
+ cd xarray-2023.1.0
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(pip) >= 19'
+ echo 'python3dist(packaging)'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(toml) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ TMPDIR=/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ RPM_TOXENV=py311
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir -r
Handling setuptools>=42 from build-system.requires
Requirement satisfied: setuptools>=42
   (installed: setuptools 65.5.1)
Handling setuptools-scm>=7 from build-system.requires
Requirement satisfied: setuptools-scm>=7
   (installed: setuptools-scm 7.1.0)
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:295: UserWarning: git archive did not support describe output
  warnings.warn("git archive did not support describe output")
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:312: UserWarning: unexported git archival found
  warnings.warn("unexported git archival found")
HOOK STDOUT: running egg_info
HOOK STDOUT: writing xarray.egg-info/PKG-INFO
HOOK STDOUT: writing dependency_links to xarray.egg-info/dependency_links.txt
HOOK STDOUT: writing requirements to xarray.egg-info/requires.txt
HOOK STDOUT: writing top-level names to xarray.egg-info/top_level.txt
HOOK STDOUT: reading manifest file 'xarray.egg-info/SOURCES.txt'
HOOK STDOUT: adding license file 'LICENSE'
HOOK STDOUT: writing manifest file 'xarray.egg-info/SOURCES.txt'
Handling wheel from get_requires_for_build_wheel
Requirement satisfied: wheel
   (installed: wheel 0.38.4)
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:295: UserWarning: git archive did not support describe output
  warnings.warn("git archive did not support describe output")
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:312: UserWarning: unexported git archival found
  warnings.warn("unexported git archival found")
HOOK STDOUT: running dist_info
HOOK STDOUT: writing xarray.egg-info/PKG-INFO
HOOK STDOUT: writing dependency_links to xarray.egg-info/dependency_links.txt
HOOK STDOUT: writing requirements to xarray.egg-info/requires.txt
HOOK STDOUT: writing top-level names to xarray.egg-info/top_level.txt
HOOK STDOUT: reading manifest file 'xarray.egg-info/SOURCES.txt'
HOOK STDOUT: adding license file 'LICENSE'
HOOK STDOUT: writing manifest file 'xarray.egg-info/SOURCES.txt'
HOOK STDOUT: creating '/builddir/build/BUILD/xarray-2023.1.0/xarray-2023.1.0.dist-info'
Handling numpy (>=1.20) from hook generated metadata: Requires-Dist
Requirement satisfied: numpy (>=1.20)
   (installed: numpy 1.24.3)
Handling pandas (>=1.3) from hook generated metadata: Requires-Dist
Requirement satisfied: pandas (>=1.3)
   (installed: pandas 1.5.3)
Handling packaging (>=21.3) from hook generated metadata: Requires-Dist
Requirement satisfied: packaging (>=21.3)
   (installed: packaging 23.0)
Handling scipy ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'accel'
Handling bottleneck ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: bottleneck ; extra == 'accel'
Handling numbagg ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: numbagg ; extra == 'accel'
Handling flox ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: flox ; extra == 'accel'
Handling netCDF4 ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: netCDF4 ; extra == 'complete'
Handling h5netcdf ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: h5netcdf ; extra == 'complete'
Handling scipy ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'complete'
Handling zarr ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: zarr ; extra == 'complete'
Handling fsspec ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: fsspec ; extra == 'complete'
Handling cftime ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cftime ; extra == 'complete'
Handling rasterio ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: rasterio ; extra == 'complete'
Handling cfgrib ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cfgrib ; extra == 'complete'
Handling pooch ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pooch ; extra == 'complete'
Handling bottleneck ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: bottleneck ; extra == 'complete'
Handling numbagg ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: numbagg ; extra == 'complete'
Handling flox ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: flox ; extra == 'complete'
Handling dask[complete] ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: dask[complete] ; extra == 'complete'
Handling matplotlib ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: matplotlib ; extra == 'complete'
Handling seaborn ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: seaborn ; extra == 'complete'
Handling nc-time-axis ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nc-time-axis ; extra == 'complete'
Handling pydap ; (python_version < "3.10") and extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pydap ; (python_version < "3.10") and extra == 'complete'
Handling netCDF4 ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: netCDF4 ; extra == 'docs'
Handling h5netcdf ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: h5netcdf ; extra == 'docs'
Handling scipy ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'docs'
Handling zarr ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: zarr ; extra == 'docs'
Handling fsspec ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: fsspec ; extra == 'docs'
Handling cftime ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cftime ; extra == 'docs'
Handling rasterio ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: rasterio ; extra == 'docs'
Handling cfgrib ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cfgrib ; extra == 'docs'
Handling pooch ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pooch ; extra == 'docs'
Handling bottleneck ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: bottleneck ; extra == 'docs'
Handling numbagg ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: numbagg ; extra == 'docs'
Handling flox ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: flox ; extra == 'docs'
Handling dask[complete] ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: dask[complete] ; extra == 'docs'
Handling matplotlib ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: matplotlib ; extra == 'docs'
Handling seaborn ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: seaborn ; extra == 'docs'
Handling nc-time-axis ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nc-time-axis ; extra == 'docs'
Handling sphinx-autosummary-accessors ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: sphinx-autosummary-accessors ; extra == 'docs'
Handling sphinx-rtd-theme ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: sphinx-rtd-theme ; extra == 'docs'
Handling ipython ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: ipython ; extra == 'docs'
Handling ipykernel ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: ipykernel ; extra == 'docs'
Handling jupyter-client ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: jupyter-client ; extra == 'docs'
Handling nbsphinx ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nbsphinx ; extra == 'docs'
Handling scanpydoc ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scanpydoc ; extra == 'docs'
Handling pydap ; (python_version < "3.10") and extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pydap ; (python_version < "3.10") and extra == 'docs'
Handling netCDF4 ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: netCDF4 ; extra == 'io'
Handling h5netcdf ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: h5netcdf ; extra == 'io'
Handling scipy ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'io'
Handling zarr ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: zarr ; extra == 'io'
Handling fsspec ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: fsspec ; extra == 'io'
Handling cftime ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cftime ; extra == 'io'
Handling rasterio ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: rasterio ; extra == 'io'
Handling cfgrib ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cfgrib ; extra == 'io'
Handling pooch ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pooch ; extra == 'io'
Handling pydap ; (python_version < "3.10") and extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pydap ; (python_version < "3.10") and extra == 'io'
Handling dask[complete] ; extra == 'parallel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: dask[complete] ; extra == 'parallel'
Handling matplotlib ; extra == 'viz' from hook generated metadata: Requires-Dist
Ignoring alien requirement: matplotlib ; extra == 'viz'
Handling seaborn ; extra == 'viz' from hook generated metadata: Requires-Dist
Ignoring alien requirement: seaborn ; extra == 'viz'
Handling nc-time-axis ; extra == 'viz' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nc-time-axis ; extra == 'viz'
+ rm -rfv xarray-2023.1.0.dist-info/
removed 'xarray-2023.1.0.dist-info/LICENSE'
removed 'xarray-2023.1.0.dist-info/top_level.txt'
removed 'xarray-2023.1.0.dist-info/METADATA'
removed directory 'xarray-2023.1.0.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-xarray-2023.1.0-1.fc38.buildreqs.nosrc.rpm
Child return code was: 11
Dynamic buildrequires detected
Going to install missing buildrequires. See root.log for details.
ENTER ['do_with_status'](['bash', '--login', '-c', '/usr/bin/rpmbuild -ba --noprep --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'], chrootPath='/var/lib/mock/f38-build-side-42-init-devel-1090490-30049/root'env={'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'}shell=Falselogger=<mockbuild.trace_decorator.getLog object at 0xffffff737fbed0>timeout=864000uid=996gid=135user='mockbuild'nspawn_args=[]unshare_net=TrueprintOutput=False)
Executing command: ['bash', '--login', '-c', '/usr/bin/rpmbuild -ba --noprep --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec'] with env {'TERM': 'vt100', 'SHELL': '/bin/bash', 'HOME': '/builddir', 'HOSTNAME': 'mock', 'PATH': '/usr/bin:/bin:/usr/sbin:/sbin', 'PROMPT_COMMAND': 'printf "\\033]0;<mock-chroot>\\007"', 'PS1': '<mock-chroot> \\s-\\v\\$ ', 'LANG': 'C.UTF-8'} and shell False
Building target platforms: noarch
Building for target noarch
setting SOURCE_DATE_EPOCH=1691798400
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.e6k1Ir
+ umask 022
+ cd /builddir/build/BUILD
+ cd xarray-2023.1.0
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(pip) >= 19'
+ echo 'python3dist(packaging)'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(toml) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ TMPDIR=/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ RPM_TOXENV=py311
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir -r
Handling setuptools>=42 from build-system.requires
Requirement satisfied: setuptools>=42
   (installed: setuptools 65.5.1)
Handling setuptools-scm>=7 from build-system.requires
Requirement satisfied: setuptools-scm>=7
   (installed: setuptools-scm 7.1.0)
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:295: UserWarning: git archive did not support describe output
  warnings.warn("git archive did not support describe output")
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:312: UserWarning: unexported git archival found
  warnings.warn("unexported git archival found")
HOOK STDOUT: running egg_info
HOOK STDOUT: writing xarray.egg-info/PKG-INFO
HOOK STDOUT: writing dependency_links to xarray.egg-info/dependency_links.txt
HOOK STDOUT: writing requirements to xarray.egg-info/requires.txt
HOOK STDOUT: writing top-level names to xarray.egg-info/top_level.txt
HOOK STDOUT: reading manifest file 'xarray.egg-info/SOURCES.txt'
HOOK STDOUT: adding license file 'LICENSE'
HOOK STDOUT: writing manifest file 'xarray.egg-info/SOURCES.txt'
Handling wheel from get_requires_for_build_wheel
Requirement satisfied: wheel
   (installed: wheel 0.38.4)
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:295: UserWarning: git archive did not support describe output
  warnings.warn("git archive did not support describe output")
/usr/lib/python3.11/site-packages/setuptools_scm/git.py:312: UserWarning: unexported git archival found
  warnings.warn("unexported git archival found")
HOOK STDOUT: running dist_info
HOOK STDOUT: writing xarray.egg-info/PKG-INFO
HOOK STDOUT: writing dependency_links to xarray.egg-info/dependency_links.txt
HOOK STDOUT: writing requirements to xarray.egg-info/requires.txt
HOOK STDOUT: writing top-level names to xarray.egg-info/top_level.txt
HOOK STDOUT: reading manifest file 'xarray.egg-info/SOURCES.txt'
HOOK STDOUT: adding license file 'LICENSE'
HOOK STDOUT: writing manifest file 'xarray.egg-info/SOURCES.txt'
HOOK STDOUT: creating '/builddir/build/BUILD/xarray-2023.1.0/xarray-2023.1.0.dist-info'
Handling numpy (>=1.20) from hook generated metadata: Requires-Dist
Requirement satisfied: numpy (>=1.20)
   (installed: numpy 1.24.3)
Handling pandas (>=1.3) from hook generated metadata: Requires-Dist
Requirement satisfied: pandas (>=1.3)
   (installed: pandas 1.5.3)
Handling packaging (>=21.3) from hook generated metadata: Requires-Dist
Requirement satisfied: packaging (>=21.3)
   (installed: packaging 23.0)
Handling scipy ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'accel'
Handling bottleneck ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: bottleneck ; extra == 'accel'
Handling numbagg ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: numbagg ; extra == 'accel'
Handling flox ; extra == 'accel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: flox ; extra == 'accel'
Handling netCDF4 ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: netCDF4 ; extra == 'complete'
Handling h5netcdf ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: h5netcdf ; extra == 'complete'
Handling scipy ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'complete'
Handling zarr ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: zarr ; extra == 'complete'
Handling fsspec ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: fsspec ; extra == 'complete'
Handling cftime ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cftime ; extra == 'complete'
Handling rasterio ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: rasterio ; extra == 'complete'
Handling cfgrib ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cfgrib ; extra == 'complete'
Handling pooch ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pooch ; extra == 'complete'
Handling bottleneck ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: bottleneck ; extra == 'complete'
Handling numbagg ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: numbagg ; extra == 'complete'
Handling flox ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: flox ; extra == 'complete'
Handling dask[complete] ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: dask[complete] ; extra == 'complete'
Handling matplotlib ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: matplotlib ; extra == 'complete'
Handling seaborn ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: seaborn ; extra == 'complete'
Handling nc-time-axis ; extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nc-time-axis ; extra == 'complete'
Handling pydap ; (python_version < "3.10") and extra == 'complete' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pydap ; (python_version < "3.10") and extra == 'complete'
Handling netCDF4 ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: netCDF4 ; extra == 'docs'
Handling h5netcdf ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: h5netcdf ; extra == 'docs'
Handling scipy ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'docs'
Handling zarr ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: zarr ; extra == 'docs'
Handling fsspec ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: fsspec ; extra == 'docs'
Handling cftime ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cftime ; extra == 'docs'
Handling rasterio ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: rasterio ; extra == 'docs'
Handling cfgrib ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cfgrib ; extra == 'docs'
Handling pooch ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pooch ; extra == 'docs'
Handling bottleneck ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: bottleneck ; extra == 'docs'
Handling numbagg ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: numbagg ; extra == 'docs'
Handling flox ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: flox ; extra == 'docs'
Handling dask[complete] ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: dask[complete] ; extra == 'docs'
Handling matplotlib ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: matplotlib ; extra == 'docs'
Handling seaborn ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: seaborn ; extra == 'docs'
Handling nc-time-axis ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nc-time-axis ; extra == 'docs'
Handling sphinx-autosummary-accessors ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: sphinx-autosummary-accessors ; extra == 'docs'
Handling sphinx-rtd-theme ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: sphinx-rtd-theme ; extra == 'docs'
Handling ipython ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: ipython ; extra == 'docs'
Handling ipykernel ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: ipykernel ; extra == 'docs'
Handling jupyter-client ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: jupyter-client ; extra == 'docs'
Handling nbsphinx ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nbsphinx ; extra == 'docs'
Handling scanpydoc ; extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scanpydoc ; extra == 'docs'
Handling pydap ; (python_version < "3.10") and extra == 'docs' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pydap ; (python_version < "3.10") and extra == 'docs'
Handling netCDF4 ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: netCDF4 ; extra == 'io'
Handling h5netcdf ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: h5netcdf ; extra == 'io'
Handling scipy ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: scipy ; extra == 'io'
Handling zarr ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: zarr ; extra == 'io'
