Build log for python-skbio (0.5.6-6) on armhf

python-skbio → 0.5.6-6 → armhf → 2022-11-21 02:53:26

sbuild (Debian sbuild) 0.71.0 (24 Aug 2016) on testwandboard

+==============================================================================+
| python-skbio 0.5.6-6 (armhf)                 Mon, 21 Nov 2022 02:04:58 +0000 |
+==============================================================================+

Package: python-skbio
Version: 0.5.6-6
Source Version: 0.5.6-6
Distribution: bookworm-staging
Machine Architecture: armhf
Host Architecture: armhf
Build Architecture: armhf

I: NOTICE: Log filtering will replace 'var/lib/schroot/mount/bookworm-staging-armhf-sbuild-6a182a09-8ebf-4caf-968c-de99c146af21' with '<<CHROOT>>'

+------------------------------------------------------------------------------+
| Update chroot                                                                |
+------------------------------------------------------------------------------+

Get:1 http://172.17.4.1/private bookworm-staging InRelease [11.3 kB]
Get:2 http://172.17.4.1/private bookworm-staging/main Sources [13.4 MB]
Get:3 http://172.17.4.1/private bookworm-staging/main armhf Packages [14.3 MB]
Fetched 27.7 MB in 32s (865 kB/s)
Reading package lists...
W: No sandbox user '_apt' on the system, can not drop privileges
W: http://172.17.4.1/private/dists/bookworm-staging/InRelease: Key is stored in legacy trusted.gpg keyring (/etc/apt/trusted.gpg), see the DEPRECATION section in apt-key(8) for details.

+------------------------------------------------------------------------------+
| Fetch source files                                                           |
+------------------------------------------------------------------------------+


Check APT
---------

Checking available source versions...

Download source files with APT
------------------------------

Reading package lists...
NOTICE: 'python-skbio' packaging is maintained in the 'Git' version control system at:
https://salsa.debian.org/med-team/python-skbio.git
Please use:
git clone https://salsa.debian.org/med-team/python-skbio.git
to retrieve the latest (possibly unreleased) updates to the package.
Need to get 8386 kB of source archives.
Get:1 http://172.17.4.1/private bookworm-staging/main python-skbio 0.5.6-6 (dsc) [2791 B]
Get:2 http://172.17.4.1/private bookworm-staging/main python-skbio 0.5.6-6 (tar) [8366 kB]
Get:3 http://172.17.4.1/private bookworm-staging/main python-skbio 0.5.6-6 (diff) [17.3 kB]
Fetched 8386 kB in 2s (4079 kB/s)
Download complete and in download only mode
I: NOTICE: Log filtering will replace 'build/python-skbio-MVd6kl/python-skbio-0.5.6' with '<<PKGBUILDDIR>>'
I: NOTICE: Log filtering will replace 'build/python-skbio-MVd6kl' with '<<BUILDDIR>>'

+------------------------------------------------------------------------------+
| Install build-essential                                                      |
+------------------------------------------------------------------------------+


Setup apt archive
-----------------

Merged Build-Depends: build-essential, fakeroot
Filtered Build-Depends: build-essential, fakeroot
dpkg-deb: building package 'sbuild-build-depends-core-dummy' in '/<<BUILDDIR>>/resolver-OPfPfE/apt_archive/sbuild-build-depends-core-dummy.deb'.
dpkg-scanpackages: warning: Packages in archive but missing from override file:
dpkg-scanpackages: warning:   sbuild-build-depends-core-dummy
dpkg-scanpackages: info: Wrote 1 entries to output Packages file.
gpg: keybox '/<<BUILDDIR>>/resolver-OPfPfE/gpg/pubring.kbx' created
gpg: /<<BUILDDIR>>/resolver-OPfPfE/gpg/trustdb.gpg: trustdb created
gpg: key 35506D9A48F77B2E: public key "Sbuild Signer (Sbuild Build Dependency Archive Key) <buildd-tools-devel@lists.alioth.debian.org>" imported
gpg: Total number processed: 1
gpg:               imported: 1
gpg: key 35506D9A48F77B2E: "Sbuild Signer (Sbuild Build Dependency Archive Key) <buildd-tools-devel@lists.alioth.debian.org>" not changed
gpg: key 35506D9A48F77B2E: secret key imported
gpg: Total number processed: 1
gpg:              unchanged: 1
gpg:       secret keys read: 1
gpg:   secret keys imported: 1
gpg: using "Sbuild Signer" as default secret key for signing
Ign:1 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ InRelease
Get:2 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Release [957 B]
Get:3 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Release.gpg [370 B]
Get:4 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Sources [349 B]
Get:5 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Packages [433 B]
Fetched 2109 B in 1s (2682 B/s)
Reading package lists...
W: No sandbox user '_apt' on the system, can not drop privileges
Reading package lists...

Install core build dependencies (apt-based resolver)
----------------------------------------------------

Installing build dependencies
Reading package lists...
Building dependency tree...
Reading state information...
The following packages were automatically installed and are no longer required:
  krb5-locales libpam-cap libperl5.34 netbase perl-modules-5.34 sensible-utils
Use 'apt autoremove' to remove them.
The following NEW packages will be installed:
  sbuild-build-depends-core-dummy
0 upgraded, 1 newly installed, 0 to remove and 65 not upgraded.
Need to get 852 B of archives.
After this operation, 0 B of additional disk space will be used.
Get:1 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ sbuild-build-depends-core-dummy 0.invalid.0 [852 B]
debconf: delaying package configuration, since apt-utils is not installed
Fetched 852 B in 0s (15.9 kB/s)
Selecting previously unselected package sbuild-build-depends-core-dummy.
(Reading database ... 14774 files and directories currently installed.)
Preparing to unpack .../sbuild-build-depends-core-dummy_0.invalid.0_armhf.deb ...
Unpacking sbuild-build-depends-core-dummy (0.invalid.0) ...
Setting up sbuild-build-depends-core-dummy (0.invalid.0) ...
W: No sandbox user '_apt' on the system, can not drop privileges

+------------------------------------------------------------------------------+
| Check architectures                                                          |
+------------------------------------------------------------------------------+

Arch check ok (armhf included in any all)

+------------------------------------------------------------------------------+
| Install package build dependencies                                           |
+------------------------------------------------------------------------------+


Setup apt archive
-----------------

Merged Build-Depends: debhelper-compat (= 13), cython3, dh-python, python3-all-dev, python3-setuptools, python3-numpy (>= 1:1.9.2), python3-matplotlib, python3-cachecontrol, python3-decorator, python3-pandas (>= 0.19.2), python3-scipy (>= 1.3.3), python3-ipython, python3-hdmedians (>= 0.14.1), python3-sklearn, python3-natsort, libssw-dev, sphinx-common, libsimde-dev, python3-lockfile, python3-pytest
Filtered Build-Depends: debhelper-compat (= 13), cython3, dh-python, python3-all-dev, python3-setuptools, python3-numpy (>= 1:1.9.2), python3-matplotlib, python3-cachecontrol, python3-decorator, python3-pandas (>= 0.19.2), python3-scipy (>= 1.3.3), python3-ipython, python3-hdmedians (>= 0.14.1), python3-sklearn, python3-natsort, libssw-dev, sphinx-common, libsimde-dev, python3-lockfile, python3-pytest
dpkg-deb: building package 'sbuild-build-depends-python-skbio-dummy' in '/<<BUILDDIR>>/resolver-OPfPfE/apt_archive/sbuild-build-depends-python-skbio-dummy.deb'.
dpkg-scanpackages: warning: Packages in archive but missing from override file:
dpkg-scanpackages: warning:   sbuild-build-depends-core-dummy sbuild-build-depends-python-skbio-dummy
dpkg-scanpackages: info: Wrote 2 entries to output Packages file.
gpg: using "Sbuild Signer" as default secret key for signing
Ign:1 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ InRelease
Get:2 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Release [963 B]
Get:3 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Release.gpg [370 B]
Get:4 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Sources [667 B]
Get:5 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ Packages [732 B]
Fetched 2732 B in 1s (3504 B/s)
Reading package lists...
W: No sandbox user '_apt' on the system, can not drop privileges
Reading package lists...