Handling fsspec ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: fsspec ; extra == 'io'
Handling cftime ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cftime ; extra == 'io'
Handling rasterio ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: rasterio ; extra == 'io'
Handling cfgrib ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: cfgrib ; extra == 'io'
Handling pooch ; extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pooch ; extra == 'io'
Handling pydap ; (python_version < "3.10") and extra == 'io' from hook generated metadata: Requires-Dist
Ignoring alien requirement: pydap ; (python_version < "3.10") and extra == 'io'
Handling dask[complete] ; extra == 'parallel' from hook generated metadata: Requires-Dist
Ignoring alien requirement: dask[complete] ; extra == 'parallel'
Handling matplotlib ; extra == 'viz' from hook generated metadata: Requires-Dist
Ignoring alien requirement: matplotlib ; extra == 'viz'
Handling seaborn ; extra == 'viz' from hook generated metadata: Requires-Dist
Ignoring alien requirement: seaborn ; extra == 'viz'
Handling nc-time-axis ; extra == 'viz' from hook generated metadata: Requires-Dist
Ignoring alien requirement: nc-time-axis ; extra == 'viz'
+ rm -rfv xarray-2023.1.0.dist-info/
removed 'xarray-2023.1.0.dist-info/LICENSE'
removed 'xarray-2023.1.0.dist-info/top_level.txt'
removed 'xarray-2023.1.0.dist-info/METADATA'
removed directory 'xarray-2023.1.0.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%build): /bin/sh -e /var/tmp/rpm-tmp.DYbax3
+ umask 022
+ cd /builddir/build/BUILD
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd xarray-2023.1.0
+ mkdir -p /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ TMPDIR=/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_wheel.py /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir
Processing /builddir/build/BUILD/xarray-2023.1.0
  Preparing metadata (pyproject.toml): started
  Running command Preparing metadata (pyproject.toml)
  /usr/lib/python3.11/site-packages/setuptools_scm/git.py:295: UserWarning: git archive did not support describe output
    warnings.warn("git archive did not support describe output")
  /usr/lib/python3.11/site-packages/setuptools_scm/git.py:312: UserWarning: unexported git archival found
    warnings.warn("unexported git archival found")
  running dist_info
  creating /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info
  writing /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info/PKG-INFO
  writing dependency_links to /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info/dependency_links.txt
  writing requirements to /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info/requires.txt
  writing top-level names to /builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info/top_level.txt
  writing manifest file '/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info/SOURCES.txt'
  reading manifest file '/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info/SOURCES.txt'
  adding license file 'LICENSE'
  writing manifest file '/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray.egg-info/SOURCES.txt'
  creating '/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-modern-metadata-hvx9wz8u/xarray-2023.1.0.dist-info'
  Preparing metadata (pyproject.toml): finished with status 'done'
Building wheels for collected packages: xarray
  Building wheel for xarray (pyproject.toml): started
  Running command Building wheel for xarray (pyproject.toml)
  /usr/lib/python3.11/site-packages/setuptools_scm/git.py:295: UserWarning: git archive did not support describe output
    warnings.warn("git archive did not support describe output")
  /usr/lib/python3.11/site-packages/setuptools_scm/git.py:312: UserWarning: unexported git archival found
    warnings.warn("unexported git archival found")
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build/lib
  creating build/lib/xarray
  copying xarray/conventions.py -> build/lib/xarray
  copying xarray/tutorial.py -> build/lib/xarray
  copying xarray/convert.py -> build/lib/xarray
  copying xarray/__init__.py -> build/lib/xarray
  copying xarray/testing.py -> build/lib/xarray
  creating build/lib/xarray/coding
  copying xarray/coding/strings.py -> build/lib/xarray/coding
  copying xarray/coding/variables.py -> build/lib/xarray/coding
  copying xarray/coding/cftime_offsets.py -> build/lib/xarray/coding
  copying xarray/coding/times.py -> build/lib/xarray/coding
  copying xarray/coding/cftimeindex.py -> build/lib/xarray/coding
  copying xarray/coding/calendar_ops.py -> build/lib/xarray/coding
  copying xarray/coding/__init__.py -> build/lib/xarray/coding
  copying xarray/coding/frequencies.py -> build/lib/xarray/coding
  creating build/lib/xarray/static
  copying xarray/static/__init__.py -> build/lib/xarray/static
  creating build/lib/xarray/plot
  copying xarray/plot/dataarray_plot.py -> build/lib/xarray/plot
  copying xarray/plot/facetgrid.py -> build/lib/xarray/plot
  copying xarray/plot/accessor.py -> build/lib/xarray/plot
  copying xarray/plot/__init__.py -> build/lib/xarray/plot
  copying xarray/plot/utils.py -> build/lib/xarray/plot
  copying xarray/plot/dataset_plot.py -> build/lib/xarray/plot
  creating build/lib/xarray/util
  copying xarray/util/deprecation_helpers.py -> build/lib/xarray/util
  copying xarray/util/generate_aggregations.py -> build/lib/xarray/util
  copying xarray/util/print_versions.py -> build/lib/xarray/util
  copying xarray/util/generate_ops.py -> build/lib/xarray/util
  copying xarray/util/__init__.py -> build/lib/xarray/util
  creating build/lib/xarray/tests
  copying xarray/tests/test_weighted.py -> build/lib/xarray/tests
  copying xarray/tests/test_concat.py -> build/lib/xarray/tests
  copying xarray/tests/test_ufuncs.py -> build/lib/xarray/tests
  copying xarray/tests/test_coding_times.py -> build/lib/xarray/tests
  copying xarray/tests/test_deprecation_helpers.py -> build/lib/xarray/tests
  copying xarray/tests/test_accessor_dt.py -> build/lib/xarray/tests
  copying xarray/tests/test_backends_common.py -> build/lib/xarray/tests
  copying xarray/tests/test_cupy.py -> build/lib/xarray/tests
  copying xarray/tests/test_utils.py -> build/lib/xarray/tests
  copying xarray/tests/test_distributed.py -> build/lib/xarray/tests
  copying xarray/tests/test_dataset.py -> build/lib/xarray/tests
  copying xarray/tests/test_plot.py -> build/lib/xarray/tests
  copying xarray/tests/test_interp.py -> build/lib/xarray/tests
  copying xarray/tests/test_cftimeindex_resample.py -> build/lib/xarray/tests
  copying xarray/tests/test_merge.py -> build/lib/xarray/tests
  copying xarray/tests/test_cftimeindex.py -> build/lib/xarray/tests
  copying xarray/tests/test_testing.py -> build/lib/xarray/tests
  copying xarray/tests/test_dtypes.py -> build/lib/xarray/tests
  copying xarray/tests/test_duck_array_ops.py -> build/lib/xarray/tests
  copying xarray/tests/test_formatting.py -> build/lib/xarray/tests
  copying xarray/tests/test_calendar_ops.py -> build/lib/xarray/tests
  copying xarray/tests/test_tutorial.py -> build/lib/xarray/tests
  copying xarray/tests/test_formatting_html.py -> build/lib/xarray/tests
  copying xarray/tests/test_backends_api.py -> build/lib/xarray/tests
  copying xarray/tests/test_backends_file_manager.py -> build/lib/xarray/tests
  copying xarray/tests/test_array_api.py -> build/lib/xarray/tests
  copying xarray/tests/test_extensions.py -> build/lib/xarray/tests
  copying xarray/tests/test_combine.py -> build/lib/xarray/tests
  copying xarray/tests/test_units.py -> build/lib/xarray/tests
  copying xarray/tests/test_coding_strings.py -> build/lib/xarray/tests
  copying xarray/tests/test_dataarray.py -> build/lib/xarray/tests
  copying xarray/tests/test_coding.py -> build/lib/xarray/tests
  copying xarray/tests/test_indexing.py -> build/lib/xarray/tests
  copying xarray/tests/test_options.py -> build/lib/xarray/tests
  copying xarray/tests/test_accessor_str.py -> build/lib/xarray/tests
  copying xarray/tests/test_indexes.py -> build/lib/xarray/tests
  copying xarray/tests/test_cftime_offsets.py -> build/lib/xarray/tests
  copying xarray/tests/test_backends_lru_cache.py -> build/lib/xarray/tests
  copying xarray/tests/test_plugins.py -> build/lib/xarray/tests
  copying xarray/tests/test_backends_locks.py -> build/lib/xarray/tests
  copying xarray/tests/test_missing.py -> build/lib/xarray/tests
  copying xarray/tests/test_nputils.py -> build/lib/xarray/tests
  copying xarray/tests/test_rolling.py -> build/lib/xarray/tests
  copying xarray/tests/test_dask.py -> build/lib/xarray/tests
  copying xarray/tests/conftest.py -> build/lib/xarray/tests
  copying xarray/tests/__init__.py -> build/lib/xarray/tests
  copying xarray/tests/test_conventions.py -> build/lib/xarray/tests
  copying xarray/tests/test_variable.py -> build/lib/xarray/tests
  copying xarray/tests/test_groupby.py -> build/lib/xarray/tests
  copying xarray/tests/test_print_versions.py -> build/lib/xarray/tests
  copying xarray/tests/test_sparse.py -> build/lib/xarray/tests
  copying xarray/tests/test_backends.py -> build/lib/xarray/tests
  copying xarray/tests/test_computation.py -> build/lib/xarray/tests
  copying xarray/tests/test_coarsen.py -> build/lib/xarray/tests
  creating build/lib/xarray/indexes
  copying xarray/indexes/__init__.py -> build/lib/xarray/indexes
  creating build/lib/xarray/core
  copying xarray/core/npcompat.py -> build/lib/xarray/core
  copying xarray/core/dataarray.py -> build/lib/xarray/core
  copying xarray/core/common.py -> build/lib/xarray/core
  copying xarray/core/nanops.py -> build/lib/xarray/core
  copying xarray/core/dataset.py -> build/lib/xarray/core
  copying xarray/core/arithmetic.py -> build/lib/xarray/core
  copying xarray/core/ops.py -> build/lib/xarray/core
  copying xarray/core/dask_array_ops.py -> build/lib/xarray/core
  copying xarray/core/rolling_exp.py -> build/lib/xarray/core
  copying xarray/core/accessor_dt.py -> build/lib/xarray/core
  copying xarray/core/merge.py -> build/lib/xarray/core
  copying xarray/core/_aggregations.py -> build/lib/xarray/core
  copying xarray/core/extensions.py -> build/lib/xarray/core
  copying xarray/core/formatting_html.py -> build/lib/xarray/core
  copying xarray/core/formatting.py -> build/lib/xarray/core
  copying xarray/core/resample.py -> build/lib/xarray/core
  copying xarray/core/indexes.py -> build/lib/xarray/core
  copying xarray/core/groupby.py -> build/lib/xarray/core
  copying xarray/core/weighted.py -> build/lib/xarray/core
  copying xarray/core/parallel.py -> build/lib/xarray/core
  copying xarray/core/concat.py -> build/lib/xarray/core
  copying xarray/core/indexing.py -> build/lib/xarray/core
  copying xarray/core/nputils.py -> build/lib/xarray/core
  copying xarray/core/pycompat.py -> build/lib/xarray/core
  copying xarray/core/rolling.py -> build/lib/xarray/core
  copying xarray/core/resample_cftime.py -> build/lib/xarray/core
  copying xarray/core/alignment.py -> build/lib/xarray/core
  copying xarray/core/dtypes.py -> build/lib/xarray/core
  copying xarray/core/pdcompat.py -> build/lib/xarray/core
  copying xarray/core/options.py -> build/lib/xarray/core
  copying xarray/core/types.py -> build/lib/xarray/core
  copying xarray/core/variable.py -> build/lib/xarray/core
  copying xarray/core/missing.py -> build/lib/xarray/core
  copying xarray/core/accessor_str.py -> build/lib/xarray/core
  copying xarray/core/duck_array_ops.py -> build/lib/xarray/core
  copying xarray/core/computation.py -> build/lib/xarray/core
  copying xarray/core/_typed_ops.py -> build/lib/xarray/core
  copying xarray/core/__init__.py -> build/lib/xarray/core
  copying xarray/core/utils.py -> build/lib/xarray/core
  copying xarray/core/coordinates.py -> build/lib/xarray/core
  copying xarray/core/combine.py -> build/lib/xarray/core
  creating build/lib/xarray/backends
  copying xarray/backends/pydap_.py -> build/lib/xarray/backends
  copying xarray/backends/common.py -> build/lib/xarray/backends
  copying xarray/backends/plugins.py -> build/lib/xarray/backends
  copying xarray/backends/api.py -> build/lib/xarray/backends
  copying xarray/backends/netcdf3.py -> build/lib/xarray/backends
  copying xarray/backends/netCDF4_.py -> build/lib/xarray/backends
  copying xarray/backends/h5netcdf_.py -> build/lib/xarray/backends
  copying xarray/backends/scipy_.py -> build/lib/xarray/backends
  copying xarray/backends/pseudonetcdf_.py -> build/lib/xarray/backends
  copying xarray/backends/lru_cache.py -> build/lib/xarray/backends
  copying xarray/backends/locks.py -> build/lib/xarray/backends
  copying xarray/backends/store.py -> build/lib/xarray/backends
  copying xarray/backends/pynio_.py -> build/lib/xarray/backends
  copying xarray/backends/file_manager.py -> build/lib/xarray/backends
  copying xarray/backends/memory.py -> build/lib/xarray/backends
  copying xarray/backends/cfgrib_.py -> build/lib/xarray/backends
  copying xarray/backends/zarr.py -> build/lib/xarray/backends
  copying xarray/backends/__init__.py -> build/lib/xarray/backends
  copying xarray/backends/rasterio_.py -> build/lib/xarray/backends
  creating build/lib/xarray/static/html
  copying xarray/static/html/__init__.py -> build/lib/xarray/static/html
  creating build/lib/xarray/static/css
  copying xarray/static/css/__init__.py -> build/lib/xarray/static/css
  running egg_info
  writing xarray.egg-info/PKG-INFO
  writing dependency_links to xarray.egg-info/dependency_links.txt
  writing requirements to xarray.egg-info/requires.txt
  writing top-level names to xarray.egg-info/top_level.txt
  reading manifest file 'xarray.egg-info/SOURCES.txt'
  adding license file 'LICENSE'
  writing manifest file 'xarray.egg-info/SOURCES.txt'
  /usr/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'xarray.tests.data' as data is deprecated, please list it in `packages`.
      !!
      ############################
      # Package would be ignored #
      ############################
      Python recognizes 'xarray.tests.data' as an importable package,
      but it is not listed in the `packages` configuration of setuptools.
      'xarray.tests.data' has been automatically added to the distribution only
      because it may contain data files, but this behavior is likely to change
      in future versions of setuptools (and therefore is considered deprecated).
      Please make sure that 'xarray.tests.data' is included as a package by using
      the `packages` configuration field or the proper discovery methods
      (for example by using `find_namespace_packages(...)`/`find_namespace:`
      instead of `find_packages(...)`/`find:`).
      You can read more about "package discovery" and "data files" on setuptools
      documentation page.
  !!
    check.warn(importable)
  copying xarray/py.typed -> build/lib/xarray
  creating build/lib/xarray/tests/data
  copying xarray/tests/data/example.grib -> build/lib/xarray/tests/data
  copying xarray/tests/data/example_1.nc -> build/lib/xarray/tests/data
  copying xarray/tests/data/bears.nc -> build/lib/xarray/tests/data
  copying xarray/tests/data/example.uamiv -> build/lib/xarray/tests/data
  copying xarray/tests/data/example.ict -> build/lib/xarray/tests/data
  copying xarray/tests/data/example_1.nc.gz -> build/lib/xarray/tests/data
  copying xarray/static/css/style.css -> build/lib/xarray/static/css
  copying xarray/static/html/icons-svg-inline.html -> build/lib/xarray/static/html
  copying xarray/core/_typed_ops.pyi -> build/lib/xarray/core
  installing to build/bdist.linux-riscv64/wheel
  running install
  running install_lib
  creating build/bdist.linux-riscv64
  creating build/bdist.linux-riscv64/wheel
  creating build/bdist.linux-riscv64/wheel/xarray
  creating build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/strings.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/variables.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/cftime_offsets.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/times.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/cftimeindex.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/calendar_ops.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/coding/frequencies.py -> build/bdist.linux-riscv64/wheel/xarray/coding
  copying build/lib/xarray/conventions.py -> build/bdist.linux-riscv64/wheel/xarray
  copying build/lib/xarray/tutorial.py -> build/bdist.linux-riscv64/wheel/xarray
  creating build/bdist.linux-riscv64/wheel/xarray/static
  creating build/bdist.linux-riscv64/wheel/xarray/static/html
  copying build/lib/xarray/static/html/icons-svg-inline.html -> build/bdist.linux-riscv64/wheel/xarray/static/html
  copying build/lib/xarray/static/html/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/static/html
  creating build/bdist.linux-riscv64/wheel/xarray/static/css
  copying build/lib/xarray/static/css/style.css -> build/bdist.linux-riscv64/wheel/xarray/static/css
  copying build/lib/xarray/static/css/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/static/css
  copying build/lib/xarray/static/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/static
  creating build/bdist.linux-riscv64/wheel/xarray/plot
  copying build/lib/xarray/plot/dataarray_plot.py -> build/bdist.linux-riscv64/wheel/xarray/plot
  copying build/lib/xarray/plot/facetgrid.py -> build/bdist.linux-riscv64/wheel/xarray/plot
  copying build/lib/xarray/plot/accessor.py -> build/bdist.linux-riscv64/wheel/xarray/plot
  copying build/lib/xarray/plot/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/plot
  copying build/lib/xarray/plot/utils.py -> build/bdist.linux-riscv64/wheel/xarray/plot
  copying build/lib/xarray/plot/dataset_plot.py -> build/bdist.linux-riscv64/wheel/xarray/plot
  creating build/bdist.linux-riscv64/wheel/xarray/util
  copying build/lib/xarray/util/deprecation_helpers.py -> build/bdist.linux-riscv64/wheel/xarray/util
  copying build/lib/xarray/util/generate_aggregations.py -> build/bdist.linux-riscv64/wheel/xarray/util
  copying build/lib/xarray/util/print_versions.py -> build/bdist.linux-riscv64/wheel/xarray/util
  copying build/lib/xarray/util/generate_ops.py -> build/bdist.linux-riscv64/wheel/xarray/util
  copying build/lib/xarray/util/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/util
  copying build/lib/xarray/convert.py -> build/bdist.linux-riscv64/wheel/xarray