Install python-skbio build dependencies (apt-based resolver)
------------------------------------------------------------

Installing build dependencies
Reading package lists...
Building dependency tree...
Reading state information...
The following packages were automatically installed and are no longer required:
  krb5-locales libpam-cap libperl5.34 netbase perl-modules-5.34
Use 'apt autoremove' to remove them.
The following additional packages will be installed:
  autoconf automake autopoint autotools-dev blt bsdextrautils ca-certificates
  cpp-12 cython3 debhelper dh-autoreconf dh-python dh-strip-nondeterminism dwz
  file fontconfig-config fonts-dejavu-core fonts-lyx g++-12 gcc-12 gcc-12-base
  gettext gettext-base groff-base intltool-debian libarchive-zip-perl libasan8
  libatomic1 libblas3 libbrotli1 libbsd0 libcc1-0 libdebhelper-perl
  libdeflate0 libelf1 libexpat1 libexpat1-dev libfile-stripnondeterminism-perl
  libfontconfig1 libfreetype6 libfribidi0 libgcc-12-dev libgcc-s1 libgfortran5
  libglib2.0-0 libgomp1 libgraphite2-3 libharfbuzz0b libicu72 libimagequant0
  libjbig0 libjpeg62-turbo libjs-jquery libjs-jquery-ui libjs-sphinxdoc
  libjs-underscore libjson-perl liblapack3 liblbfgsb0 liblcms2-2 liblerc4
  libmagic-mgc libmagic1 libmd0 libmpdec3 libncurses6 libopenjp2-7
  libpipeline1 libpng16-16 libprocps8 libpython3-all-dev libpython3-dev
  libpython3-stdlib libpython3.10 libpython3.10-dev libpython3.10-minimal
  libpython3.10-stdlib libqhull-r8.0 libraqm0 libsimde-dev libssl3 libssw-dev
  libssw0 libstdc++-12-dev libstdc++6 libsub-override-perl libtcl8.6 libtiff5
  libtk8.6 libtool libubsan1 libuchardet0 libwebp7 libwebpdemux2 libwebpmux3
  libx11-6 libx11-data libxau6 libxcb1 libxdmcp6 libxext6 libxft2 libxml2
  libxrender1 libxslt1.1 libxss1 m4 man-db media-types openssl po-debconf
  procps python-matplotlib-data python3 python3-all python3-all-dev
  python3-appdirs python3-asttokens python3-attr python3-backcall
  python3-brotli python3-cachecontrol python3-certifi python3-chardet
  python3-charset-normalizer python3-cycler python3-dateutil python3-decorator
  python3-dev python3-distutils python3-executing python3-fonttools python3-fs
  python3-hdmedians python3-idna python3-iniconfig python3-ipython
  python3-jedi python3-joblib python3-kiwisolver python3-lib2to3
  python3-lockfile python3-lxml python3-lz4 python3-matplotlib
  python3-matplotlib-inline python3-minimal python3-more-itertools
  python3-mpmath python3-msgpack python3-natsort python3-numpy
  python3-packaging python3-pandas python3-pandas-lib python3-parso
  python3-pexpect python3-pickleshare python3-pil python3-pil.imagetk
  python3-pkg-resources python3-pluggy python3-prompt-toolkit
  python3-ptyprocess python3-pure-eval python3-py python3-pygments
  python3-pyparsing python3-pytest python3-requests python3-scipy
  python3-setuptools python3-six python3-sklearn python3-sklearn-lib
  python3-stack-data python3-sympy python3-threadpoolctl python3-tk
  python3-tomli python3-traitlets python3-tz python3-ufolib2
  python3-unicodedata2 python3-urllib3 python3-wcwidth python3.10
  python3.10-dev python3.10-minimal sphinx-common tk8.6-blt2.5 ucf
  unicode-data x11-common zlib1g-dev
Suggested packages:
  autoconf-archive gnu-standards autoconf-doc blt-demo gcc-12-locales
  cpp-12-doc cython-doc dh-make flit python3-build python3-installer
  gcc-12-doc gettext-doc libasprintf-dev libgettextpo-dev groff
  low-memory-monitor libjs-jquery-ui-docs liblcms2-utils libstdc++-12-doc
  tcl8.6 tk8.6 libtool-doc gfortran | fortran95-compiler gcj-jdk m4-doc
  apparmor less www-browser libmail-box-perl python3-doc python3-venv
  python-attr-doc python-cycler-doc python-ipython-doc python-lockfile-doc
  python-lxml-doc cm-super-minimal dvipng ffmpeg fonts-staypuft ghostscript
  gir1.2-gtk-3.0 inkscape ipython3 librsvg2-common python-matplotlib-doc
  python3-cairocffi python3-gi python3-gi-cairo python3-gobject python3-pyqt5
  python3-sip python3-tornado texlive-extra-utils texlive-latex-extra
  python-mpmath-doc python3-gmpy2 python-natsort-doc gfortran python-numpy-doc
  python-pandas-doc python3-statsmodels python-pexpect-doc python-pil-doc
  subversion python-pygments-doc ttf-bitstream-vera python-pyparsing-doc
  python3-cryptography python3-openssl python3-socks python-requests-doc
  python-scipy-doc python-setuptools-doc python3-dap python-sklearn-doc
  texlive-fonts-extra python-sympy-doc tix python3-tk-dbg python3.10-venv
  python3.10-doc binfmt-support
Recommended packages:
  curl | wget | lynx libarchive-cpio-perl libglib2.0-data shared-mime-info
  xdg-user-dirs javascript-common libjson-xs-perl libgpm2 libltdl-dev
  libmail-sendmail-perl psmisc python3-simplejson python3-psutil python3-bs4
  python3-html5lib python3-bottleneck python3-numexpr python3-odf
  python3-openpyxl python3-xlwt python3-tables python3-jinja2 python3-olefile
  python3-nose isympy-common python3-sphinx
The following NEW packages will be installed:
  autoconf automake autopoint autotools-dev blt bsdextrautils ca-certificates
  cython3 debhelper dh-autoreconf dh-python dh-strip-nondeterminism dwz file
  fontconfig-config fonts-dejavu-core fonts-lyx gettext gettext-base
  groff-base intltool-debian libarchive-zip-perl libblas3 libbrotli1 libbsd0
  libdebhelper-perl libdeflate0 libelf1 libexpat1 libexpat1-dev
  libfile-stripnondeterminism-perl libfontconfig1 libfreetype6 libfribidi0
  libgfortran5 libglib2.0-0 libgraphite2-3 libharfbuzz0b libicu72
  libimagequant0 libjbig0 libjpeg62-turbo libjs-jquery libjs-jquery-ui
  libjs-sphinxdoc libjs-underscore libjson-perl liblapack3 liblbfgsb0
  liblcms2-2 liblerc4 libmagic-mgc libmagic1 libmd0 libmpdec3 libncurses6
  libopenjp2-7 libpipeline1 libpng16-16 libprocps8 libpython3-all-dev
  libpython3-dev libpython3-stdlib libpython3.10 libpython3.10-dev
  libpython3.10-minimal libpython3.10-stdlib libqhull-r8.0 libraqm0
  libsimde-dev libssw-dev libssw0 libsub-override-perl libtcl8.6 libtiff5
  libtk8.6 libtool libuchardet0 libwebp7 libwebpdemux2 libwebpmux3 libx11-6
  libx11-data libxau6 libxcb1 libxdmcp6 libxext6 libxft2 libxml2 libxrender1
  libxslt1.1 libxss1 m4 man-db media-types openssl po-debconf procps
  python-matplotlib-data python3 python3-all python3-all-dev python3-appdirs
  python3-asttokens python3-attr python3-backcall python3-brotli
  python3-cachecontrol python3-certifi python3-chardet
  python3-charset-normalizer python3-cycler python3-dateutil python3-decorator
  python3-dev python3-distutils python3-executing python3-fonttools python3-fs
  python3-hdmedians python3-idna python3-iniconfig python3-ipython
  python3-jedi python3-joblib python3-kiwisolver python3-lib2to3
  python3-lockfile python3-lxml python3-lz4 python3-matplotlib
  python3-matplotlib-inline python3-minimal python3-more-itertools
  python3-mpmath python3-msgpack python3-natsort python3-numpy
  python3-packaging python3-pandas python3-pandas-lib python3-parso
  python3-pexpect python3-pickleshare python3-pil python3-pil.imagetk
  python3-pkg-resources python3-pluggy python3-prompt-toolkit
  python3-ptyprocess python3-pure-eval python3-py python3-pygments
  python3-pyparsing python3-pytest python3-requests python3-scipy
  python3-setuptools python3-six python3-sklearn python3-sklearn-lib
  python3-stack-data python3-sympy python3-threadpoolctl python3-tk
  python3-tomli python3-traitlets python3-tz python3-ufolib2
  python3-unicodedata2 python3-urllib3 python3-wcwidth python3.10
  python3.10-dev python3.10-minimal sbuild-build-depends-python-skbio-dummy
  sphinx-common tk8.6-blt2.5 ucf unicode-data x11-common zlib1g-dev
The following packages will be upgraded:
  cpp-12 g++-12 gcc-12 gcc-12-base libasan8 libatomic1 libcc1-0 libgcc-12-dev
  libgcc-s1 libgomp1 libssl3 libstdc++-12-dev libstdc++6 libubsan1
14 upgraded, 182 newly installed, 0 to remove and 51 not upgraded.
Need to get 140 MB of archives.
After this operation, 457 MB of additional disk space will be used.
Get:1 copy:/<<BUILDDIR>>/resolver-OPfPfE/apt_archive ./ sbuild-build-depends-python-skbio-dummy 0.invalid.0 [1012 B]
Get:2 http://172.17.4.1/private bookworm-staging/main armhf libcc1-0 armhf 12.2.0-9+rpi1 [32.2 kB]
Get:3 http://172.17.4.1/private bookworm-staging/main armhf libgomp1 armhf 12.2.0-9+rpi1 [95.4 kB]
Get:4 http://172.17.4.1/private bookworm-staging/main armhf libatomic1 armhf 12.2.0-9+rpi1 [8224 B]
Get:5 http://172.17.4.1/private bookworm-staging/main armhf libasan8 armhf 12.2.0-9+rpi1 [2089 kB]
Get:6 http://172.17.4.1/private bookworm-staging/main armhf libubsan1 armhf 12.2.0-9+rpi1 [861 kB]
Get:7 http://172.17.4.1/private bookworm-staging/main armhf gcc-12-base armhf 12.2.0-9+rpi1 [37.2 kB]
Get:8 http://172.17.4.1/private bookworm-staging/main armhf libstdc++6 armhf 12.2.0-9+rpi1 [504 kB]
Get:9 http://172.17.4.1/private bookworm-staging/main armhf g++-12 armhf 12.2.0-9+rpi1 [7913 kB]
Get:10 http://172.17.4.1/private bookworm-staging/main armhf libstdc++-12-dev armhf 12.2.0-9+rpi1 [2025 kB]
Get:11 http://172.17.4.1/private bookworm-staging/main armhf libgcc-12-dev armhf 12.2.0-9+rpi1 [725 kB]
Get:12 http://172.17.4.1/private bookworm-staging/main armhf gcc-12 armhf 12.2.0-9+rpi1 [14.2 MB]
Get:13 http://172.17.4.1/private bookworm-staging/main armhf cpp-12 armhf 12.2.0-9+rpi1 [7156 kB]
Get:14 http://172.17.4.1/private bookworm-staging/main armhf libgcc-s1 armhf 12.2.0-9+rpi1 [36.1 kB]
Get:15 http://172.17.4.1/private bookworm-staging/main armhf libssl3 armhf 3.0.7-1 [1614 kB]
Get:16 http://172.17.4.1/private bookworm-staging/main armhf libpython3.10-minimal armhf 3.10.8-3 [769 kB]
Get:17 http://172.17.4.1/private bookworm-staging/main armhf libexpat1 armhf 2.5.0-1 [77.2 kB]
Get:18 http://172.17.4.1/private bookworm-staging/main armhf python3.10-minimal armhf 3.10.8-3 [1478 kB]
Get:19 http://172.17.4.1/private bookworm-staging/main armhf python3-minimal armhf 3.10.6-1 [38.7 kB]
Get:20 http://172.17.4.1/private bookworm-staging/main armhf media-types all 8.0.0 [33.4 kB]
Get:21 http://172.17.4.1/private bookworm-staging/main armhf libmpdec3 armhf 2.5.1-2+rpi1 [73.5 kB]
Get:22 http://172.17.4.1/private bookworm-staging/main armhf libpython3.10-stdlib armhf 3.10.8-3 [1597 kB]
Get:23 http://172.17.4.1/private bookworm-staging/main armhf python3.10 armhf 3.10.8-3 [506 kB]
Get:24 http://172.17.4.1/private bookworm-staging/main armhf libpython3-stdlib armhf 3.10.6-1 [21.7 kB]
Get:25 http://172.17.4.1/private bookworm-staging/main armhf python3 armhf 3.10.6-1 [38.2 kB]
Get:26 http://172.17.4.1/private bookworm-staging/main armhf libuchardet0 armhf 0.0.7-1 [65.0 kB]
Get:27 http://172.17.4.1/private bookworm-staging/main armhf groff-base armhf 1.22.4-8 [793 kB]
Get:28 http://172.17.4.1/private bookworm-staging/main armhf bsdextrautils armhf 2.38.1-1.1 [78.2 kB]
Get:29 http://172.17.4.1/private bookworm-staging/main armhf libpipeline1 armhf 1.5.7-1 [33.4 kB]
Get:30 http://172.17.4.1/private bookworm-staging/main armhf man-db armhf 2.11.0-1 [1299 kB]
Get:31 http://172.17.4.1/private bookworm-staging/main armhf libncurses6 armhf 6.3+20220423-2 [79.6 kB]
Get:32 http://172.17.4.1/private bookworm-staging/main armhf libprocps8 armhf 2:3.3.17-7.1 [41.9 kB]
Get:33 http://172.17.4.1/private bookworm-staging/main armhf procps armhf 2:3.3.17-7.1 [457 kB]
Get:34 http://172.17.4.1/private bookworm-staging/main armhf openssl armhf 3.0.7-1 [1349 kB]
Get:35 http://172.17.4.1/private bookworm-staging/main armhf ca-certificates all 20211016 [156 kB]
Get:36 http://172.17.4.1/private bookworm-staging/main armhf libmagic-mgc armhf 1:5.41-4 [295 kB]
Get:37 http://172.17.4.1/private bookworm-staging/main armhf libmagic1 armhf 1:5.41-4 [120 kB]
Get:38 http://172.17.4.1/private bookworm-staging/main armhf file armhf 1:5.41-4 [65.8 kB]
Get:39 http://172.17.4.1/private bookworm-staging/main armhf gettext-base armhf 0.21-9 [171 kB]
Get:40 http://172.17.4.1/private bookworm-staging/main armhf ucf all 3.0043 [74.0 kB]
Get:41 http://172.17.4.1/private bookworm-staging/main armhf m4 armhf 1.4.19-1 [260 kB]
Get:42 http://172.17.4.1/private bookworm-staging/main armhf autoconf all 2.71-2 [343 kB]
Get:43 http://172.17.4.1/private bookworm-staging/main armhf autotools-dev all 20220109.1 [51.6 kB]
Get:44 http://172.17.4.1/private bookworm-staging/main armhf automake all 1:1.16.5-1.3 [823 kB]