  copying build/lib/xarray/py.typed -> build/bdist.linux-riscv64/wheel/xarray
  creating build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_weighted.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_concat.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_ufuncs.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_coding_times.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_deprecation_helpers.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_accessor_dt.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_backends_common.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_cupy.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_utils.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_distributed.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_dataset.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_plot.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_interp.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_cftimeindex_resample.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_merge.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_cftimeindex.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_testing.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_dtypes.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_duck_array_ops.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_formatting.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_calendar_ops.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_tutorial.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_formatting_html.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_backends_api.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_backends_file_manager.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_array_api.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_extensions.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_combine.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_units.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  creating build/bdist.linux-riscv64/wheel/xarray/tests/data
  copying build/lib/xarray/tests/data/example.grib -> build/bdist.linux-riscv64/wheel/xarray/tests/data
  copying build/lib/xarray/tests/data/example_1.nc -> build/bdist.linux-riscv64/wheel/xarray/tests/data
  copying build/lib/xarray/tests/data/bears.nc -> build/bdist.linux-riscv64/wheel/xarray/tests/data
  copying build/lib/xarray/tests/data/example.uamiv -> build/bdist.linux-riscv64/wheel/xarray/tests/data
  copying build/lib/xarray/tests/data/example.ict -> build/bdist.linux-riscv64/wheel/xarray/tests/data
  copying build/lib/xarray/tests/data/example_1.nc.gz -> build/bdist.linux-riscv64/wheel/xarray/tests/data
  copying build/lib/xarray/tests/test_coding_strings.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_dataarray.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_coding.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_indexing.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_options.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_accessor_str.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_indexes.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_cftime_offsets.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_backends_lru_cache.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_plugins.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_backends_locks.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_missing.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_nputils.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_rolling.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_dask.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/conftest.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_conventions.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_variable.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_groupby.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_print_versions.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_sparse.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_backends.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_computation.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  copying build/lib/xarray/tests/test_coarsen.py -> build/bdist.linux-riscv64/wheel/xarray/tests
  creating build/bdist.linux-riscv64/wheel/xarray/indexes
  copying build/lib/xarray/indexes/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/indexes
  copying build/lib/xarray/__init__.py -> build/bdist.linux-riscv64/wheel/xarray
  creating build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/npcompat.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/dataarray.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/common.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/nanops.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/dataset.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/arithmetic.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/ops.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/dask_array_ops.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/rolling_exp.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/accessor_dt.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/merge.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/_aggregations.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/extensions.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/formatting_html.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/formatting.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/resample.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/indexes.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/groupby.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/weighted.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/parallel.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/concat.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/indexing.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/nputils.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/pycompat.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/rolling.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/resample_cftime.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/alignment.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/dtypes.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/pdcompat.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/_typed_ops.pyi -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/options.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/types.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/variable.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/missing.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/accessor_str.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/duck_array_ops.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/computation.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/_typed_ops.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/utils.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/coordinates.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/core/combine.py -> build/bdist.linux-riscv64/wheel/xarray/core
  copying build/lib/xarray/testing.py -> build/bdist.linux-riscv64/wheel/xarray
  creating build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/pydap_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/common.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/plugins.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/api.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/netcdf3.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/netCDF4_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/h5netcdf_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/scipy_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/pseudonetcdf_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/lru_cache.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/locks.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/store.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/pynio_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/file_manager.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/memory.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/cfgrib_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/zarr.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/__init__.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  copying build/lib/xarray/backends/rasterio_.py -> build/bdist.linux-riscv64/wheel/xarray/backends
  running install_egg_info
  Copying xarray.egg-info to build/bdist.linux-riscv64/wheel/xarray-2023.1.0-py3.11.egg-info
  running install_scripts
  creating build/bdist.linux-riscv64/wheel/xarray-2023.1.0.dist-info/WHEEL
  creating '/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir/pip-wheel-70w5i0i3/tmpvcdq2cfs/xarray-2023.1.0-py3-none-any.whl' and adding 'build/bdist.linux-riscv64/wheel' to it
  adding 'xarray/__init__.py'
  adding 'xarray/conventions.py'
  adding 'xarray/convert.py'
  adding 'xarray/py.typed'
  adding 'xarray/testing.py'
  adding 'xarray/tutorial.py'
  adding 'xarray/backends/__init__.py'
  adding 'xarray/backends/api.py'
  adding 'xarray/backends/cfgrib_.py'
  adding 'xarray/backends/common.py'
  adding 'xarray/backends/file_manager.py'
  adding 'xarray/backends/h5netcdf_.py'
  adding 'xarray/backends/locks.py'
  adding 'xarray/backends/lru_cache.py'
  adding 'xarray/backends/memory.py'
  adding 'xarray/backends/netCDF4_.py'
  adding 'xarray/backends/netcdf3.py'
  adding 'xarray/backends/plugins.py'
  adding 'xarray/backends/pseudonetcdf_.py'
  adding 'xarray/backends/pydap_.py'
  adding 'xarray/backends/pynio_.py'
  adding 'xarray/backends/rasterio_.py'
  adding 'xarray/backends/scipy_.py'
  adding 'xarray/backends/store.py'
  adding 'xarray/backends/zarr.py'
  adding 'xarray/coding/__init__.py'
  adding 'xarray/coding/calendar_ops.py'
  adding 'xarray/coding/cftime_offsets.py'
  adding 'xarray/coding/cftimeindex.py'
  adding 'xarray/coding/frequencies.py'
  adding 'xarray/coding/strings.py'
  adding 'xarray/coding/times.py'
  adding 'xarray/coding/variables.py'
  adding 'xarray/core/__init__.py'
  adding 'xarray/core/_aggregations.py'
  adding 'xarray/core/_typed_ops.py'
  adding 'xarray/core/_typed_ops.pyi'
  adding 'xarray/core/accessor_dt.py'
  adding 'xarray/core/accessor_str.py'
  adding 'xarray/core/alignment.py'
  adding 'xarray/core/arithmetic.py'
  adding 'xarray/core/combine.py'
  adding 'xarray/core/common.py'
  adding 'xarray/core/computation.py'
  adding 'xarray/core/concat.py'
  adding 'xarray/core/coordinates.py'
  adding 'xarray/core/dask_array_ops.py'
  adding 'xarray/core/dataarray.py'
  adding 'xarray/core/dataset.py'
  adding 'xarray/core/dtypes.py'
  adding 'xarray/core/duck_array_ops.py'
  adding 'xarray/core/extensions.py'
  adding 'xarray/core/formatting.py'
  adding 'xarray/core/formatting_html.py'
  adding 'xarray/core/groupby.py'
  adding 'xarray/core/indexes.py'
  adding 'xarray/core/indexing.py'
  adding 'xarray/core/merge.py'
  adding 'xarray/core/missing.py'
  adding 'xarray/core/nanops.py'
  adding 'xarray/core/npcompat.py'
  adding 'xarray/core/nputils.py'
  adding 'xarray/core/ops.py'
  adding 'xarray/core/options.py'
  adding 'xarray/core/parallel.py'
  adding 'xarray/core/pdcompat.py'
  adding 'xarray/core/pycompat.py'
  adding 'xarray/core/resample.py'
  adding 'xarray/core/resample_cftime.py'
  adding 'xarray/core/rolling.py'
  adding 'xarray/core/rolling_exp.py'
  adding 'xarray/core/types.py'
  adding 'xarray/core/utils.py'
  adding 'xarray/core/variable.py'
  adding 'xarray/core/weighted.py'
  adding 'xarray/indexes/__init__.py'
  adding 'xarray/plot/__init__.py'
  adding 'xarray/plot/accessor.py'
  adding 'xarray/plot/dataarray_plot.py'
  adding 'xarray/plot/dataset_plot.py'
  adding 'xarray/plot/facetgrid.py'
  adding 'xarray/plot/utils.py'
  adding 'xarray/static/__init__.py'
  adding 'xarray/static/css/__init__.py'
  adding 'xarray/static/css/style.css'
  adding 'xarray/static/html/__init__.py'
  adding 'xarray/static/html/icons-svg-inline.html'
  adding 'xarray/tests/__init__.py'
  adding 'xarray/tests/conftest.py'
  adding 'xarray/tests/test_accessor_dt.py'
  adding 'xarray/tests/test_accessor_str.py'
  adding 'xarray/tests/test_array_api.py'
  adding 'xarray/tests/test_backends.py'
  adding 'xarray/tests/test_backends_api.py'
  adding 'xarray/tests/test_backends_common.py'
  adding 'xarray/tests/test_backends_file_manager.py'
  adding 'xarray/tests/test_backends_locks.py'
  adding 'xarray/tests/test_backends_lru_cache.py'
  adding 'xarray/tests/test_calendar_ops.py'
  adding 'xarray/tests/test_cftime_offsets.py'
  adding 'xarray/tests/test_cftimeindex.py'
  adding 'xarray/tests/test_cftimeindex_resample.py'
  adding 'xarray/tests/test_coarsen.py'
  adding 'xarray/tests/test_coding.py'
  adding 'xarray/tests/test_coding_strings.py'
  adding 'xarray/tests/test_coding_times.py'
  adding 'xarray/tests/test_combine.py'
  adding 'xarray/tests/test_computation.py'
  adding 'xarray/tests/test_concat.py'
  adding 'xarray/tests/test_conventions.py'
  adding 'xarray/tests/test_cupy.py'
  adding 'xarray/tests/test_dask.py'
  adding 'xarray/tests/test_dataarray.py'
  adding 'xarray/tests/test_dataset.py'
  adding 'xarray/tests/test_deprecation_helpers.py'
  adding 'xarray/tests/test_distributed.py'
  adding 'xarray/tests/test_dtypes.py'
  adding 'xarray/tests/test_duck_array_ops.py'
  adding 'xarray/tests/test_extensions.py'
  adding 'xarray/tests/test_formatting.py'
  adding 'xarray/tests/test_formatting_html.py'
  adding 'xarray/tests/test_groupby.py'
  adding 'xarray/tests/test_indexes.py'
  adding 'xarray/tests/test_indexing.py'
  adding 'xarray/tests/test_interp.py'
  adding 'xarray/tests/test_merge.py'
  adding 'xarray/tests/test_missing.py'
  adding 'xarray/tests/test_nputils.py'
  adding 'xarray/tests/test_options.py'
  adding 'xarray/tests/test_plot.py'
  adding 'xarray/tests/test_plugins.py'
  adding 'xarray/tests/test_print_versions.py'
  adding 'xarray/tests/test_rolling.py'
  adding 'xarray/tests/test_sparse.py'
  adding 'xarray/tests/test_testing.py'
  adding 'xarray/tests/test_tutorial.py'
  adding 'xarray/tests/test_ufuncs.py'
  adding 'xarray/tests/test_units.py'
  adding 'xarray/tests/test_utils.py'
  adding 'xarray/tests/test_variable.py'
  adding 'xarray/tests/test_weighted.py'
  adding 'xarray/tests/data/bears.nc'
  adding 'xarray/tests/data/example.grib'
  adding 'xarray/tests/data/example.ict'
  adding 'xarray/tests/data/example.uamiv'
  adding 'xarray/tests/data/example_1.nc'
  adding 'xarray/tests/data/example_1.nc.gz'
  adding 'xarray/util/__init__.py'
  adding 'xarray/util/deprecation_helpers.py'
  adding 'xarray/util/generate_aggregations.py'
  adding 'xarray/util/generate_ops.py'
  adding 'xarray/util/print_versions.py'
  adding 'xarray-2023.1.0.dist-info/LICENSE'
  adding 'xarray-2023.1.0.dist-info/METADATA'
  adding 'xarray-2023.1.0.dist-info/WHEEL'
  adding 'xarray-2023.1.0.dist-info/top_level.txt'
  adding 'xarray-2023.1.0.dist-info/RECORD'
  removing build/bdist.linux-riscv64/wheel
  Building wheel for xarray (pyproject.toml): finished with status 'done'
  Created wheel for xarray: filename=xarray-2023.1.0-py3-none-any.whl size=973057 sha256=fe63005ad08c093bb458fd559cbb8e83f3e710a8914dfcade8416969e74b4a26
  Stored in directory: /builddir/.cache/pip/wheels/f5/76/cd/200bc5fbd725c66d9a664a1beab05a23bb423ce921988615b1
Successfully built xarray
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%install): /bin/sh -e /var/tmp/rpm-tmp.k1j2HG
+ umask 022
+ cd /builddir/build/BUILD
+ '[' /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch '!=' / ']'
+ rm -rf /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch
++ dirname /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch
+ mkdir -p /builddir/build/BUILDROOT
+ mkdir /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd xarray-2023.1.0
++ xargs basename --multiple
++ sed -E 's/([^-]+)-([^-]+)-.+\.whl/\1==\2/'
++ ls /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir/xarray-2023.1.0-py3-none-any.whl
+ specifier=xarray==2023.1.0
+ TMPDIR=/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir
+ /usr/bin/python3 -m pip install --root /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch --prefix /usr --no-deps --disable-pip-version-check --progress-bar off --verbose --ignore-installed --no-warn-script-location --no-index --no-cache-dir --find-links /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir xarray==2023.1.0
Using pip 22.3.1 from /usr/lib/python3.11/site-packages/pip (python 3.11)
Looking in links: /builddir/build/BUILD/xarray-2023.1.0/pyproject-wheeldir
Processing ./pyproject-wheeldir/xarray-2023.1.0-py3-none-any.whl
Installing collected packages: xarray
Successfully installed xarray-2023.1.0
+ '[' -d /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/bin ']'
+ rm -f /builddir/build/BUILD/python-xarray-2023.1.0-1.fc38.noarch-pyproject-ghost-distinfo
+ site_dirs=()
+ '[' -d /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages ']'
+ site_dirs+=("/usr/lib/python3.11/site-packages")
+ '[' /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib64/python3.11/site-packages '!=' /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages ']'
+ '[' -d /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib64/python3.11/site-packages ']'
+ for site_dir in ${site_dirs[@]}
+ for distinfo in /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch$site_dir/*.dist-info
+ echo '%ghost /usr/lib/python3.11/site-packages/xarray-2023.1.0.dist-info'
+ sed -i s/pip/rpm/ /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray-2023.1.0.dist-info/INSTALLER
+ PYTHONPATH=/usr/lib/rpm/redhat
+ /usr/bin/python3 -B /usr/lib/rpm/redhat/pyproject_preprocess_record.py --buildroot /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch --record /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray-2023.1.0.dist-info/RECORD --output /builddir/build/BUILD/python-xarray-2023.1.0-1.fc38.noarch-pyproject-record
+ rm -fv /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray-2023.1.0.dist-info/RECORD
removed '/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray-2023.1.0.dist-info/RECORD'