Get:45 http://172.17.4.1/private bookworm-staging/main armhf autopoint all 0.21-9 [510 kB]
Get:46 http://172.17.4.1/private bookworm-staging/main armhf libtcl8.6 armhf 8.6.12+dfsg-1 [906 kB]
Get:47 http://172.17.4.1/private bookworm-staging/main armhf libbrotli1 armhf 1.0.9-2+b2 [260 kB]
Get:48 http://172.17.4.1/private bookworm-staging/main armhf libpng16-16 armhf 1.6.38-2 [273 kB]
Get:49 http://172.17.4.1/private bookworm-staging/main armhf libfreetype6 armhf 2.12.1+dfsg-3 [352 kB]
Get:50 http://172.17.4.1/private bookworm-staging/main armhf fonts-dejavu-core all 2.37-2 [1069 kB]
Get:51 http://172.17.4.1/private bookworm-staging/main armhf fontconfig-config armhf 2.13.1-4.5 [281 kB]
Get:52 http://172.17.4.1/private bookworm-staging/main armhf libfontconfig1 armhf 2.13.1-4.5 [330 kB]
Get:53 http://172.17.4.1/private bookworm-staging/main armhf libxau6 armhf 1:1.0.9-1 [19.1 kB]
Get:54 http://172.17.4.1/private bookworm-staging/main armhf libmd0 armhf 1.0.4-2 [28.6 kB]
Get:55 http://172.17.4.1/private bookworm-staging/main armhf libbsd0 armhf 0.11.7-1 [112 kB]
Get:56 http://172.17.4.1/private bookworm-staging/main armhf libxdmcp6 armhf 1:1.1.2-3 [25.0 kB]
Get:57 http://172.17.4.1/private bookworm-staging/main armhf libxcb1 armhf 1.15-1 [139 kB]
Get:58 http://172.17.4.1/private bookworm-staging/main armhf libx11-data all 2:1.8.1-2 [308 kB]
Get:59 http://172.17.4.1/private bookworm-staging/main armhf libx11-6 armhf 2:1.8.1-2 [704 kB]
Get:60 http://172.17.4.1/private bookworm-staging/main armhf libxrender1 armhf 1:0.9.10-1.1 [30.0 kB]
Get:61 http://172.17.4.1/private bookworm-staging/main armhf libxft2 armhf 2.3.6-1 [53.7 kB]
Get:62 http://172.17.4.1/private bookworm-staging/main armhf libxext6 armhf 2:1.3.4-1 [48.0 kB]
Get:63 http://172.17.4.1/private bookworm-staging/main armhf x11-common all 1:7.7+23 [252 kB]
Get:64 http://172.17.4.1/private bookworm-staging/main armhf libxss1 armhf 1:1.2.3-1 [17.3 kB]
Get:65 http://172.17.4.1/private bookworm-staging/main armhf libtk8.6 armhf 8.6.12-1 [680 kB]
Get:66 http://172.17.4.1/private bookworm-staging/main armhf tk8.6-blt2.5 armhf 2.5.3+dfsg-4.1 [477 kB]
Get:67 http://172.17.4.1/private bookworm-staging/main armhf blt armhf 2.5.3+dfsg-4.1 [14.9 kB]
Get:68 http://172.17.4.1/private bookworm-staging/main armhf cython3 armhf 0.29.32-1 [1224 kB]
Get:69 http://172.17.4.1/private bookworm-staging/main armhf libdebhelper-perl all 13.10.1 [79.9 kB]
Get:70 http://172.17.4.1/private bookworm-staging/main armhf libtool all 2.4.7-4 [526 kB]
Get:71 http://172.17.4.1/private bookworm-staging/main armhf dh-autoreconf all 20 [17.1 kB]
Get:72 http://172.17.4.1/private bookworm-staging/main armhf libarchive-zip-perl all 1.68-1 [104 kB]
Get:73 http://172.17.4.1/private bookworm-staging/main armhf libsub-override-perl all 0.09-4 [9304 B]
Get:74 http://172.17.4.1/private bookworm-staging/main armhf libfile-stripnondeterminism-perl all 1.13.0-1 [26.6 kB]
Get:75 http://172.17.4.1/private bookworm-staging/main armhf dh-strip-nondeterminism all 1.13.0-1 [15.8 kB]
Get:76 http://172.17.4.1/private bookworm-staging/main armhf libelf1 armhf 0.187-2+rpi2 [177 kB]
Get:77 http://172.17.4.1/private bookworm-staging/main armhf dwz armhf 0.14+20220924-2 [93.1 kB]
Get:78 http://172.17.4.1/private bookworm-staging/main armhf libicu72 armhf 72.1-2 [9008 kB]
Get:79 http://172.17.4.1/private bookworm-staging/main armhf libxml2 armhf 2.9.14+dfsg-1.1 [570 kB]
Get:80 http://172.17.4.1/private bookworm-staging/main armhf gettext armhf 0.21-9 [1220 kB]
Get:81 http://172.17.4.1/private bookworm-staging/main armhf intltool-debian all 0.35.0+20060710.6 [22.9 kB]
Get:82 http://172.17.4.1/private bookworm-staging/main armhf po-debconf all 1.0.21+nmu1 [248 kB]
Get:83 http://172.17.4.1/private bookworm-staging/main armhf debhelper all 13.10.1 [940 kB]
Get:84 http://172.17.4.1/private bookworm-staging/main armhf python3-lib2to3 all 3.10.8-1 [77.3 kB]
Get:85 http://172.17.4.1/private bookworm-staging/main armhf python3-distutils all 3.10.8-1 [139 kB]
Get:86 http://172.17.4.1/private bookworm-staging/main armhf dh-python all 5.20220819+rpi1 [114 kB]
Get:87 http://172.17.4.1/private bookworm-staging/main armhf fonts-lyx all 2.3.6.1-1 [206 kB]
Get:88 http://172.17.4.1/private bookworm-staging/main armhf libblas3 armhf 3.10.1-2 [109 kB]
Get:89 http://172.17.4.1/private bookworm-staging/main armhf libdeflate0 armhf 1.14-1 [50.9 kB]
Get:90 http://172.17.4.1/private bookworm-staging/main armhf libexpat1-dev armhf 2.5.0-1 [130 kB]
Get:91 http://172.17.4.1/private bookworm-staging/main armhf libfribidi0 armhf 1.0.8-2.1 [63.7 kB]
Get:92 http://172.17.4.1/private bookworm-staging/main armhf libgfortran5 armhf 12.2.0-9+rpi1 [238 kB]
Get:93 http://172.17.4.1/private bookworm-staging/main armhf libglib2.0-0 armhf 2.74.1-2 [1214 kB]
Get:94 http://172.17.4.1/private bookworm-staging/main armhf libgraphite2-3 armhf 1.3.14-1 [70.3 kB]
Get:95 http://172.17.4.1/private bookworm-staging/main armhf libharfbuzz0b armhf 5.2.0-2+b1 [1832 kB]
Get:96 http://172.17.4.1/private bookworm-staging/main armhf libimagequant0 armhf 2.17.0-1 [28.0 kB]
Get:97 http://172.17.4.1/private bookworm-staging/main armhf libjbig0 armhf 2.1-3.1+b2 [27.6 kB]
Get:98 http://172.17.4.1/private bookworm-staging/main armhf libjpeg62-turbo armhf 1:2.1.2-1 [144 kB]
Get:99 http://172.17.4.1/private bookworm-staging/main armhf libjs-jquery all 3.6.1+dfsg+~3.5.14-1 [326 kB]
Get:100 http://172.17.4.1/private bookworm-staging/main armhf libjs-jquery-ui all 1.13.2+dfsg-1 [250 kB]
Get:101 http://172.17.4.1/private bookworm-staging/main armhf libjs-underscore all 1.13.4~dfsg+~1.11.4-2 [116 kB]
Get:102 http://172.17.4.1/private bookworm-staging/main armhf libjs-sphinxdoc all 4.5.0-4 [142 kB]
Get:103 http://172.17.4.1/private bookworm-staging/main armhf libjson-perl all 4.10000-1 [87.5 kB]
Get:104 http://172.17.4.1/private bookworm-staging/main armhf liblapack3 armhf 3.10.1-2 [1587 kB]
Get:105 http://172.17.4.1/private bookworm-staging/main armhf liblbfgsb0 armhf 3.0+dfsg.3-11 [24.3 kB]
Get:106 http://172.17.4.1/private bookworm-staging/main armhf liblcms2-2 armhf 2.13.1-1 [125 kB]