+ rm -fv /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray-2023.1.0.dist-info/REQUESTED
removed '/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray-2023.1.0.dist-info/REQUESTED'
++ wc -l /builddir/build/BUILD/python-xarray-2023.1.0-1.fc38.noarch-pyproject-ghost-distinfo
++ cut -f1 '-d '
+ lines=1
+ '[' 1 -ne 1 ']'
+ /usr/bin/python3 /usr/lib/rpm/redhat/pyproject_save_files.py --output-files /builddir/build/BUILD/python-xarray-2023.1.0-1.fc38.noarch-pyproject-files --output-modules /builddir/build/BUILD/python-xarray-2023.1.0-1.fc38.noarch-pyproject-modules --buildroot /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch --sitelib /usr/lib/python3.11/site-packages --sitearch /usr/lib64/python3.11/site-packages --python-version 3.11 --pyproject-record /builddir/build/BUILD/python-xarray-2023.1.0-1.fc38.noarch-pyproject-record --prefix /usr xarray
+ /usr/bin/find-debuginfo -j8 --strict-build-id -m -i --build-id-seed 2023.1.0-1.fc38 --unique-debug-suffix -2023.1.0-1.fc38.noarch --unique-debug-src-base python-xarray-2023.1.0-1.fc38.noarch --run-dwz --dwz-low-mem-die-limit 10000000 --dwz-max-die-limit 50000000 -S debugsourcefiles.list /builddir/build/BUILD/xarray-2023.1.0
find: 'debug': No such file or directory
+ /usr/lib/rpm/check-buildroot
+ /usr/lib/rpm/redhat/brp-ldconfig
+ /usr/lib/rpm/brp-compress
+ /usr/lib/rpm/redhat/brp-strip-lto /usr/bin/strip
+ /usr/lib/rpm/brp-strip-static-archive /usr/bin/strip
+ /usr/lib/rpm/check-rpaths
+ /usr/lib/rpm/redhat/brp-mangle-shebangs
+ /usr/lib/rpm/brp-remove-la-files
+ env /usr/lib/rpm/redhat/brp-python-bytecompile '' 1 0 -j8
Bytecompiling .py files below /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11 using python3.11
+ /usr/lib/rpm/redhat/brp-python-hardlink
Executing(%check): /bin/sh -e /var/tmp/rpm-tmp.K1YMlG
+ umask 022
+ cd /builddir/build/BUILD
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer -I/usr/lib/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd xarray-2023.1.0
+ rm -rf xarray
+ pytest_args=(-n auto -m "not network" -k 'not test_open_nczarr')
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -fasynchronous-unwind-tables -fstack-clash-protection -fno-omit-frame-pointer '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ PATH=/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/sbin
+ PYTHONPATH=/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib64/python3.11/site-packages:/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages
+ PYTHONDONTWRITEBYTECODE=1
+ PYTEST_ADDOPTS=' --ignore=/builddir/build/BUILD/xarray-2023.1.0/.pyproject-builddir'
+ PYTEST_XDIST_AUTO_NUM_WORKERS=8
+ /usr/bin/pytest -ra -n auto -m 'not network' -k 'not test_open_nczarr' --pyargs xarray
============================= test session starts ==============================
platform linux -- Python 3.11.4, pytest-7.2.2, pluggy-1.0.0
rootdir: /builddir/build/BUILD/xarray-2023.1.0, configfile: setup.cfg
plugins: xdist-3.1.0
gw0 I / gw1 I / gw2 I / gw3 I / gw4 I / gw5 I / gw6 I / gw7 I
gw0 [16499] / gw1 [16499] / gw2 [16499] / gw3 [16499] / gw4 [16499] / gw5 [16499] / gw6 [16499] / gw7 [16499]
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sssss...........s............................x....................x..x.. [ 79%]
....................x........................xs...........s.s....x...... [ 79%]
.....s.s.sss......ss.......s.....................x....x.................. [ 80%]
..x..................................x.....x...............x.ss.ss.....s [ 80%]
s........s.....ss.sssx.ssss.s.sssss.......................sssss......... [ 81%]
ss.sss..................x...................x........ss.ssxs.............. [ 81%]
...x..................................x........x...........s........sss.s. [ 81%]
s.s......s........s.....ssssss...s.s.....x..s.s......s.s....X.xsX..ssssX [ 82%]
ssss.sssssssss.ssss.....s.xx.s....s.ssssssx..sX.XX.sX.x...s...s.x.xX.... [ 82%]
................sss.ss.x.......ss.sssssss.xssssss...............x........ [ 83%]
..xx.......x........x........x.....sssssx........x.......x.....x....x... [ 83%]
.........x.x....x...x.....x.x.......sssss...sssssxssx..sxsx.s.ssssss.sss [ 84%]
sssss.ssss.sss.sss..s..s.....xx..ss.....x..s......x......s...x.x....s.s. [ 84%]
.s....s...s.....s..sssssssss.sss.ssssssxsss.ssssssssss.ssss.sssss.ssss.s [ 84%]
ssss.xsssss.ssssssssss.sssssssssssss.s....s.....xs....ssss.ss......s.... [ 85%]
...s...ss..s.......ssx....s....x...s.x...........x.....xs..X..x......... [ 85%]
..........x...........xs...........x.................................... [ 86%]
.......x.........s.............s......x.........s................s...... [ 86%]
........................xs....X..............x..........x...xs.......... [ 87%]
...x.x...s......x.....x......s........s.....x..s.............s.........s [ 87%]
s.sss...sss...sx..sssssssssssss.ss.........s..s.s.sssssssssss.ssssssssss [ 88%]
ssss.ssss.sssx....s..s.ss......x.........sssss.ss.s....x.........x...sss [ 88%]
ss...................x..s.............s.s..ss.s.ss.s.ss.s.ss..s.s....... [ 88%]
......s.s.sss.............s.............s.s.s.ss.................ss..sss [ 89%]
s.ssssssssss.ssss.sss.ss.ss..ss.s.ssss...ss..ss.ssss..ssss.s.ssss.ss..s.s [ 89%]
s.ssssss.sssssss..ss.sssssssss.ssss.ssss..sss.s.ss...................... [ 90%]
s.sss.sssssssssss.sssssss.sss.ssssss...........s..ssss....ssssss......... [ 90%]
.x..........................xs.........sss.ss..ssxsssssss.sssssx........ [ 91%]
...............s.x......ssss.ssx............s.....Xx.......ssss..ss...... [ 91%]
.........................x......x..................x...............s.ss. [ 91%]
.s................X...X......X...................X..X....X.X..xX....X... [ 92%]
.............X.X.....Xx.............X.X...ss..sss.s.sXss.s.sssss..sssss.. [ 92%]
.....s...x.....................x.........s.ss.ss.s...................... [ 93%]
........X......X....ss.ss.s............................................. [ 93%]
.............x.......x....x........s..x.......X...........s............. [ 94%]
...x...........................................s...................s.sss [ 94%]
sss..s.s.s.ss.sssss.s.s.sssss.ss.s.sss.ssss.ss.s.ssss.ss.s.sss.ss.sssss. [ 95%]
.s..X.sx...............s...........................................x.... [ 95%]
.X........X....s.....x..x....ssss.s.....x...X.....X..x...x.............x [ 95%]
......x.............x...x......X........................sssssssss..sssss [ 96%]
s.s.s.sss.s.ssssss.s.s.sssssssssss.ss.sss.s.ssssssss.ssss.sss.sssssss.s. [ 96%]
s.ss.................................................................... [ 97%]
..............x..............x.......................................... [ 97%]
........................................................................ [ 98%]
........................................................................ [ 98%]
........................................................................ [ 98%]
..................XX............sss..................................... [ 99%]
...........................ssss......................................... [ 99%]
............................                                             [100%]
=================================== FAILURES ===================================
______________ TestNetCDF4Data.test_roundtrip_numpy_datetime_data ______________
[gw1] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff632c1cc0>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int64]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff63279090>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff632c1cc0>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestNetCDF4Data object at 0xffffff6397ed90>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:311: in roundtrip
    with self.open(path, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:592: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int64]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
___________ TestScipyInMemoryData.test_roundtrip_numpy_datetime_data ___________
[gw2] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = <xarray.backends.scipy_.ScipyArrayWrapper object at 0xffffff75bfe650>
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:       days since 2000-01-01...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff75bfe890>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = <xarray.backends.scipy_.ScipyArrayWrapper object at 0xffffff75bfe650>
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestScipyInMemoryData object at 0xffffff7c0d7190>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:311: in roundtrip
    with self.open(path, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/scipy_.py:307: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:       days since 2000-01-01...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
____________ TestScipyFileObject.test_roundtrip_numpy_datetime_data ____________
[gw2] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = <xarray.backends.scipy_.ScipyArrayWrapper object at 0xffffff75e58c10>
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:       days since 2000-01-01...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff75be4490>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = <xarray.backends.scipy_.ScipyArrayWrapper object at 0xffffff75e58c10>
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestScipyFileObject object at 0xffffff7c0d4050>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:2716: in roundtrip
    with self.open(f, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/scipy_.py:307: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:       days since 2000-01-01...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
__________ TestNetCDF4ViaDaskData.test_roundtrip_numpy_datetime_data ___________
[gw1] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff62b7f700>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int64]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff62bae890>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff62b7f700>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestNetCDF4ViaDaskData object at 0xffffff63924fd0>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:1719: in roundtrip
    with TestNetCDF4Data.roundtrip(
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:311: in roundtrip
    with self.open(path, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:592: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int64]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
_____________ TestScipyFilePath.test_roundtrip_numpy_datetime_data _____________
[gw2] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = <xarray.backends.scipy_.ScipyArrayWrapper object at 0xffffff7390ebd0>
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:       days since 2000-01-01...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff73a6e210>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = <xarray.backends.scipy_.ScipyArrayWrapper object at 0xffffff7390ebd0>
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestScipyFilePath object at 0xffffff7c8a4650>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:311: in roundtrip
    with self.open(path, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/scipy_.py:307: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:       days since 2000-01-01...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
__________ TestCFEncodedDataStore.test_roundtrip_numpy_datetime_data ___________
[gw0] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = array([ 0.,  1., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
array([ 0.,  1., nan])
Attributes:
    units:       days since 2000-01-01 00:00:...<xarray.Variable ()>
array(18262)
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
array([ 0.,  1., nan])
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff91cc5910>
variable = <xarray.Variable (t: 3)>
array([ 0.,  1., nan])
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = array([ 0.,  1., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_conventions.TestCFEncodedDataStore object at 0xffffff99c37990>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_conventions.py:427: in roundtrip
    yield open_dataset(store, **open_kwargs)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
array([ 0.,  1., nan])
Attributes:
    units:       days since 2000-01-01 00:00:...<xarray.Variable ()>
array(18262)
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
_________ TestNetCDF3ViaNetCDF4Data.test_roundtrip_numpy_datetime_data _________
[gw2] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff73179f40>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff744c9750>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff73179f40>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestNetCDF3ViaNetCDF4Data object at 0xffffff77817390>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:311: in roundtrip
    with self.open(path, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:592: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
_____________ TestZarrDictStore.test_roundtrip_numpy_datetime_data _____________
[gw1] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = LazilyIndexedArray(array=<xarray.backends.zarr.ZarrArrayWrapper object at 0xffffff6096aac0>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:    proleptic_gregorian
 ...e ()>
[1 values with dtype=int64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 1950-01-01})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 2000-01-01 00:00:00
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff60a25d50>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 2000-01-01 00:00:00
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = LazilyIndexedArray(array=<xarray.backends.zarr.ZarrArrayWrapper object at 0xffffff6096aac0>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestZarrDictStore object at 0xffffff63a8a010>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:1788: in roundtrip
    with self.open(store_target, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:1773: in open
    with xr.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/zarr.py:902: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:    proleptic_gregorian
 ...e ()>
[1 values with dtype=int64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 1950-01-01})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
_____ TestNetCDF4ClassicViaNetCDF4Data.test_roundtrip_numpy_datetime_data ______
[gw2] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff734a1000>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff73ce9f50>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff734a1000>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestNetCDF4ClassicViaNetCDF4Data object at 0xffffff770a8f50>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:311: in roundtrip
    with self.open(path, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:592: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
___________ TestGenericNetCDFData.test_roundtrip_numpy_datetime_data ___________
[gw2] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff73de7080>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff73ffe8d0>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    units:     days since 2000-01-01 00:00:00
    calendar:  proleptic_gregorian
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0xffffff73de7080>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestGenericNetCDFData object at 0xffffff7751a010>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:311: in roundtrip
    with self.open(path, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:337: in open
    with open_dataset(path, engine=self.engine, **kwargs) as ds:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:592: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    _FillValue:  nan
    units:       ...e ()>
[1 values with dtype=int32]
Attributes:
    units:     days since 1950-01-01
    calendar:  proleptic_gregorian})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
____ test_cf_datetime_nan[num_dates0-days since 2000-01-01-expected_list0] _____
[gw5] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([nan]), units = 'days since 2000-01-01', calendar = 'standard'
use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([nan]), units = 'days since 2000-01-01'
calendar = 'standard'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['106751 days 23:47:16.854775807']
Length: 1, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807']
Length: 1, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807']
Length: 1, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807']
Length: 1, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([9223372036854775807]), b = 946684800000000000
arr_mask = array([False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
num_dates = [nan], units = 'days since 2000-01-01', expected_list = ['NaT']
    @arm_xfail
    @requires_cftime
    @pytest.mark.parametrize(
        ["num_dates", "units", "expected_list"],
        [
            ([np.nan], "days since 2000-01-01", ["NaT"]),
            ([np.nan, 0], "days since 2000-01-01", ["NaT", "2000-01-01T00:00:00Z"]),
            (
                [np.nan, 0, 1],
                "days since 2000-01-01",
                ["NaT", "2000-01-01T00:00:00Z", "2000-01-02T00:00:00Z"],
            ),
        ],
    )
    def test_cf_datetime_nan(num_dates, units, expected_list) -> None:
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "All-NaN")
>           actual = coding.times.decode_cf_datetime(num_dates, units)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_coding_times.py:494: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:292: in decode_cf_datetime
    dates[np.nanargmin(num_dates)].year < 1678
<__array_function__ internals>:200: in nanargmin
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
a = array([inf]), axis = None, out = None
    @array_function_dispatch(_nanargmin_dispatcher)
    def nanargmin(a, axis=None, out=None, *, keepdims=np._NoValue):
        """
        Return the indices of the minimum values in the specified axis ignoring
        NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
        cannot be trusted if a slice contains only NaNs and Infs.
    