Get:107 http://172.17.4.1/private bookworm-staging/main armhf liblerc4 armhf 4.0.0+ds-2 [133 kB]
Get:108 http://172.17.4.1/private bookworm-staging/main armhf libopenjp2-7 armhf 2.5.0-1 [165 kB]
Get:109 http://172.17.4.1/private bookworm-staging/main armhf libpython3.10 armhf 3.10.8-3 [1458 kB]
Get:110 http://172.17.4.1/private bookworm-staging/main armhf zlib1g-dev armhf 1:1.2.11.dfsg-4.1 [183 kB]
Get:111 http://172.17.4.1/private bookworm-staging/main armhf libpython3.10-dev armhf 3.10.8-3 [2935 kB]
Get:112 http://172.17.4.1/private bookworm-staging/main armhf libpython3-dev armhf 3.10.6-1 [22.0 kB]
Get:113 http://172.17.4.1/private bookworm-staging/main armhf libpython3-all-dev armhf 3.10.6-1 [1068 B]
Get:114 http://172.17.4.1/private bookworm-staging/main armhf libqhull-r8.0 armhf 2020.2-5 [221 kB]
Get:115 http://172.17.4.1/private bookworm-staging/main armhf libraqm0 armhf 0.7.0-4 [9388 B]
Get:116 http://172.17.4.1/private bookworm-staging/main armhf libsimde-dev all 0.7.2-6 [259 kB]
Get:117 http://172.17.4.1/private bookworm-staging/main armhf libssw0 armhf 1.1-13 [21.0 kB]
Get:118 http://172.17.4.1/private bookworm-staging/main armhf libssw-dev armhf 1.1-13 [24.0 kB]
Get:119 http://172.17.4.1/private bookworm-staging/main armhf libwebp7 armhf 1.2.2-2+b1 [237 kB]
Get:120 http://172.17.4.1/private bookworm-staging/main armhf libtiff5 armhf 4.4.0-5+b1 [278 kB]
Get:121 http://172.17.4.1/private bookworm-staging/main armhf libwebpdemux2 armhf 1.2.2-2+b1 [95.5 kB]
Get:122 http://172.17.4.1/private bookworm-staging/main armhf libwebpmux3 armhf 1.2.2-2+b1 [103 kB]
Get:123 http://172.17.4.1/private bookworm-staging/main armhf libxslt1.1 armhf 1.1.35-1 [221 kB]
Get:124 http://172.17.4.1/private bookworm-staging/main armhf python-matplotlib-data all 3.5.2-4 [2725 kB]
Get:125 http://172.17.4.1/private bookworm-staging/main armhf python3-all armhf 3.10.6-1 [1060 B]
Get:126 http://172.17.4.1/private bookworm-staging/main armhf python3.10-dev armhf 3.10.8-3 [509 kB]
Get:127 http://172.17.4.1/private bookworm-staging/main armhf python3-dev armhf 3.10.6-1 [25.4 kB]
Get:128 http://172.17.4.1/private bookworm-staging/main armhf python3-all-dev armhf 3.10.6-1 [1068 B]
Get:129 http://172.17.4.1/private bookworm-staging/main armhf python3-appdirs all 1.4.4-3 [13.0 kB]
Get:130 http://172.17.4.1/private bookworm-staging/main armhf python3-six all 1.16.0-4 [17.5 kB]
Get:131 http://172.17.4.1/private bookworm-staging/main armhf python3-asttokens all 2.0.5-2 [15.1 kB]
Get:132 http://172.17.4.1/private bookworm-staging/main armhf python3-attr all 22.1.0-1 [64.1 kB]
Get:133 http://172.17.4.1/private bookworm-staging/main armhf python3-backcall all 0.2.0-3 [12.1 kB]
Get:134 http://172.17.4.1/private bookworm-staging/main armhf python3-brotli armhf 1.0.9-2+b2 [278 kB]
Get:135 http://172.17.4.1/private bookworm-staging/main armhf python3-certifi all 2022.6.15-2 [154 kB]
Get:136 http://172.17.4.1/private bookworm-staging/main armhf python3-charset-normalizer all 2.0.6-3 [30.8 kB]
Get:137 http://172.17.4.1/private bookworm-staging/main armhf python3-idna all 3.3-1 [39.4 kB]
Get:138 http://172.17.4.1/private bookworm-staging/main armhf python3-urllib3 all 1.26.12-1 [117 kB]
Get:139 http://172.17.4.1/private bookworm-staging/main armhf python3-pkg-resources all 65.5.0-1 [278 kB]
Get:140 http://172.17.4.1/private bookworm-staging/main armhf python3-chardet all 4.0.0-2 [99.1 kB]
Get:141 http://172.17.4.1/private bookworm-staging/main armhf python3-requests all 2.27.1+dfsg-1 [71.9 kB]
Get:142 http://172.17.4.1/private bookworm-staging/main armhf python3-msgpack armhf 1.0.3-1 [75.9 kB]
Get:143 http://172.17.4.1/private bookworm-staging/main armhf python3-cachecontrol all 0.12.12-2 [17.9 kB]
Get:144 http://172.17.4.1/private bookworm-staging/main armhf python3-cycler all 0.11.0-1 [8020 B]
Get:145 http://172.17.4.1/private bookworm-staging/main armhf python3-dateutil all 2.8.2-1 [78.2 kB]
Get:146 http://172.17.4.1/private bookworm-staging/main armhf python3-decorator all 5.1.1-2 [16.5 kB]
Get:147 http://172.17.4.1/private bookworm-staging/main armhf python3-executing all 0.8.0-1 [15.2 kB]
Get:148 http://172.17.4.1/private bookworm-staging/main armhf python3-numpy armhf 1:1.21.5-2 [3409 kB]
Get:149 http://172.17.4.1/private bookworm-staging/main armhf python3-scipy armhf 1.8.1-14+rpi1 [12.9 MB]
Get:150 http://172.17.4.1/private bookworm-staging/main armhf python3-ufolib2 all 0.13.1+dfsg1-1 [32.0 kB]
Get:151 http://172.17.4.1/private bookworm-staging/main armhf python3-mpmath all 1.2.1-2 [418 kB]
Get:152 http://172.17.4.1/private bookworm-staging/main armhf python3-sympy all 1.11.1-1 [4358 kB]
Get:153 http://172.17.4.1/private bookworm-staging/main armhf python3-tz all 2022.6-1 [31.8 kB]
Get:154 http://172.17.4.1/private bookworm-staging/main armhf python3-fs all 2.4.16-2 [95.2 kB]
Get:155 http://172.17.4.1/private bookworm-staging/main armhf python3-lxml armhf 4.9.1-1 [959 kB]
Get:156 http://172.17.4.1/private bookworm-staging/main armhf python3-lz4 armhf 4.0.2+dfsg-1 [23.9 kB]
Get:157 http://172.17.4.1/private bookworm-staging/main armhf python3-unicodedata2 armhf 14.0.0+ds2-1 [298 kB]
Get:158 http://172.17.4.1/private bookworm-staging/main armhf unicode-data all 15.0.0-1 [7984 kB]
Get:159 http://172.17.4.1/private bookworm-staging/main armhf python3-fonttools armhf 4.37.4-1 [983 kB]
Get:160 http://172.17.4.1/private bookworm-staging/main armhf python3-hdmedians armhf 0.14.2-4+b1 [104 kB]
Get:161 http://172.17.4.1/private bookworm-staging/main armhf python3-iniconfig all 1.1.1-2 [6396 B]
Get:162 http://172.17.4.1/private bookworm-staging/main armhf python3-parso all 0.8.1-1 [65.9 kB]
Get:163 http://172.17.4.1/private bookworm-staging/main armhf python3-jedi all 0.18.0-1 [620 kB]
Get:164 http://172.17.4.1/private bookworm-staging/main armhf python3-traitlets all 5.5.0-1 [92.4 kB]
Get:165 http://172.17.4.1/private bookworm-staging/main armhf python3-matplotlib-inline all 0.1.6-1 [8540 B]
Get:166 http://172.17.4.1/private bookworm-staging/main armhf python3-ptyprocess all 0.7.0-3 [14.9 kB]
Get:167 http://172.17.4.1/private bookworm-staging/main armhf python3-pexpect all 4.8.0-3 [55.0 kB]
Get:168 http://172.17.4.1/private bookworm-staging/main armhf python3-pickleshare all 0.7.5-5 [7600 B]
Get:169 http://172.17.4.1/private bookworm-staging/main armhf python3-wcwidth all 0.2.5+dfsg1-1 [21.7 kB]
Get:170 http://172.17.4.1/private bookworm-staging/main armhf python3-prompt-toolkit all 3.0.32-1 [273 kB]
Get:171 http://172.17.4.1/private bookworm-staging/main armhf python3-pygments all 2.12.0+dfsg-2 [765 kB]
Get:172 http://172.17.4.1/private bookworm-staging/main armhf python3-pure-eval all 0.2.2-1 [11.2 kB]
Get:173 http://172.17.4.1/private bookworm-staging/main armhf python3-stack-data all 0.5.0-1 [21.1 kB]
Get:174 http://172.17.4.1/private bookworm-staging/main armhf python3-setuptools all 65.5.0-1 [519 kB]
Get:175 http://172.17.4.1/private bookworm-staging/main armhf python3-ipython all 8.5.0-1 [517 kB]
Get:176 http://172.17.4.1/private bookworm-staging/main armhf python3-joblib all 1.2.0-4 [211 kB]
Get:177 http://172.17.4.1/private bookworm-staging/main armhf python3-kiwisolver armhf 1.3.2-1+b1 [52.1 kB]
Get:178 http://172.17.4.1/private bookworm-staging/main armhf python3-lockfile all 1:0.12.2-2.2 [17.3 kB]
Get:179 http://172.17.4.1/private bookworm-staging/main armhf python3-pil armhf 9.2.0-1.1 [427 kB]
Get:180 http://172.17.4.1/private bookworm-staging/main armhf python3-tk armhf 3.10.8-1 [101 kB]
Get:181 http://172.17.4.1/private bookworm-staging/main armhf python3-pil.imagetk armhf 9.2.0-1.1 [71.8 kB]
Get:182 http://172.17.4.1/private bookworm-staging/main armhf python3-pyparsing all 3.0.7-3 [136 kB]
Get:183 http://172.17.4.1/private bookworm-staging/main armhf python3-packaging all 21.3-1.1 [34.2 kB]
Get:184 http://172.17.4.1/private bookworm-staging/main armhf python3-matplotlib armhf 3.5.2-4 [6479 kB]
Get:185 http://172.17.4.1/private bookworm-staging/main armhf python3-more-itertools all 8.10.0-2 [53.0 kB]
Get:186 http://172.17.4.1/private bookworm-staging/main armhf python3-natsort all 8.0.2-2 [39.0 kB]
Get:187 http://172.17.4.1/private bookworm-staging/main armhf python3-pandas-lib armhf 1.3.5+dfsg-6 [2906 kB]
Get:188 http://172.17.4.1/private bookworm-staging/main armhf python3-pandas all 1.3.5+dfsg-6 [2646 kB]
Get:189 http://172.17.4.1/private bookworm-staging/main armhf python3-pluggy all 1.0.0+repack-1 [19.7 kB]
Get:190 http://172.17.4.1/private bookworm-staging/main armhf python3-py all 1.11.0-1 [89.2 kB]
Get:191 http://172.17.4.1/private bookworm-staging/main armhf python3-tomli all 2.0.1-1 [18.1 kB]
Get:192 http://172.17.4.1/private bookworm-staging/main armhf python3-pytest all 7.1.2-4 [218 kB]
Get:193 http://172.17.4.1/private bookworm-staging/main armhf python3-threadpoolctl all 3.1.0-1 [21.2 kB]
Get:194 http://172.17.4.1/private bookworm-staging/main armhf python3-sklearn-lib armhf 1.1.2+dfsg-8 [1801 kB]
Get:195 http://172.17.4.1/private bookworm-staging/main armhf python3-sklearn all 1.1.2+dfsg-8 [2040 kB]
Get:196 http://172.17.4.1/private bookworm-staging/main armhf sphinx-common all 4.5.0-4 [638 kB]
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Setting up media-types (8.0.0) ...
Setting up libpipeline1:armhf (1.5.7-1) ...
Setting up libgraphite2-3:armhf (1.3.14-1) ...
Setting up libsimde-dev (0.7.2-6) ...
Setting up liblcms2-2:armhf (2.13.1-1) ...
Setting up libxau6:armhf (1:1.0.9-1) ...
Setting up cpp-12 (12.2.0-9+rpi1) ...
Setting up libicu72:armhf (72.1-2) ...
Setting up liblerc4:armhf (4.0.0+ds-2) ...
Setting up bsdextrautils (2.38.1-1.1) ...
Setting up libmagic-mgc (1:5.41-4) ...
Setting up libarchive-zip-perl (1.68-1) ...
Setting up libglib2.0-0:armhf (2.74.1-2) ...
No schema files found: doing nothing.
Setting up fonts-lyx (2.3.6.1-1) ...
Setting up libdebhelper-perl (13.10.1) ...
Setting up libbrotli1:armhf (1.0.9-2+b2) ...
Setting up x11-common (1:7.7+23) ...
invoke-rc.d: could not determine current runlevel
invoke-rc.d: policy-rc.d denied execution of restart.
Setting up libmagic1:armhf (1:5.41-4) ...
Setting up libdeflate0:armhf (1.14-1) ...
Setting up gettext-base (0.21-9) ...
Setting up m4 (1.4.19-1) ...
Setting up libqhull-r8.0:armhf (2020.2-5) ...
Setting up file (1:5.41-4) ...
Setting up libgomp1:armhf (12.2.0-9+rpi1) ...
Setting up libjbig0:armhf (2.1-3.1+b2) ...
Setting up unicode-data (15.0.0-1) ...
Setting up autotools-dev (20220109.1) ...
Setting up libblas3:armhf (3.10.1-2) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so.3 to provide /usr/lib/arm-linux-gnueabihf/libblas.so.3 (libblas.so.3-arm-linux-gnueabihf) in auto mode
Setting up libexpat1-dev:armhf (2.5.0-1) ...
Setting up libjpeg62-turbo:armhf (1:2.1.2-1) ...
Setting up libx11-data (2:1.8.1-2) ...
Setting up libncurses6:armhf (6.3+20220423-2) ...
Setting up libfribidi0:armhf (1.0.8-2.1) ...
Setting up libimagequant0:armhf (2.17.0-1) ...
Setting up libpng16-16:armhf (1.6.38-2) ...
Setting up libtcl8.6:armhf (8.6.12+dfsg-1) ...
Setting up autopoint (0.21-9) ...
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Setting up ucf (3.0043) ...
Setting up libgfortran5:armhf (12.2.0-9+rpi1) ...
Setting up autoconf (2.71-2) ...
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Setting up libubsan1:armhf (12.2.0-9+rpi1) ...
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Setting up libmd0:armhf (1.0.4-2) ...
Setting up libasan8:armhf (12.2.0-9+rpi1) ...
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Setting up libjson-perl (4.10000-1) ...
Setting up libmpdec3:armhf (2.5.1-2+rpi1) ...
Setting up libopenjp2-7:armhf (2.5.0-1) ...
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Setting up libjs-jquery (3.6.1+dfsg+~3.5.14-1) ...
Setting up python-matplotlib-data (3.5.2-4) ...
Setting up openssl (3.0.7-1) ...
Setting up libwebpmux3:armhf (1.2.2-2+b1) ...
Setting up libbsd0:armhf (0.11.7-1) ...
Setting up libelf1:armhf (0.187-2+rpi2) ...
Setting up libssw0:armhf (1.1-13) ...
Setting up libxml2:armhf (2.9.14+dfsg-1.1) ...
Setting up libcc1-0:armhf (12.2.0-9+rpi1) ...
Setting up libprocps8:armhf (2:3.3.17-7.1) ...
Setting up libjs-underscore (1.13.4~dfsg+~1.11.4-2) ...
Setting up automake (1:1.16.5-1.3) ...