        Parameters
        ----------
        a : array_like
            Input data.
        axis : int, optional
            Axis along which to operate.  By default flattened input is used.
        out : array, optional
            If provided, the result will be inserted into this array. It should
            be of the appropriate shape and dtype.
    
            .. versionadded:: 1.22.0
        keepdims : bool, optional
            If this is set to True, the axes which are reduced are left
            in the result as dimensions with size one. With this option,
            the result will broadcast correctly against the array.
    
            .. versionadded:: 1.22.0
    
        Returns
        -------
        index_array : ndarray
            An array of indices or a single index value.
    
        See Also
        --------
        argmin, nanargmax
    
        Examples
        --------
        >>> a = np.array([[np.nan, 4], [2, 3]])
        >>> np.argmin(a)
        0
        >>> np.nanargmin(a)
        2
        >>> np.nanargmin(a, axis=0)
        array([1, 1])
        >>> np.nanargmin(a, axis=1)
        array([1, 0])
    
        """
        a, mask = _replace_nan(a, np.inf)
        if mask is not None:
            mask = np.all(mask, axis=axis)
            if np.any(mask):
>               raise ValueError("All-NaN slice encountered")
E               ValueError: All-NaN slice encountered
/usr/lib64/python3.11/site-packages/numpy/lib/nanfunctions.py:552: ValueError
__________ TestZarrDirectoryStore.test_roundtrip_numpy_datetime_data ___________
[gw1] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['0 days 00:00:00', '106751 days 23:47:16.854775807'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['0 days 00:00:00', '106751 days 23:47:16.854775807']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([                  0, 9223372036854775807]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
data = LazilyIndexedArray(array=<xarray.backends.zarr.ZarrArrayWrapper object at 0xffffff631893c0>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
>           result = decode_cf_datetime(example_value, units, calendar, use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:184: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
num_dates = array([ 0., nan]), units = 'days since 2000-01-01 00:00:00'
calendar = 'proleptic_gregorian', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
                dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
            except (KeyError, OutOfBoundsDatetime, OutOfBoundsTimedelta, OverflowError):
                dates = _decode_datetime_with_cftime(
                    flat_num_dates.astype(float), units, calendar
                )
    
                if (
                    dates[np.nanargmin(num_dates)].year < 1678
                    or dates[np.nanargmax(num_dates)].year >= 2262
                ):
                    if _is_standard_calendar(calendar):
                        warnings.warn(
                            "Unable to decode time axis into full "
                            "numpy.datetime64 objects, continuing using "
                            "cftime.datetime objects instead, reason: dates out "
                            "of range",
                            SerializationWarning,
                            stacklevel=3,
                        )
                else:
                    if _is_standard_calendar(calendar):
>                       dates = cftime_to_nptime(dates)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeProlepticGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=True)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
During handling of the above exception, another exception occurred:
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:    proleptic_gregorian
 ...e ()>
[1 values with dtype=int64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 1950-01-01})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
>               new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:551: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
name = 't'
var = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 2000-01-01 00:00:00
concat_characters = True, mask_and_scale = True, decode_times = True
decode_endianness = True, stack_char_dim = False, use_cftime = None
decode_timedelta = True
    def decode_cf_variable(
        name: Hashable,
        var: Variable,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_endianness: bool = True,
        stack_char_dim: bool = True,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> Variable:
        """
        Decodes a variable which may hold CF encoded information.
    
        This includes variables that have been masked and scaled, which
        hold CF style time variables (this is almost always the case if
        the dataset has been serialized) and which have strings encoded
        as character arrays.
    
        Parameters
        ----------
        name : str
            Name of the variable. Used for better error messages.
        var : Variable
            A variable holding potentially CF encoded information.
        concat_characters : bool
            Should character arrays be concatenated to strings, for
            example: ["h", "e", "l", "l", "o"] -> "hello"
        mask_and_scale : bool
            Lazily scale (using scale_factor and add_offset) and mask
            (using _FillValue). If the _Unsigned attribute is present
            treat integer arrays as unsigned.
        decode_times : bool
            Decode cf times ("hours since 2000-01-01") to np.datetime64.
        decode_endianness : bool
            Decode arrays from non-native to native endianness.
        stack_char_dim : bool
            Whether to stack characters into bytes along the last dimension of this
            array. Passed as an argument because we need to look at the full
            dataset to figure out if this is appropriate.
        use_cftime : bool, optional
            Only relevant if encoded dates come from a standard calendar
            (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
    
        Returns
        -------
        out : Variable
            A variable holding the decoded equivalent of var.
        """
        # Ensure datetime-like Variables are passed through unmodified (GH 6453)
        if _contains_datetime_like_objects(var):
            return var
    
        original_dtype = var.dtype
    
        if decode_timedelta is None:
            decode_timedelta = decode_times
    
        if concat_characters:
            if stack_char_dim:
                var = strings.CharacterArrayCoder().decode(var, name=name)
            var = strings.EncodedStringCoder().decode(var)
    
        if mask_and_scale:
            for coder in [
                variables.UnsignedIntegerCoder(),
                variables.CFMaskCoder(),
                variables.CFScaleOffsetCoder(),
            ]:
                var = coder.decode(var, name=name)
    
        if decode_timedelta:
            var = times.CFTimedeltaCoder().decode(var, name=name)
        if decode_times:
>           var = times.CFDatetimeCoder(use_cftime=use_cftime).decode(var, name=name)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:397: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <xarray.coding.times.CFDatetimeCoder object at 0xffffff63189e50>
variable = <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 2000-01-01 00:00:00
name = 't'
    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
        units = variable.attrs.get("units", None)
        if isinstance(units, str) and "since" in units:
            dims, data, attrs, encoding = unpack_for_decoding(variable)
    
            units = pop_to(attrs, encoding, "units")
            calendar = pop_to(attrs, encoding, "calendar")
>           dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:716: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data = LazilyIndexedArray(array=<xarray.backends.zarr.ZarrArrayWrapper object at 0xffffff631893c0>, key=BasicIndexer((slice(None, None, None),)))
units = 'days since 2000-01-01 00:00:00', calendar = 'proleptic_gregorian'
use_cftime = None
    def _decode_cf_datetime_dtype(
        data, units: str, calendar: str, use_cftime: bool | None
    ) -> np.dtype:
        # Verify that at least the first and last date can be decoded
        # successfully. Otherwise, tracebacks end up swallowed by
        # Dataset.__repr__ when users try to view their lazily decoded array.
        values = indexing.ImplicitToExplicitIndexingAdapter(indexing.as_indexable(data))
        example_value = np.concatenate(
            [first_n_items(values, 1) or [0], last_item(values) or [0]]
        )
    
        try:
            result = decode_cf_datetime(example_value, units, calendar, use_cftime)
        except Exception:
            calendar_msg = (
                "the default calendar" if calendar is None else f"calendar {calendar!r}"
            )
            msg = (
                f"unable to decode time units {units!r} with {calendar_msg!r}. Try "
                "opening your dataset with decode_times=False or installing cftime "
                "if it is not installed."
            )
>           raise ValueError(msg)
E           ValueError: unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:194: ValueError
During handling of the above exception, another exception occurred:
self = <xarray.tests.test_backends.TestZarrDirectoryStore object at 0xffffff63a15910>
    @arm_xfail
    def test_roundtrip_numpy_datetime_data(self) -> None:
        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
        expected = Dataset({"t": ("t", times), "t0": times[0]})
        kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
>       with self.roundtrip(expected, save_kwargs=kwargs) as actual:
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:1788: in roundtrip
    with self.open(store_target, **open_kwargs) as ds:
/usr/lib64/python3.11/contextlib.py:137: in __enter__
    return next(self.gen)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:1773: in open
    with xr.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/api.py:541: in open_dataset
    backend_ds = backend.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/zarr.py:902: in open_dataset
    ds = store_entrypoint.open_dataset(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/backends/store.py:35: in open_dataset
    vars, attrs, coord_names = conventions.decode_cf_variables(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
variables = Frozen({'t': <xarray.Variable (t: 3)>
[3 values with dtype=float64]
Attributes:
    calendar:    proleptic_gregorian
 ...e ()>
[1 values with dtype=int64]
Attributes:
    calendar:  proleptic_gregorian
    units:     days since 1950-01-01})
attributes = Frozen({}), concat_characters = True, mask_and_scale = True
decode_times = True, decode_coords = True, drop_variables = set()
use_cftime = None, decode_timedelta = None
    def decode_cf_variables(
        variables: T_Variables,
        attributes: T_Attrs,
        concat_characters: bool = True,
        mask_and_scale: bool = True,
        decode_times: bool = True,
        decode_coords: bool = True,
        drop_variables: T_DropVariables = None,
        use_cftime: bool | None = None,
        decode_timedelta: bool | None = None,
    ) -> tuple[T_Variables, T_Attrs, set[Hashable]]:
        """
        Decode several CF encoded variables.
    
        See: decode_cf_variable
        """
        dimensions_used_by = defaultdict(list)
        for v in variables.values():
            for d in v.dims:
                dimensions_used_by[d].append(v)
    
        def stackable(dim: Hashable) -> bool:
            # figure out if a dimension can be concatenated over
            if dim in variables:
                return False
            for v in dimensions_used_by[dim]:
                if v.dtype.kind != "S" or dim != v.dims[-1]:
                    return False
            return True
    
        coord_names = set()
    
        if isinstance(drop_variables, str):
            drop_variables = [drop_variables]
        elif drop_variables is None:
            drop_variables = []
        drop_variables = set(drop_variables)
    
        # Time bounds coordinates might miss the decoding attributes
        if decode_times:
            _update_bounds_attributes(variables)
    
        new_vars = {}
        for k, v in variables.items():
            if k in drop_variables:
                continue
            stack_char_dim = (
                concat_characters
                and v.dtype == "S1"
                and v.ndim > 0
                and stackable(v.dims[-1])
            )
            try:
                new_vars[k] = decode_cf_variable(
                    k,
                    v,
                    concat_characters=concat_characters,
                    mask_and_scale=mask_and_scale,
                    decode_times=decode_times,
                    stack_char_dim=stack_char_dim,
                    use_cftime=use_cftime,
                    decode_timedelta=decode_timedelta,
                )
            except Exception as e:
>               raise type(e)(f"Failed to decode variable {k!r}: {e}")
E               ValueError: Failed to decode variable 't': unable to decode time units 'days since 2000-01-01 00:00:00' with "calendar 'proleptic_gregorian'". Try opening your dataset with decode_times=False or installing cftime if it is not installed.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/conventions.py:562: ValueError
____ test_cf_datetime_nan[num_dates1-days since 2000-01-01-expected_list1] _____
[gw5] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([nan,  0.]), units = 'days since 2000-01-01'
calendar = 'standard', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([nan,  0.]), units = 'days since 2000-01-01'
calendar = 'standard'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00']
Length: 2, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00']
Length: 2, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([9223372036854775807,                   0]), b = 946684800000000000
arr_mask = array([False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=False),
       cftime.DatetimeGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=False)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
num_dates = [nan, 0], units = 'days since 2000-01-01'
expected_list = ['NaT', '2000-01-01T00:00:00Z']
    @arm_xfail
    @requires_cftime
    @pytest.mark.parametrize(
        ["num_dates", "units", "expected_list"],
        [
            ([np.nan], "days since 2000-01-01", ["NaT"]),
            ([np.nan, 0], "days since 2000-01-01", ["NaT", "2000-01-01T00:00:00Z"]),
            (
                [np.nan, 0, 1],
                "days since 2000-01-01",
                ["NaT", "2000-01-01T00:00:00Z", "2000-01-02T00:00:00Z"],
            ),
        ],
    )
    def test_cf_datetime_nan(num_dates, units, expected_list) -> None:
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "All-NaN")
>           actual = coding.times.decode_cf_datetime(num_dates, units)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_coding_times.py:494: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: in decode_cf_datetime
    dates = cftime_to_nptime(dates)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=False),
       cftime.DatetimeGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=False)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
____ test_cf_datetime_nan[num_dates2-days since 2000-01-01-expected_list2] _____
[gw5] linux -- Python 3.11.4 /usr/bin/python3
num_dates = array([nan,  0.,  1.]), units = 'days since 2000-01-01'
calendar = 'standard', use_cftime = None
    def decode_cf_datetime(
        num_dates, units: str, calendar: str | None = None, use_cftime: bool | None = None
    ) -> np.ndarray:
        """Given an array of numeric dates in netCDF format, convert it into a
        numpy array of date time objects.
    
        For standard (Gregorian) calendars, this function uses vectorized
        operations, which makes it much faster than cftime.num2date. In such a
        case, the returned array will be of type np.datetime64.
    
        Note that time unit in `units` must not be smaller than microseconds and
        not larger than days.
    
        See Also
        --------
        cftime.num2date
        """
        num_dates = np.asarray(num_dates)
        flat_num_dates = num_dates.ravel()
        if calendar is None:
            calendar = "standard"
    
        if use_cftime is None:
            try:
>               dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:285: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
flat_num_dates = array([nan,  0.,  1.]), units = 'days since 2000-01-01'
calendar = 'standard'
    def _decode_datetime_with_pandas(
        flat_num_dates: np.ndarray, units: str, calendar: str
    ) -> np.ndarray:
        if not _is_standard_calendar(calendar):
            raise OutOfBoundsDatetime(
                "Cannot decode times from a non-standard calendar, {!r}, using "
                "pandas.".format(calendar)
            )
    
        delta, ref_date = _unpack_netcdf_time_units(units)
        delta = _netcdf_to_numpy_timeunit(delta)
        try:
            ref_date = pd.Timestamp(ref_date)
        except ValueError:
            # ValueError is raised by pd.Timestamp for non-ISO timestamp
            # strings, in which case we fall back to using cftime
            raise OutOfBoundsDatetime
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "invalid value encountered", RuntimeWarning)
            if flat_num_dates.size > 0:
                # avoid size 0 datetimes GH1329
                pd.to_timedelta(flat_num_dates.min(), delta) + ref_date
                pd.to_timedelta(flat_num_dates.max(), delta) + ref_date
    
        # To avoid integer overflow when converting to nanosecond units for integer
        # dtypes smaller than np.int64 cast all integer and unsigned integer dtype
        # arrays to np.int64 (GH 2002, GH 6589).  Note this is safe even in the case
        # of np.uint64 values, because any np.uint64 value that would lead to
        # overflow when converting to np.int64 would not be representable with a
        # timedelta64 value, and therefore would raise an error in the lines above.
        if flat_num_dates.dtype.kind in "iu":
            flat_num_dates = flat_num_dates.astype(np.int64)
    
        # Cast input ordinals to integers of nanoseconds because pd.to_timedelta
        # works much faster when dealing with integers (GH 1399).
        flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
            np.int64
        )
    
        # Use pd.to_timedelta to safely cast integer values to timedeltas,
        # and add those to a Timestamp to safely produce a DatetimeIndex.  This
        # ensures that we do not encounter integer overflow at any point in the
        # process without raising OutOfBoundsDatetime.
>       return (pd.to_timedelta(flat_num_dates_ns_int, "ns") + ref_date).values
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00',
                '1 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00',
                '1 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
>       return self._arith_method(other, operator.add)
/usr/lib64/python3.11/site-packages/pandas/core/arraylike.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00',
                '1 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        if (
            isinstance(other, Index)
            and is_object_dtype(other.dtype)
            and type(other) is not Index
        ):
            # We return NotImplemented for object-dtype index *subclasses* so they have
            # a chance to implement ops before we unwrap them.
            # See https://github.com/pandas-dev/pandas/issues/31109
            return NotImplemented
    
>       return super()._arith_method(other, op)
/usr/lib64/python3.11/site-packages/pandas/core/indexes/base.py:7007: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = TimedeltaIndex(['106751 days 23:47:16.854775807', '0 days 00:00:00',
                '1 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)
other = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def _arith_method(self, other, op):
        res_name = ops.get_op_result_name(self, other)
    
        lvalues = self._values
        rvalues = extract_array(other, extract_numpy=True, extract_range=True)
        rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape)
        rvalues = ensure_wrapped_if_datetimelike(rvalues)
    
        with np.errstate(all="ignore"):
>           result = ops.arithmetic_op(lvalues, rvalues, op)
/usr/lib64/python3.11/site-packages/pandas/core/base.py:1325: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
left = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00', '1 days 00:00:00']
Length: 3, dtype: timedelta64[ns]
right = Timestamp('2000-01-01 00:00:00'), op = <built-in function add>
    def arithmetic_op(left: ArrayLike, right: Any, op):
        """
        Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ...
    
        Note: the caller is responsible for ensuring that numpy warnings are
        suppressed (with np.errstate(all="ignore")) if needed.
    
        Parameters
        ----------
        left : np.ndarray or ExtensionArray
        right : object
            Cannot be a DataFrame or Index.  Series is *not* excluded.
        op : {operator.add, operator.sub, ...}
            Or one of the reversed variants from roperator.
    