update-alternatives: using /usr/bin/automake-1.16 to provide /usr/bin/automake (automake) in auto mode
Setting up libfile-stripnondeterminism-perl (1.13.0-1) ...
Setting up libxdmcp6:armhf (1:1.1.2-3) ...
Setting up liblapack3:armhf (3.10.1-2) ...
update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so.3 to provide /usr/lib/arm-linux-gnueabihf/liblapack.so.3 (liblapack.so.3-arm-linux-gnueabihf) in auto mode
Setting up libxcb1:armhf (1.15-1) ...
Setting up gettext (0.21-9) ...
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Setting up libtool (2.4.7-4) ...
Setting up fontconfig-config (2.13.1-4.5) ...
Setting up libwebpdemux2:armhf (1.2.2-2+b1) ...
Setting up intltool-debian (0.35.0+20060710.6) ...
Setting up libpython3.10-stdlib:armhf (3.10.8-3) ...
Setting up dh-autoreconf (20) ...
Setting up ca-certificates (20211016) ...
Updating certificates in /etc/ssl/certs...
127 added, 0 removed; done.
Setting up libjs-jquery-ui (1.13.2+dfsg-1) ...
Setting up libfreetype6:armhf (2.12.1+dfsg-3) ...
Setting up libgcc-12-dev:armhf (12.2.0-9+rpi1) ...
Setting up libjs-sphinxdoc (4.5.0-4) ...
Setting up dh-strip-nondeterminism (1.13.0-1) ...
Setting up dwz (0.14+20220924-2) ...
Setting up groff-base (1.22.4-8) ...
Setting up procps (2:3.3.17-7.1) ...
Setting up libxslt1.1:armhf (1.1.35-1) ...
Setting up libx11-6:armhf (2:1.8.1-2) ...
Setting up libharfbuzz0b:armhf (5.2.0-2+b1) ...
Setting up libpython3-stdlib:armhf (3.10.6-1) ...
Setting up liblbfgsb0:armhf (3.0+dfsg.3-11) ...
Setting up libpython3.10:armhf (3.10.8-3) ...
Setting up python3.10 (3.10.8-3) ...
Setting up libxrender1:armhf (1:0.9.10-1.1) ...
Setting up libstdc++-12-dev:armhf (12.2.0-9+rpi1) ...
Setting up po-debconf (1.0.21+nmu1) ...
Setting up libxext6:armhf (2:1.3.4-1) ...
Setting up python3 (3.10.6-1) ...
Setting up man-db (2.11.0-1) ...
Not building database; man-db/auto-update is not 'true'.
Setting up python3-tz (2022.6-1) ...
Setting up python3-natsort (8.0.2-2) ...
Setting up python3-six (1.16.0-4) ...
Setting up python3-decorator (5.1.1-2) ...
Setting up python3-wcwidth (0.2.5+dfsg1-1) ...
Setting up python3-pyparsing (3.0.7-3) ...
Setting up python3-certifi (2022.6.15-2) ...
Setting up gcc-12 (12.2.0-9+rpi1) ...
Setting up python3-brotli (1.0.9-2+b2) ...
Setting up libraqm0:armhf (0.7.0-4) ...
Setting up sphinx-common (4.5.0-4) ...
Setting up python3-cycler (0.11.0-1) ...
Setting up python3-kiwisolver (1.3.2-1+b1) ...
Setting up python3-idna (3.3-1) ...
Setting up cython3 (0.29.32-1) ...
Setting up libpython3.10-dev:armhf (3.10.8-3) ...
Setting up python3-pickleshare (0.7.5-5) ...
Setting up python3-urllib3 (1.26.12-1) ...
Setting up python3-pluggy (1.0.0+repack-1) ...
Setting up python3.10-dev (3.10.8-3) ...
Setting up python3-lxml:armhf (4.9.1-1) ...
Setting up python3-traitlets (5.5.0-1) ...
Setting up python3-dateutil (2.8.2-1) ...
Setting up python3-msgpack (1.0.3-1) ...
Setting up libxss1:armhf (1:1.2.3-1) ...
Setting up python3-mpmath (1.2.1-2) ...
Setting up python3-lib2to3 (3.10.8-1) ...
Setting up python3-appdirs (1.4.4-3) ...
Setting up python3-ptyprocess (0.7.0-3) ...
Setting up g++-12 (12.2.0-9+rpi1) ...
Setting up python3-pkg-resources (65.5.0-1) ...
Setting up python3-prompt-toolkit (3.0.32-1) ...
Setting up python3-distutils (3.10.8-1) ...
Setting up dh-python (5.20220819+rpi1) ...
Setting up python3-more-itertools (8.10.0-2) ...
Setting up python3-iniconfig (1.1.1-2) ...
Setting up python3-sympy (1.11.1-1) ...
Setting up python3-attr (22.1.0-1) ...
Setting up python3-lockfile (1:0.12.2-2.2) ...
Setting up python3-pure-eval (0.2.2-1) ...
Setting up libpython3-dev:armhf (3.10.6-1) ...
Setting up python3-setuptools (65.5.0-1) ...
Setting up python3-tomli (2.0.1-1) ...
Setting up python3-py (1.11.0-1) ...
Setting up python3-backcall (0.2.0-3) ...
Setting up python3-joblib (1.2.0-4) ...
Setting up python3-parso (0.8.1-1) ...
Setting up python3-lz4 (4.0.2+dfsg-1) ...
Setting up python3-asttokens (2.0.5-2) ...
Setting up python3-charset-normalizer (2.0.6-3) ...
Setting up python3-unicodedata2 (14.0.0+ds2-1) ...
Setting up python3-threadpoolctl (3.1.0-1) ...
Setting up python3-all (3.10.6-1) ...
Setting up debhelper (13.10.1) ...
Setting up python3-matplotlib-inline (0.1.6-1) ...
Setting up python3-fs (2.4.16-2) ...
Setting up python3-pygments (2.12.0+dfsg-2) ...
Setting up python3-packaging (21.3-1.1) ...
Setting up python3-chardet (4.0.0-2) ...
Setting up python3-executing (0.8.0-1) ...
Setting up libpython3-all-dev:armhf (3.10.6-1) ...
Setting up python3-pexpect (4.8.0-3) ...
Setting up python3-dev (3.10.6-1) ...
Setting up python3-requests (2.27.1+dfsg-1) ...
Setting up python3-numpy (1:1.21.5-2) ...
Setting up python3-hdmedians (0.14.2-4+b1) ...
Setting up python3-cachecontrol (0.12.12-2) ...
Setting up python3-stack-data (0.5.0-1) ...
Setting up python3-all-dev (3.10.6-1) ...
Setting up python3-scipy (1.8.1-14+rpi1) ...
Setting up python3-jedi (0.18.0-1) ...
Setting up python3-pytest (7.1.2-4) ...
Setting up python3-pandas-lib:armhf (1.3.5+dfsg-6) ...
Setting up python3-sklearn-lib:armhf (1.1.2+dfsg-8) ...
Setting up python3-pandas (1.3.5+dfsg-6) ...
Setting up python3-ipython (8.5.0-1) ...
Setting up python3-sklearn (1.1.2+dfsg-8) ...
Setting up python3-fonttools (4.37.4-1) ...
Setting up python3-ufolib2 (0.13.1+dfsg1-1) ...
Processing triggers for sgml-base (1.31) ...
Setting up libfontconfig1:armhf (2.13.1-4.5) ...
Setting up libxft2:armhf (2.3.6-1) ...
Setting up libtk8.6:armhf (8.6.12-1) ...
Processing triggers for libc-bin (2.35-2+rpi1) ...
Setting up tk8.6-blt2.5 (2.5.3+dfsg-4.1) ...
Setting up blt (2.5.3+dfsg-4.1) ...
Setting up python3-tk:armhf (3.10.8-1) ...
Setting up python3-pil.imagetk:armhf (9.2.0-1.1) ...
Setting up python3-pil:armhf (9.2.0-1.1) ...
Setting up python3-matplotlib (3.5.2-4) ...
Setting up sbuild-build-depends-python-skbio-dummy (0.invalid.0) ...
Processing triggers for ca-certificates (20211016) ...
Updating certificates in /etc/ssl/certs...
0 added, 0 removed; done.
Running hooks in /etc/ca-certificates/update.d...
done.
Processing triggers for libc-bin (2.35-2+rpi1) ...
W: No sandbox user '_apt' on the system, can not drop privileges