        Returns
        -------
        ndarray or ExtensionArray
            Or a 2-tuple of these in the case of divmod or rdivmod.
        """
        # NB: We assume that extract_array and ensure_wrapped_if_datetimelike
        #  have already been called on `left` and `right`,
        #  and `maybe_prepare_scalar_for_op` has already been called on `right`
        # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
        # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
    
        if (
            should_extension_dispatch(left, right)
            or isinstance(right, (Timedelta, BaseOffset, Timestamp))
            or right is NaT
        ):
            # Timedelta/Timestamp and other custom scalars are included in the check
            # because numexpr will fail on it, see GH#31457
>           res_values = op(left, right)
/usr/lib64/python3.11/site-packages/pandas/core/ops/array_ops.py:218: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00', '1 days 00:00:00']
Length: 3, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @wraps(method)
    def new_method(self, other):
    
        if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries):
            # For comparison ops, Index does *not* defer to Series
            pass
        else:
            for cls in [ABCDataFrame, ABCSeries, ABCIndex]:
                if isinstance(self, cls):
                    break
                if isinstance(other, cls):
                    return NotImplemented
    
        other = item_from_zerodim(other)
    
>       return method(self, other)
/usr/lib64/python3.11/site-packages/pandas/core/ops/common.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00', '1 days 00:00:00']
Length: 3, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @unpack_zerodim_and_defer("__add__")
    def __add__(self, other):
        other_dtype = getattr(other, "dtype", None)
    
        # scalar others
        if other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_timedeltalike_scalar(other)
        elif isinstance(other, BaseOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
>           result = self._add_datetimelike_scalar(other)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1441: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <TimedeltaArray>
['106751 days 23:47:16.854775807', '0 days 00:00:00', '1 days 00:00:00']
Length: 3, dtype: timedelta64[ns]
other = Timestamp('2000-01-01 00:00:00')
    @final
    def _add_datetimelike_scalar(self, other) -> DatetimeArray:
        if not is_timedelta64_dtype(self.dtype):
            raise TypeError(
                f"cannot add {type(self).__name__} and {type(other).__name__}"
            )
    
        self = cast("TimedeltaArray", self)
    
        from pandas.core.arrays import DatetimeArray
        from pandas.core.arrays.datetimes import tz_to_dtype
    
        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
            # Preserve our resolution
            return DatetimeArray._simple_new(result, dtype=result.dtype)
    
        if self._reso != other._reso:
            raise NotImplementedError(
                "Addition between TimedeltaArray and Timestamp with mis-matched "
                "resolutions is not yet supported."
            )
    
        i8 = self.asi8
>       result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
/usr/lib64/python3.11/site-packages/pandas/core/arrays/datetimelike.py:1142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
arr = array([9223372036854775807,                   0,      86400000000000])
b = 946684800000000000, arr_mask = array([False, False, False]), b_mask = None
    def checked_add_with_arr(
        arr: npt.NDArray[np.int64],
        b: int | npt.NDArray[np.int64],
        arr_mask: npt.NDArray[np.bool_] | None = None,
        b_mask: npt.NDArray[np.bool_] | None = None,
    ) -> npt.NDArray[np.int64]:
        """
        Perform array addition that checks for underflow and overflow.
    
        Performs the addition of an int64 array and an int64 integer (or array)
        but checks that they do not result in overflow first. For elements that
        are indicated to be NaN, whether or not there is overflow for that element
        is automatically ignored.
    
        Parameters
        ----------
        arr : np.ndarray[int64] addend.
        b : array or scalar addend.
        arr_mask : np.ndarray[bool] or None, default None
            array indicating which elements to exclude from checking
        b_mask : np.ndarray[bool] or None, default None
            array or scalar indicating which element(s) to exclude from checking
    
        Returns
        -------
        sum : An array for elements x + b for each element x in arr if b is
              a scalar or an array for elements x + y for each element pair
              (x, y) in (arr, b).
    
        Raises
        ------
        OverflowError if any x + y exceeds the maximum or minimum int64 value.
        """
        # For performance reasons, we broadcast 'b' to the new array 'b2'
        # so that it has the same size as 'arr'.
        b2 = np.broadcast_to(b, arr.shape)
        if b_mask is not None:
            # We do the same broadcasting for b_mask as well.
            b2_mask = np.broadcast_to(b_mask, arr.shape)
        else:
            b2_mask = None
    
        # For elements that are NaN, regardless of their value, we should
        # ignore whether they overflow or not when doing the checked add.
        if arr_mask is not None and b2_mask is not None:
            not_nan = np.logical_not(arr_mask | b2_mask)
        elif arr_mask is not None:
            not_nan = np.logical_not(arr_mask)
        elif b_mask is not None:
            # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
            # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
            # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
            # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
            # , bytes, _NestedSequence[Union[bool, int, float, complex, str
            # , bytes]]]"
            not_nan = np.logical_not(b2_mask)  # type: ignore[arg-type]
        else:
            not_nan = np.empty(arr.shape, dtype=bool)
            not_nan.fill(True)
    
        # gh-14324: For each element in 'arr' and its corresponding element
        # in 'b2', we check the sign of the element in 'b2'. If it is positive,
        # we then check whether its sum with the element in 'arr' exceeds
        # np.iinfo(np.int64).max. If so, we have an overflow error. If it
        # it is negative, we then check whether its sum with the element in
        # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
        # error as well.
        i8max = lib.i8max
        i8min = iNaT
    
        mask1 = b2 > 0
        mask2 = b2 < 0
    
        if not mask1.any():
            to_raise = ((i8min - b2 > arr) & not_nan).any()
        elif not mask2.any():
            to_raise = ((i8max - b2 < arr) & not_nan).any()
        else:
            to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
                (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
            ).any()
    
        if to_raise:
>           raise OverflowError("Overflow in int64 addition")
E           OverflowError: Overflow in int64 addition
/usr/lib64/python3.11/site-packages/pandas/core/algorithms.py:1249: OverflowError
During handling of the above exception, another exception occurred:
times = array([cftime.DatetimeGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=False),
       cftime.DatetimeGregorian(...as_year_zero=False),
       cftime.DatetimeGregorian(2000, 1, 2, 0, 0, 0, 0, has_year_zero=False)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
>               dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>   ???
E   ValueError: year 294277 is out of range
pandas/_libs/tslibs/timestamps.pyx:1673: ValueError
During handling of the above exception, another exception occurred:
num_dates = [nan, 0, 1], units = 'days since 2000-01-01'
expected_list = ['NaT', '2000-01-01T00:00:00Z', '2000-01-02T00:00:00Z']
    @arm_xfail
    @requires_cftime
    @pytest.mark.parametrize(
        ["num_dates", "units", "expected_list"],
        [
            ([np.nan], "days since 2000-01-01", ["NaT"]),
            ([np.nan, 0], "days since 2000-01-01", ["NaT", "2000-01-01T00:00:00Z"]),
            (
                [np.nan, 0, 1],
                "days since 2000-01-01",
                ["NaT", "2000-01-01T00:00:00Z", "2000-01-02T00:00:00Z"],
            ),
        ],
    )
    def test_cf_datetime_nan(num_dates, units, expected_list) -> None:
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "All-NaN")
>           actual = coding.times.decode_cf_datetime(num_dates, units)
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_coding_times.py:494: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:306: in decode_cf_datetime
    dates = cftime_to_nptime(dates)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
times = array([cftime.DatetimeGregorian(294277, 1, 9, 4, 0, 54, 775807, has_year_zero=False),
       cftime.DatetimeGregorian(...as_year_zero=False),
       cftime.DatetimeGregorian(2000, 1, 2, 0, 0, 0, 0, has_year_zero=False)],
      dtype=object)
raise_on_invalid = True
    def cftime_to_nptime(times, raise_on_invalid: bool = True) -> np.ndarray:
        """Given an array of cftime.datetime objects, return an array of
        numpy.datetime64 objects of the same size
    
        If raise_on_invalid is True (default), invalid dates trigger a ValueError.
        Otherwise, the invalid element is replaced by np.NaT."""
        times = np.asarray(times)
        new = np.empty(times.shape, dtype="M8[ns]")
        for i, t in np.ndenumerate(times):
            try:
                # Use pandas.Timestamp in place of datetime.datetime, because
                # NumPy casts it safely it np.datetime64[ns] for dates outside
                # 1678 to 2262 (this is not currently the case for
                # datetime.datetime).
                dt = pd.Timestamp(
                    t.year, t.month, t.day, t.hour, t.minute, t.second, t.microsecond
                )
            except ValueError as e:
                if raise_on_invalid:
>                   raise ValueError(
                        "Cannot convert date {} to a date in the "
                        "standard calendar.  Reason: {}.".format(t, e)
                    )
E                   ValueError: Cannot convert date 294277-01-09 04:00:54 to a date in the standard calendar.  Reason: year 294277 is out of range.
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:453: ValueError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff933509d0>
center = True, min_periods = None, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93352450>
center = False, min_periods = None, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93352fd0>
center = True, min_periods = 1, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93352dd0>
center = False, min_periods = 1, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93353b90>
center = True, min_periods = 1, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93353090>
center = False, min_periods = 1, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93353950>
center = True, min_periods = 1, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-1-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93351710>
center = False, min_periods = 1, window = 1, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93352010>
center = True, min_periods = None, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93353810>
center = False, min_periods = None, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93350390>
center = True, min_periods = 1, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935eedd0>
center = False, min_periods = 1, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935ed210>
center = True, min_periods = 2, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d1750>
center = False, min_periods = 2, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d2290>
center = True, min_periods = 2, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-2-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d2910>
center = False, min_periods = 2, window = 2, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d2c90>
center = True, min_periods = None, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d2e90>
center = False, min_periods = None, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d3110>
center = True, min_periods = 1, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d3d90>
center = False, min_periods = 1, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935d2c50>
center = True, min_periods = 2, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c0410>
center = False, min_periods = 2, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c2750>
center = True, min_periods = 3, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-3-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c3310>
center = False, min_periods = 3, window = 3, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c2a10>
center = True, min_periods = None, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c2fd0>
center = False, min_periods = None, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c32d0>
center = True, min_periods = 1, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c3c10>
center = False, min_periods = 1, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c3a50>
center = True, min_periods = 2, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c1510>
center = False, min_periods = 2, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c1ed0>
center = True, min_periods = 3, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[sum-4-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c0fd0>
center = False, min_periods = 3, window = 4, name = 'sum'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:24: in move_sum
    return move_func(np.nansum, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c0a90>
center = True, min_periods = None, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c1010>
center = False, min_periods = None, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c0150>
center = True, min_periods = 1, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c16d0>
center = False, min_periods = 1, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c0e90>
center = True, min_periods = 1, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c1e90>
center = False, min_periods = 1, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c2690>
center = True, min_periods = 1, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-1-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c3590>
center = False, min_periods = 1, window = 1, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff935c2d90>
center = True, min_periods = None, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345af50>
center = False, min_periods = None, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345af10>
center = True, min_periods = 1, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345b310>
center = False, min_periods = 1, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93459a10>
center = True, min_periods = 2, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93459710>
center = False, min_periods = 2, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93459b10>
center = True, min_periods = 2, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-2-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93459ed0>
center = False, min_periods = 2, window = 2, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345a4d0>
center = True, min_periods = None, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345bbd0>
center = False, min_periods = None, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345bb90>
center = True, min_periods = 1, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345ba50>
center = False, min_periods = 1, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93459210>
center = True, min_periods = 2, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93458950>
center = False, min_periods = 2, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93458610>
center = True, min_periods = 3, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-3-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93458b90>
center = False, min_periods = 3, window = 3, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
_____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-None-True] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93458a90>
center = True, min_periods = None, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
____ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-None-False] _____
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93459090>
center = False, min_periods = None, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-1-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff9345bf50>
center = True, min_periods = 1, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-1-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93de4750>
center = False, min_periods = 1, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-2-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93de4bd0>
center = True, min_periods = 2, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-2-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93de4f10>
center = False, min_periods = 2, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-3-True] _______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93de6050>
center = True, min_periods = 3, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
______ TestDataArrayRolling.test_rolling_reduce_nonnumeric[max-4-3-False] ______
[gw0] linux -- Python 3.11.4 /usr/bin/python3
self = <xarray.tests.test_rolling.TestDataArrayRolling object at 0xffffff93de6d50>
center = False, min_periods = 3, window = 4, name = 'max'
    @pytest.mark.parametrize("center", (True, False))
    @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "max"))
    def test_rolling_reduce_nonnumeric(self, center, min_periods, window, name) -> None:
        da = DataArray(
            [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
        ).isnull()
    
        if min_periods is not None and window < min_periods:
            min_periods = window
    
        rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
    
        # add nan prefix to numpy methods to get similar behavior as bottleneck
        actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
>       expected = getattr(rolling_obj, name)()
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_rolling.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:155: in method
    return self._numpy_or_bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:578: in _numpy_or_bottleneck_reduce
    return self._bottleneck_reduce(
/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/rolling.py:539: in _bottleneck_reduce
    values = func(
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:49: in move_max
    return move_func(np.nanmax, a, window, min_count, axis=axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:150: in move_func
    idx = _mask(a, window, mc, axis)
/usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: in _mask
    idx = np.empty(a.shape, dtype=np.bool)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
attr = 'bool'
    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
E           AttributeError: module 'numpy' has no attribute 'bool'.
E           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
E           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
E               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
/usr/lib64/python3.11/site-packages/numpy/__init__.py:305: AttributeError
=============================== warnings summary ===============================
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
../../../../usr/lib/python3.11/site-packages/seaborn/rcmod.py:82
  /usr/lib/python3.11/site-packages/seaborn/rcmod.py:82: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    if LooseVersion(mpl.__version__) >= "3.0":
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
../../../../usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346
  /usr/lib/python3.11/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    other = LooseVersion(other)
../../../../usr/lib/python3.11/site-packages/seaborn/cm.py:1582: 48 warnings
  /usr/lib/python3.11/site-packages/seaborn/cm.py:1582: MatplotlibDeprecationWarning: The register_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps.register(name)`` instead.
    mpl_cm.register_cmap(_name, _cmap)
../../../../usr/lib/python3.11/site-packages/seaborn/cm.py:1583: 48 warnings
  /usr/lib/python3.11/site-packages/seaborn/cm.py:1583: MatplotlibDeprecationWarning: The register_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps.register(name)`` instead.
    mpl_cm.register_cmap(_name + "_r", _cmap_r)
../../../../usr/lib/python3.11/site-packages/pint/registry.py:539: 32 warnings
tests/test_dask.py: 4 warnings
tests/test_dataarray.py: 2 warnings
tests/test_dataset.py: 2 warnings
tests/test_units.py: 2 warnings
tests/test_variable.py: 4 warnings
  /usr/lib/python3.11/site-packages/pint/registry.py:539: DeprecationWarning: read_binary is deprecated. Use files() instead. Refer to https://importlib-resources.readthedocs.io/en/latest/using.html#migrating-from-legacy for migration advice.
    rbytes = importlib_resources.read_binary(__package__, file)
tests/test_backends.py: 30 warnings
tests/test_conventions.py: 3 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/variables.py:172: DeprecationWarning: NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays.  The conversion of 255 to int8 will fail in the future.
  For the old behavior, usually:
      np.array(value).astype(dtype)`
  will give the desired result (the cast overflows).
    encoding["_FillValue"] = dtype.type(fv)
tests/test_backends.py: 20 warnings
tests/test_conventions.py: 2 warnings
tests/test_coding_times.py: 3 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:613: RuntimeWarning: invalid value encountered in cast
    int_num = np.asarray(num, dtype=np.int64)
tests/test_backends.py: 10 warnings
tests/test_conventions.py: 1 warning
tests/test_coding_times.py: 3 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:250: RuntimeWarning: invalid value encountered in cast
    flat_num_dates_ns_int = (flat_num_dates * _NS_PER_TIME_DELTA[delta]).astype(
tests/test_backends.py: 10 warnings
tests/test_conventions.py: 1 warning
tests/test_coding_times.py: 3 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/times.py:208: RuntimeWarning: invalid value encountered in cast
    cftime.num2date(num_dates, units, calendar, only_use_cftime_datetimes=True)
tests/test_backends.py: 10 warnings
tests/test_conventions.py: 1 warning
tests/test_coding_times.py: 10 warnings
  /usr/lib64/python3.11/site-packages/pandas/core/arrays/timedeltas.py:908: RuntimeWarning: invalid value encountered in cast
    base = data.astype(np.int64)
tests/test_backends.py: 10 warnings
tests/test_conventions.py: 1 warning
tests/test_coding_times.py: 10 warnings
  /usr/lib64/python3.11/site-packages/pandas/core/arrays/timedeltas.py:912: RuntimeWarning: invalid value encountered in cast
    data = (base * m + (frac * m).astype(np.int64)).view("timedelta64[ns]")
tests/test_cftime_offsets.py: 12 warnings
tests/test_calendar_ops.py: 14 warnings
tests/test_computation.py: 4 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/cftime_offsets.py:1134: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.
    return pd.date_range(
tests/test_backends.py::test_pickle_open_mfdataset_dataset
  /usr/lib/python3.11/site-packages/_pytest/python.py:195: RuntimeWarning: deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/data/bears.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='84a3c0b2-c91d-4fc3-8c79-60d2614f2840'), but file is not already closed. This may indicate a bug.
    result = testfunction(**testargs)
tests/test_array_api.py::test_astype
tests/test_dataset.py::TestDataset::test_unary_ops
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/duck_array_ops.py:187: RuntimeWarning: invalid value encountered in cast
    return data.astype(dtype, **kwargs)
tests/test_array_api.py::test_astype
  /usr/lib64/python3.11/site-packages/numpy/array_api/_data_type_functions.py:20: RuntimeWarning: invalid value encountered in cast
    return Array._new(x._array.astype(dtype=dtype, copy=copy))
tests/test_conventions.py::TestCFEncodedDataStore::test_roundtrip_mask_and_scale[create_unsigned_masked_scaled_data-create_encoded_unsigned_masked_scaled_data]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/coding/variables.py:330: DeprecationWarning: NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays.  The conversion of -1 to uint8 will fail in the future.
  For the old behavior, usually:
      np.array(value).astype(dtype)`
  will give the desired result (the cast overflows).
    new_fill = unsigned_dtype.type(attrs["_FillValue"])
tests/test_backends.py::TestDask::test_inline_array
  /usr/lib/python3.11/site-packages/_pytest/python.py:195: RuntimeWarning: deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/tmp7t_knu0f/temp-692.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='edecc2f9-9207-4873-aa88-7a31332d4f11'), but file is not already closed. This may indicate a bug.
    result = testfunction(**testargs)
tests/test_backends.py::TestDask::test_inline_array
  /usr/lib/python3.11/site-packages/_pytest/python.py:195: RuntimeWarning: deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/tmp7t_knu0f/temp-692.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='7e88b05f-2bcd-430f-b6db-c38ef256835e'), but file is not already closed. This may indicate a bug.
    result = testfunction(**testargs)
tests/test_backends.py::TestDask::test_inline_array
  /usr/lib/python3.11/site-packages/_pytest/python.py:195: RuntimeWarning: deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/tmp7t_knu0f/temp-692.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='ff108960-d29b-4316-a749-bae0c550701b'), but file is not already closed. This may indicate a bug.
    result = testfunction(**testargs)
tests/test_backends.py::TestDask::test_inline_array
  /usr/lib/python3.11/site-packages/_pytest/python.py:195: RuntimeWarning: deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/tmp7t_knu0f/temp-692.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='5161ab8c-be45-4cc9-8840-cc5e5d0dc29b'), but file is not already closed. This may indicate a bug.
    result = testfunction(**testargs)
tests/test_dask.py: 12 warnings
tests/test_dataset.py: 7 warnings
tests/test_dataarray.py: 5 warnings
tests/test_groupby.py: 2 warnings
tests/test_units.py: 12 warnings
  <__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
tests/test_dataset.py::TestDataset::test_assign_coords_existing_multiindex
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/common.py:615: FutureWarning: Updating MultiIndexed coordinate 'x' would corrupt indices for other variables: ['level_1', 'level_2']. This will raise an error in the future. Use `.drop_vars({'x', 'level_2', 'level_1'})` before assigning new coordinate values.
    data.coords.update(results)
tests/test_formatting.py::test_lazy_array_wont_compute
tests/test_formatting.py::test_lazy_array_wont_compute
tests/test_options.py::TestAttrRetention::test_display_style_html
tests/test_options.py::TestAttrRetention::test_display_style_html
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/core/formatting_html.py:26: DeprecationWarning: read_binary is deprecated. Use files() instead. Refer to https://importlib-resources.readthedocs.io/en/latest/using.html#migrating-from-legacy for migration advice.
    read_binary(package, resource).decode("utf-8")
tests/test_groupby.py::TestDataArrayResample::test_resample_base
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_groupby.py:1818: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.
  The new arguments that you should use are 'offset' or 'origin'.
  
  >>> df.resample(freq="3s", base=2)
  
  becomes:
  
  >>> df.resample(freq="3s", offset="2s")
  
    expected = DataArray(array.to_series().resample("24H", base=base).mean())
tests/test_rolling.py::TestDataArrayRolling::test_rolling_wrapped_dask_nochunk[True]
tests/test_rolling.py::TestDataArrayRolling::test_rolling_wrapped_dask_nochunk[None]
  /usr/lib/python3.11/site-packages/dask/array/chunk.py:276: RuntimeWarning: invalid value encountered in cast
    return x.astype(astype_dtype, **kwargs)
tests/test_rolling.py: 64 warnings
  /usr/lib64/python3.11/site-packages/bottleneck/slow/move.py:168: FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
    idx = np.empty(a.shape, dtype=np.bool)
tests/test_plot.py::TestContour::test_colors
  /usr/lib/python3.11/site-packages/seaborn/palettes.py:443: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
    cmap = mpl.cm.get_cmap(name)
tests/test_units.py::TestDataArray::test_missing_value_filling[int64-method_ffill]
tests/test_units.py::TestDataArray::test_missing_value_filling[int64-method_bfill]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:2770: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py: 15 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:2807: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py::TestDataset::test_dropna[int64]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4329: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py::TestDataset::test_dropna[int64]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4333: RuntimeWarning: invalid value encountered in cast
    np.array([4.3, 9.8, 7.5, np.nan, 8.2, np.nan]).astype(dtype)
tests/test_units.py::TestDataArray::test_dropna[int64]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:2835: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py::TestDataset::test_isin[int64-no_unit]
tests/test_units.py::TestDataset::test_isin[int64-dimensionless]
tests/test_units.py::TestDataset::test_isin[int64-incompatible_unit]
tests/test_units.py::TestDataset::test_isin[int64-compatible_unit]
tests/test_units.py::TestDataset::test_isin[int64-same_unit]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4357: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py::TestDataset::test_isin[int64-no_unit]
tests/test_units.py::TestDataset::test_isin[int64-dimensionless]
tests/test_units.py::TestDataset::test_isin[int64-incompatible_unit]
tests/test_units.py::TestDataset::test_isin[int64-compatible_unit]
tests/test_units.py::TestDataset::test_isin[int64-same_unit]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4361: RuntimeWarning: invalid value encountered in cast
    np.array([4.3, 9.8, 7.5, np.nan, 8.2, np.nan]).astype(dtype)
tests/test_units.py::TestDataset::test_isin[int64-no_unit]
tests/test_units.py::TestDataset::test_isin[int64-dimensionless]
tests/test_units.py::TestDataset::test_isin[int64-incompatible_unit]
tests/test_units.py::TestDataset::test_isin[int64-compatible_unit]
tests/test_units.py::TestDataset::test_isin[int64-same_unit]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4366: RuntimeWarning: invalid value encountered in cast
    raw_values = np.array([1.4, np.nan, 2.3]).astype(dtype)
tests/test_units.py::TestDataArray::test_isin[int64-no_unit]
tests/test_units.py::TestDataArray::test_isin[int64-dimensionless]
tests/test_units.py::TestDataArray::test_isin[int64-incompatible_unit]
tests/test_units.py::TestDataArray::test_isin[int64-compatible_unit]
tests/test_units.py::TestDataArray::test_isin[int64-identical_unit]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:2860: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py::TestDataArray::test_isin[int64-no_unit]
tests/test_units.py::TestDataArray::test_isin[int64-dimensionless]
tests/test_units.py::TestDataArray::test_isin[int64-incompatible_unit]
tests/test_units.py::TestDataArray::test_isin[int64-compatible_unit]
tests/test_units.py::TestDataArray::test_isin[int64-identical_unit]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:2865: RuntimeWarning: invalid value encountered in cast
    raw_values = np.array([1.4, np.nan, 2.3]).astype(dtype)
tests/test_units.py::TestDataset::test_missing_value_filling[int64-method_ffill]
tests/test_units.py::TestDataset::test_missing_value_filling[int64-method_bfill]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4256: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py::TestDataset::test_missing_value_filling[int64-method_ffill]
tests/test_units.py::TestDataset::test_missing_value_filling[int64-method_bfill]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4260: RuntimeWarning: invalid value encountered in cast
    np.array([4.3, 9.8, 7.5, np.nan, 8.2, np.nan]).astype(dtype)
tests/test_units.py::TestDataset::test_interpolate_na[int64]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4442: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py::TestDataset::test_interpolate_na[int64]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4446: RuntimeWarning: invalid value encountered in cast
    np.array([4.3, 9.8, 7.5, np.nan, 8.2, np.nan]).astype(dtype)
tests/test_units.py: 15 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4299: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype)
tests/test_units.py: 15 warnings
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4303: RuntimeWarning: invalid value encountered in cast
    np.array([4.3, 9.8, 7.5, np.nan, 8.2, np.nan]).astype(dtype)
tests/test_units.py::TestDataset::test_combine_first[int64-data-no_unit]
tests/test_units.py::TestDataset::test_combine_first[int64-data-dimensionless]
tests/test_units.py::TestDataset::test_combine_first[int64-data-incompatible_unit]
tests/test_units.py::TestDataset::test_combine_first[int64-data-compatible_unit]
tests/test_units.py::TestDataset::test_combine_first[int64-data-same_unit]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4491: RuntimeWarning: invalid value encountered in cast
    np.array([1.4, np.nan, 2.3, np.nan, np.nan, 9.1]).astype(dtype) * data_unit
tests/test_units.py::TestDataset::test_combine_first[int64-data-no_unit]
tests/test_units.py::TestDataset::test_combine_first[int64-data-dimensionless]
tests/test_units.py::TestDataset::test_combine_first[int64-data-incompatible_unit]
tests/test_units.py::TestDataset::test_combine_first[int64-data-compatible_unit]
tests/test_units.py::TestDataset::test_combine_first[int64-data-same_unit]
  /builddir/build/BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4494: RuntimeWarning: invalid value encountered in cast
    np.array([4.3, 9.8, 7.5, np.nan, 8.2, np.nan]).astype(dtype) * data_unit
tests/test_print_versions.py::test_show_versions
  /usr/lib/python3.11/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
    warnings.warn("Setuptools is replacing distutils.")
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_cupy.py:9: could not import 'cupy': No module named 'cupy'
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_distributed.py:17: could not import 'distributed': No module named 'distributed'
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_sparse.py:21: could not import 'sparse': No module named 'sparse'
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SKIPPED [2] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:1648: https://github.com/Unidata/netcdf4-python/issues/1195
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SKIPPED [16] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:3278: could not import 'h5netcdf': No module named 'h5netcdf'
SKIPPED [16] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:3276: could not import 'Nio': No module named 'Nio'
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:2719: cannot pickle file objects
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_backends.py:2723: cannot pickle file objects
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SKIPPED [6] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4716: indexes don't support units
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SKIPPED [2] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4789: rank not implemented for non-ndarray
SKIPPED [30] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4526: behaviour of identical is unclear
SKIPPED [10] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4461: indexes don't support units
SKIPPED [10] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4619: indexes don't support units
SKIPPED [30] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4857: indexes don't support units
SKIPPED [30] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:4910: indexes don't support units
SKIPPED [12] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:5259: indexes don't support units
SKIPPED [32] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:5469: indexes don't support units
SKIPPED [20] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:5144: indexes don't support units
SKIPPED [10] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:5413: indexes don't support units
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SKIPPED [20] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:5219: indexes don't support units
SKIPPED [2] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_units.py:5371: indexes don't support units
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2425: unconditional skip
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2429: unconditional skip
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SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2437: unconditional skip
SKIPPED [45] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2441: unconditional skip
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2460: unconditional skip
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2464: unconditional skip
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2468: unconditional skip
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2472: unconditional skip
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SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2480: unconditional skip
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2314: requires sparse
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2553: requires pandas 2.0.0
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2884: requires pandas 2.0.0
SKIPPED [2] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2800: requires sparse
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SKIPPED [2] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_variable.py:2814: requires cupy
SKIPPED [72] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_weighted.py:306: `method` argument is not currently exposed
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_plot.py:2844: nc_time_axis is not installed
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_plot.py:2847: nc_time_axis is not installed
SKIPPED [1] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_plot.py:2850: nc_time_axis is not installed
SKIPPED [4] ../../BUILDROOT/python-xarray-2023.1.0-1.fc38.noarch/usr/lib/python3.11/site-packages/xarray/tests/test_plot.py:3072: requires cartopy
XFAIL tests/test_backends.py::TestGenericNetCDFData::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_backends.py::TestRasterio::test_rasterio_vrt - rasterio 1.1.1 is broken. GH3573
XFAIL tests/test_backends.py::TestNetCDF4Data::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_backends.py::TestScipyInMemoryData::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_backends.py::TestScipyFileObject::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_backends.py::TestNetCDF4ViaDaskData::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_backends.py::TestScipyFilePath::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_conventions.py::TestCFEncodedDataStore::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_backends.py::TestNetCDF3ViaNetCDF4Data::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_dataarray.py::TestReduce1D::test_idxmin[True-datetime] - reason: dask operation 'argmin' breaks when dtype is datetime64 (M)
XFAIL tests/test_backends.py::TestZarrDictStore::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_combine.py::TestNestedCombine::test_nested_concat_too_many_dims_at_once
XFAIL tests/test_dataarray.py::TestReduce1D::test_idxmax[True-datetime] - reason: dask operation 'argmax' breaks when dtype is datetime64 (M)
XFAIL tests/test_backends.py::TestNetCDF4ClassicViaNetCDF4Data::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_dask.py::TestToDaskDataFrame::test_to_dask_dataframe_2D_set_index
XFAIL tests/test_dataarray.py::TestReduce2D::test_idxmin[dask-datetime] - reason: dask operation 'argmin' breaks when dtype is datetime64 (M)
XFAIL tests/test_dataarray.py::TestReduce2D::test_idxmax[dask-datetime] - reason: dask operation 'argmax' breaks when dtype is datetime64 (M)
XFAIL tests/test_backends.py::TestZarrDirectoryStore::test_roundtrip_mask_and_scale[create_bad_unsigned_masked_scaled_data-create_bad_encoded_unsigned_masked_scaled_data] - Bad _Unsigned attribute.
XFAIL tests/test_dataset.py::TestDataset::test_copy_coords[True-expected_orig0]
XFAIL tests/test_interp.py::test_datetime[2000-01-01T12:00-0.5]
XFAIL tests/test_dataarray.py::TestDataArray::test_astype_subok - reason: DataArray cannot be backed yet by a subclasses of np.ndarray
XFAIL tests/test_missing.py::test_interpolate_na_2d[None]
XFAIL tests/test_dataarray.py::TestDataArray::test_copy_coords[True-expected_orig0]
XFAIL tests/test_units.py::test_replication_full_like_dataarray[float64-coords] - can't copy quantity into non-quantity
XFAIL tests/test_units.py::test_replication_full_like_dataarray[int64-coords] - can't copy quantity into non-quantity
XFAIL tests/test_units.py::test_replication_full_like_dataset[float64-coords] - can't copy quantity into non-quantity
XFAIL tests/test_units.py::test_align_dataarray[float64-10-coords-no_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[float64-10-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_replication_full_like_dataset[int64-coords] - can't copy quantity into non-quantity
XFAIL tests/test_units.py::test_align_dataarray[float64-10-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[float64-10-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[float64-10-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestVariable::test_computation[float64-method_rolling_window] - converts to ndarray
XFAIL tests/test_units.py::test_align_dataarray[float64-value1-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[float64-value1-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestVariable::test_computation[int64-method_rolling_window] - converts to ndarray
XFAIL tests/test_units.py::test_align_dataarray[float64-value1-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[float64-value1-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestVariable::test_aggregation[float64-method_prod] - reason: nanprod is not by older `pint` versions
XFAIL tests/test_units.py::test_align_dataarray[int64-10-coords-no_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_aggregation[float64-function_prod] - reason: nanprod is not by older `pint` versions
XFAIL tests/test_units.py::test_align_dataarray[int64-10-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[int64-10-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_aggregation[float64-method_prod] - reason: nanprod is not by older `pint` versions
XFAIL tests/test_units.py::test_align_dataarray[int64-10-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[int64-10-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[int64-value1-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[float64-incompatible_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataarray[int64-value1-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[int64-value1-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataarray[int64-value1-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[float64-compatible_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[float64-10-coords-no_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataset[float64-10-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataset[float64-10-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[float64-identical_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_bivariate_ufunc[float64-compatible_unit] - needs the type register system for __array_ufunc__
XFAIL tests/test_units.py::test_align_dataset[float64-10-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_bivariate_ufunc[int64-compatible_unit] - needs the type register system for __array_ufunc__
XFAIL tests/test_units.py::test_align_dataset[float64-10-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[int64-no_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex_like[float64-method_interp_like-data] - uses scipy
XFAIL tests/test_units.py::test_align_dataset[float64-value1-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataset[float64-value1-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[int64-dimensionless-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[float64-value1-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataset[float64-value1-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex_like[float64-method_interp_like-coords] - uses scipy
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[float64-function_searchsorted-no_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[int64-incompatible_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[int64-10-coords-no_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[float64-function_searchsorted-dimensionless] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[int64-10-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::test_align_dataset[int64-10-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex_like[int64-method_interp_like-data] - uses scipy
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[int64-compatible_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[int64-10-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[float64-function_searchsorted-incompatible_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[int64-10-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex_like[int64-method_interp_like-coords] - uses scipy
XFAIL tests/test_units.py::test_align_dataset[int64-value1-coords-dimensionless] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[float64-function_searchsorted-compatible_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[int64-identical_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[int64-value1-coords-incompatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex[float64-method_interp-data] - uses scipy
XFAIL tests/test_units.py::test_align_dataset[int64-value1-coords-compatible_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[float64-function_searchsorted-identical_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::test_align_dataset[int64-value1-coords-identical_unit] - reason: fill_value is used for both data variables and coords. See https://github.com/pydata/xarray/issues/4165
XFAIL tests/test_units.py::TestDataArray::test_missing_value_filling[float64-method_ffill] - ffill and bfill lose units in data
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex[float64-method_interp-coords] - uses scipy
XFAIL tests/test_units.py::TestDataArray::test_missing_value_filling[float64-method_bfill] - ffill and bfill lose units in data
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[int64-function_searchsorted-no_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_missing_value_filling[int64-method_ffill] - ffill and bfill lose units in data
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[int64-function_searchsorted-dimensionless] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex[int64-method_interp-data] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_aggregation[float64-method_prod] - reason: nanprod is not by older `pint` versions
XFAIL tests/test_units.py::TestDataArray::test_computation_objects[float64-method_rolling-data] - numpy.lib.stride_tricks.as_strided converts to ndarray
XFAIL tests/test_units.py::TestDataArray::test_missing_value_filling[int64-method_bfill] - ffill and bfill lose units in data
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[int64-function_searchsorted-incompatible_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_computation_objects[float64-method_rolling_exp-data] - numbagg functions are not supported by pint
XFAIL tests/test_units.py::TestDataArray::test_interp_reindex[int64-method_interp-coords] - uses scipy
XFAIL tests/test_units.py::TestDataArray::test_computation_objects[float64-method_rolling_exp-coords] - numbagg functions are not supported by pint
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[int64-function_searchsorted-compatible_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_computation_objects[int64-method_rolling-data] - numpy.lib.stride_tricks.as_strided converts to ndarray
XFAIL tests/test_units.py::TestDataArray::test_searchsorted[int64-function_searchsorted-identical_unit] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataset::test_missing_value_filling[float64-method_ffill] - ffill and bfill lose the unit
XFAIL tests/test_units.py::TestDataArray::test_computation_objects[int64-method_rolling_exp-data] - numbagg functions are not supported by pint
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[float64-no_unit-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataArray::test_computation_objects[int64-method_rolling_exp-coords] - numbagg functions are not supported by pint
XFAIL tests/test_units.py::TestDataset::test_missing_value_filling[float64-method_bfill] - ffill and bfill lose the unit
XFAIL tests/test_units.py::TestDataArray::test_interpolate_na - uses numpy.vectorize
XFAIL tests/test_units.py::TestDataArray::test_numpy_methods_with_args[float64-dimensionless-function_clip] - xarray does not implement __array_function__
XFAIL tests/test_units.py::TestDataset::test_missing_value_filling[int64-method_ffill] - ffill and bfill lose the unit
XFAIL tests/test_units.py::TestDataset::test_interpolate_na[float64] - interpolate_na uses numpy.vectorize
XFAIL tests/test_units.py::TestDataset::test_to_stacked_array[float64] - stacked dimension's labels have to be hashable, but is a numpy.array
XFAIL tests/test_units.py::TestDataset::test_missing_value_filling[int64-method_bfill] - ffill and bfill lose the unit
XFAIL tests/test_units.py::TestDataset::test_to_stacked_array[int64] - stacked dimension's labels have to be hashable, but is a numpy.array
XFAIL tests/test_units.py::TestDataset::test_interpolate_na[int64] - interpolate_na uses numpy.vectorize
XFAIL tests/test_units.py::TestDataset::test_interp_reindex[float64-method_interp-data] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_interp_reindex_like[float64-method_interp_like-data] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_computation_objects[float64-data-method_rolling] - strips units
XFAIL tests/test_units.py::TestDataset::test_interp_reindex[float64-method_interp-coords] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_computation_objects[float64-data-method_rolling_exp] - numbagg functions are not supported by pint
XFAIL tests/test_units.py::TestDataset::test_interp_reindex_like[float64-method_interp_like-coords] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_interp_reindex[int64-method_interp-data] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_computation_objects[float64-coords-method_rolling_exp] - numbagg functions are not supported by pint
XFAIL tests/test_units.py::TestDataset::test_interp_reindex_like[int64-method_interp_like-data] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_interp_reindex[int64-method_interp-coords] - uses scipy
XFAIL tests/test_units.py::TestDataset::test_computation_objects[int64-data-method_rolling] - strips units
XFAIL tests/test_units.py::TestDataset::test_interp_reindex_like[int64-method_interp_like-coords] - uses scipy
XFAIL tests/test_variable.py::TestVariable::test_timedelta64_valid_range - pandas issue 36615
XFAIL tests/test_units.py::TestDataset::test_computation_objects[int64-data-method_rolling_exp] - numbagg functions are not supported by pint
XFAIL tests/test_units.py::TestDataset::test_computation_objects[int64-coords-method_rolling_exp] - numbagg functions are not supported by pint
XFAIL tests/test_variable.py::TestVariableWithDask::test_timedelta64_valid_range - pandas issue 36615
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg4-np_arg4-median] - median is not implemented by Dask
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg4-np_arg4-reflect] - dask.array.pad bug
XFAIL tests/test_units.py::TestPlots::test_units_in_line_plot_labels[coord_unit1-coord_attrs1] - indexes don't support units
XFAIL tests/test_units.py::TestPlots::test_units_in_slice_line_plot_labels_sel[coord_unit1-coord_attrs1] - indexes don't support units
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg1-np_arg1-median] - median is not implemented by Dask
XFAIL tests/test_units.py::TestPlots::test_units_in_slice_line_plot_labels_isel[coord_unit1-coord_attrs1] - pint.errors.UnitStrippedWarning
XFAIL tests/test_variable.py::TestVariableWithDask::test_0d_object_array_with_list
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg0-np_arg0-median] - median is not implemented by Dask
XFAIL tests/test_variable.py::TestIndexVariable::test_timedelta64_valid_range - pandas issue 36615
XFAIL tests/test_variable.py::TestVariableWithDask::test_array_interface
XFAIL tests/test_variable.py::TestVariableWithDask::test_copy_index
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg2-np_arg2-median] - median is not implemented by Dask
XFAIL tests/test_variable.py::TestVariableWithDask::test_eq_all_dtypes
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg2-np_arg2-reflect] - dask.array.pad bug
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg3-np_arg3-median] - median is not implemented by Dask
XFAIL tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg3-np_arg3-reflect] - dask.array.pad bug
XPASS tests/test_backends.py::TestNetCDF4Data::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestScipyInMemoryData::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_cftimeindex.py::test_multiindex https://github.com/pandas-dev/pandas/issues/24263
XPASS tests/test_backends.py::TestScipyFileObject::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestNetCDF4ViaDaskData::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestScipyFilePath::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_conventions.py::TestCFEncodedDataStore::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestNetCDF3ViaNetCDF4Data::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestZarrDictStore::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestDask::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestNetCDF4ClassicViaNetCDF4Data::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestDask::test_encoding_mfdataset mfdataset loses encoding currently.
XPASS tests/test_backends.py::TestZarrDictStore::test_dataset_caching Zarr caching not implemented
XPASS tests/test_backends.py::TestDask::test_save_mfdataset_compute_false_roundtrip 
XPASS tests/test_backends.py::TestGenericNetCDFData::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_backends.py::TestZarrDirectoryStore::test_vectorized_indexing_negative_step zarr without dask handles negative steps in slices incorrectly
XPASS tests/test_dataset.py::TestDataset::test_copy_coords[False-expected_orig1] 
XPASS tests/test_backends.py::TestZarrDirectoryStore::test_dataset_caching Zarr caching not implemented
XPASS tests/test_options.py::TestAttrRetention::test_merge_attr_retention 
XPASS tests/test_dataarray.py::TestDataArray::test_copy_coords[False-expected_orig1] 
XPASS tests/test_rolling.py::TestDatasetRolling::test_raise_no_warning_dask_rolling_assert_close[numpy-mean-2] See https://github.com/pydata/xarray/pull/4369 or docstring
XPASS tests/test_rolling.py::TestDatasetRolling::test_raise_no_warning_dask_rolling_assert_close[numpy-max-2] See https://github.com/pydata/xarray/pull/4369 or docstring
XPASS tests/test_rolling.py::TestDatasetRolling::test_raise_no_warning_dask_rolling_assert_close[dask-mean-2] See https://github.com/pydata/xarray/pull/4369 or docstring
XPASS tests/test_rolling.py::TestDatasetRolling::test_raise_no_warning_dask_rolling_assert_close[dask-max-2] See https://github.com/pydata/xarray/pull/4369 or docstring
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[float64-without_unit] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[float64-dimensionless] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[float64-incompatible_unit] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[float64-identical_unit] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[int64-without_unit] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[int64-dimensionless] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[int64-incompatible_unit] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_bivariate_ufunc[int64-identical_unit] needs the type register system for __array_ufunc__
XPASS tests/test_units.py::TestDataArray::test_computation_objects[float64-method_rolling-coords] numpy.lib.stride_tricks.as_strided converts to ndarray
XPASS tests/test_units.py::TestDataArray::test_computation_objects[int64-method_rolling-coords] numpy.lib.stride_tricks.as_strided converts to ndarray
XPASS tests/test_units.py::TestDataset::test_computation_objects[float64-coords-method_rolling] strips units
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg3-np_arg3-median] median is not implemented by Dask
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg3-np_arg3-reflect] dask.array.pad bug
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg3-np_arg3-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg4-np_arg4-median] median is not implemented by Dask
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg4-np_arg4-reflect] dask.array.pad bug
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg4-np_arg4-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg0-np_arg0-median] median is not implemented by Dask
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg0-np_arg0-reflect] dask.array.pad bug
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg0-np_arg0-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg1-np_arg1-median] median is not implemented by Dask
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg1-np_arg1-reflect] dask.array.pad bug
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg1-np_arg1-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg2-np_arg2-median] median is not implemented by Dask
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg2-np_arg2-reflect] dask.array.pad bug
XPASS tests/test_variable.py::TestVariable::test_pad[xr_arg2-np_arg2-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg3-np_arg3-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_units.py::TestDataset::test_computation_objects[int64-coords-method_rolling] strips units
XPASS tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg4-np_arg4-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariableWithDask::test_multiindex https://github.com/pydata/xarray/issues/6209#issuecomment-1025116203
XPASS tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg1-np_arg1-reflect] dask.array.pad bug
XPASS tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg1-np_arg1-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg0-np_arg0-reflect] dask.array.pad bug
XPASS tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg0-np_arg0-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_variable.py::TestVariableWithDask::test_pad[xr_arg2-np_arg2-linear_ramp] pint bug: https://github.com/hgrecco/pint/issues/1026
XPASS tests/test_plot.py::TestDatasetScatterPlots::test_datetime_hue[discrete] datetime,timedelta hue variable not supported.
XPASS tests/test_plot.py::TestDatasetScatterPlots::test_datetime_hue[continuous] datetime,timedelta hue variable not supported.
FAILED tests/test_backends.py::TestNetCDF4Data::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestScipyInMemoryData::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestScipyFileObject::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestNetCDF4ViaDaskData::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestScipyFilePath::test_roundtrip_numpy_datetime_data
FAILED tests/test_conventions.py::TestCFEncodedDataStore::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestNetCDF3ViaNetCDF4Data::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestZarrDictStore::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestNetCDF4ClassicViaNetCDF4Data::test_roundtrip_numpy_datetime_data
FAILED tests/test_backends.py::TestGenericNetCDFData::test_roundtrip_numpy_datetime_data
FAILED tests/test_coding_times.py::test_cf_datetime_nan[num_dates0-days since 2000-01-01-expected_list0]
FAILED tests/test_backends.py::TestZarrDirectoryStore::test_roundtrip_numpy_datetime_data
FAILED tests/test_coding_times.py::test_cf_datetime_nan[num_dates1-days since 2000-01-01-expected_list1]
FAILED tests/test_coding_times.py::test_cf_datetime_nan[num_dates2-days since 2000-01-01-expected_list2]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-1-3-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-2-3-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-3-3-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[sum-4-3-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-1-3-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-2-3-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-3-3-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-None-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-None-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-1-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-1-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-2-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-2-False]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-3-True]
FAILED tests/test_rolling.py::TestDataArrayRolling::test_rolling_reduce_nonnumeric[max-4-3-False]
= 78 failed, 14333 passed, 1880 skipped, 150 xfailed, 61 xpassed, 528 warnings in 1798.26s (0:29:58) =
error: Bad exit status from /var/tmp/rpm-tmp.K1YMlG (%check)
RPM build errors:
    Bad exit status from /var/tmp/rpm-tmp.K1YMlG (%check)
Child return code was: 1
EXCEPTION: [Error('Command failed: \n # bash --login -c /usr/bin/rpmbuild -ba --noprep --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec\n', 1)]
Traceback (most recent call last):
  File "/usr/lib/python3.11/site-packages/mockbuild/trace_decorator.py", line 93, in trace
    result = func(*args, **kw)
             ^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/site-packages/mockbuild/util.py", line 598, in do_with_status
    raise exception.Error("Command failed: \n # %s\n%s" % (command, output), child.returncode)
mockbuild.exception.Error: Command failed: 
 # bash --login -c /usr/bin/rpmbuild -ba --noprep --noclean --target noarch --nodeps /builddir/build/SPECS/python-xarray.spec