+------------------------------------------------------------------------------+
| Build environment                                                            |
+------------------------------------------------------------------------------+

Kernel: Linux 4.9.0-0.bpo.6-armmp armhf (armv7l)
Toolchain package versions: binutils_2.39-6+rpi1 dpkg-dev_1.21.9+rpi1 g++-12_12.2.0-9+rpi1 gcc-12_12.2.0-9+rpi1 libc6-dev_2.35-2+rpi1 libstdc++-12-dev_12.2.0-9+rpi1 libstdc++6_12.2.0-9+rpi1 linux-libc-dev_5.19.6-1+rpi1
Package versions: adduser_3.129 apt_2.5.3 autoconf_2.71-2 automake_1:1.16.5-1.3 autopoint_0.21-9 autotools-dev_20220109.1 base-files_12.3+rpi1 base-passwd_3.6.1 bash_5.2~rc2-2 binutils_2.39-6+rpi1 binutils-arm-linux-gnueabihf_2.39-6+rpi1 binutils-common_2.39-6+rpi1 blt_2.5.3+dfsg-4.1 bsdextrautils_2.38.1-1.1 bsdutils_1:2.38.1-1.1 build-essential_12.9 bzip2_1.0.8-5+b2 ca-certificates_20211016 coreutils_9.1-1 cpp_4:12.2.0-1+rpi1 cpp-12_12.2.0-9+rpi1 cython3_0.29.32-1 dash_0.5.11+git20210903+057cd650a4ed-9 debconf_1.5.79 debhelper_13.10.1 debianutils_5.7-0.3 dh-autoreconf_20 dh-python_5.20220819+rpi1 dh-strip-nondeterminism_1.13.0-1 diffutils_1:3.8-1 dirmngr_2.2.39-1 dpkg_1.21.9+rpi1 dpkg-dev_1.21.9+rpi1 dwz_0.14+20220924-2 e2fsprogs_1.46.6~rc1-1 fakeroot_1.29-1 file_1:5.41-4 findutils_4.9.0-3 fontconfig-config_2.13.1-4.5 fonts-dejavu-core_2.37-2 fonts-lyx_2.3.6.1-1 g++_4:12.2.0-1+rpi1 g++-12_12.2.0-9+rpi1 gcc_4:12.2.0-1+rpi1 gcc-12_12.2.0-9+rpi1 gcc-12-base_12.2.0-9+rpi1 gcc-7-base_7.5.0-6+rpi1+b2 gcc-8-base_8.4.0-7+rpi1 gcc-9-base_9.4.0-2+rpi1 gettext_0.21-9 gettext-base_0.21-9 gnupg_2.2.39-1 gnupg-l10n_2.2.39-1 gnupg-utils_2.2.39-1 gpg_2.2.39-1 gpg-agent_2.2.39-1 gpg-wks-client_2.2.39-1 gpg-wks-server_2.2.39-1 gpgconf_2.2.39-1 gpgsm_2.2.39-1 gpgv_2.2.39-1 grep_3.7-1 groff-base_1.22.4-8 gzip_1.12-1 hostname_3.23 init-system-helpers_1.64 intltool-debian_0.35.0+20060710.6 iputils-ping_3:20211215-1 krb5-locales_1.20-1 libacl1_2.3.1-1 libapt-pkg6.0_2.5.3 libarchive-zip-perl_1.68-1 libasan8_12.2.0-9+rpi1 libassuan0_2.5.5-4 libatomic1_12.2.0-9+rpi1 libattr1_1:2.5.1-1 libaudit-common_1:3.0.7-1.1 libaudit1_1:3.0.7-1.1 libbinutils_2.39-6+rpi1 libblas3_3.10.1-2 libblkid1_2.38.1-1.1 libbrotli1_1.0.9-2+b2 libbsd0_0.11.7-1 libbz2-1.0_1.0.8-5+b2 libc-bin_2.35-2+rpi1 libc-dev-bin_2.35-2+rpi1 libc6_2.35-2+rpi1 libc6-dev_2.35-2+rpi1 libcap-ng0_0.8.3-1 libcap2_1:2.44-1 libcap2-bin_1:2.44-1 libcc1-0_12.2.0-9+rpi1 libcom-err2_1.46.6~rc1-1 libcrypt-dev_1:4.4.28-2 libcrypt1_1:4.4.28-2 libctf-nobfd0_2.39-6+rpi1 libctf0_2.39-6+rpi1 libdb5.3_5.3.28+dfsg1-0.10 libdebconfclient0_0.264 libdebhelper-perl_13.10.1 libdeflate0_1.14-1 libdpkg-perl_1.21.9+rpi1 libelf1_0.187-2+rpi2 libexpat1_2.5.0-1 libexpat1-dev_2.5.0-1 libext2fs2_1.46.6~rc1-1 libfakeroot_1.29-1 libffi8_3.4.2-4 libfile-stripnondeterminism-perl_1.13.0-1 libfontconfig1_2.13.1-4.5 libfreetype6_2.12.1+dfsg-3 libfribidi0_1.0.8-2.1 libgcc-12-dev_12.2.0-9+rpi1 libgcc-s1_12.2.0-9+rpi1 libgcrypt20_1.10.1-2+b2 libgdbm-compat4_1.23-3 libgdbm6_1.23-3 libgfortran5_12.2.0-9+rpi1 libglib2.0-0_2.74.1-2 libgmp10_2:6.2.1+dfsg1-1.1 libgnutls30_3.7.8-2 libgomp1_12.2.0-9+rpi1 libgpg-error0_1.45-2 libgraphite2-3_1.3.14-1 libgssapi-krb5-2_1.20-1 libharfbuzz0b_5.2.0-2+b1 libhogweed6_3.8.1-2 libicu72_72.1-2 libidn2-0_2.3.3-1 libimagequant0_2.17.0-1 libisl23_0.25-1 libjbig0_2.1-3.1+b2 libjpeg62-turbo_1:2.1.2-1 libjs-jquery_3.6.1+dfsg+~3.5.14-1 libjs-jquery-ui_1.13.2+dfsg-1 libjs-sphinxdoc_4.5.0-4 libjs-underscore_1.13.4~dfsg+~1.11.4-2 libjson-perl_4.10000-1 libk5crypto3_1.20-1 libkeyutils1_1.6.3-1 libkrb5-3_1.20-1 libkrb5support0_1.20-1 libksba8_1.6.0-3 liblapack3_3.10.1-2 liblbfgsb0_3.0+dfsg.3-11 liblcms2-2_2.13.1-1 libldap-2.5-0_2.5.13+dfsg-2+rpi1 liblerc4_4.0.0+ds-2 liblz4-1_1.9.4-1+rpi1 liblzma5_5.2.5-2.1 libmagic-mgc_1:5.41-4 libmagic1_1:5.41-4 libmd0_1.0.4-2 libmount1_2.38.1-1.1 libmpc3_1.2.1-2 libmpdec3_2.5.1-2+rpi1 libmpfr6_4.1.0-3 libncurses6_6.3+20220423-2 libncursesw6_6.3+20220423-2 libnettle8_3.8.1-2 libnpth0_1.6-3 libnsl-dev_1.3.0-2 libnsl2_1.3.0-2 libopenjp2-7_2.5.0-1 libp11-kit0_0.24.1-1 libpam-cap_1:2.44-1 libpam-modules_1.5.2-5 libpam-modules-bin_1.5.2-5 libpam-runtime_1.5.2-5 libpam0g_1.5.2-5 libpcre2-8-0_10.40-1+b2 libpcre3_2:8.39-14 libperl5.34_5.34.0-5 libperl5.36_5.36.0-4 libpipeline1_1.5.7-1 libpng16-16_1.6.38-2 libprocps8_2:3.3.17-7.1 libpython3-all-dev_3.10.6-1 libpython3-dev_3.10.6-1 libpython3-stdlib_3.10.6-1 libpython3.10_3.10.8-3 libpython3.10-dev_3.10.8-3 libpython3.10-minimal_3.10.8-3 libpython3.10-stdlib_3.10.8-3 libqhull-r8.0_2020.2-5 libraqm0_0.7.0-4 libreadline8_8.2-1 libsasl2-2_2.1.28+dfsg-8 libsasl2-modules-db_2.1.28+dfsg-8 libseccomp2_2.5.4-1+rpi1 libselinux1_3.4-1 libsemanage-common_3.4-1 libsemanage2_3.4-1 libsepol1_3.1-1 libsepol2_3.4-2 libsimde-dev_0.7.2-6 libsmartcols1_2.38.1-1.1 libsqlite3-0_3.39.4-1 libss2_1.46.6~rc1-1 libssl1.1_1.1.1o-1 libssl3_3.0.7-1 libssw-dev_1.1-13 libssw0_1.1-13 libstdc++-12-dev_12.2.0-9+rpi1 libstdc++6_12.2.0-9+rpi1 libsub-override-perl_0.09-4 libsystemd0_251.5-1+rpi1 libtasn1-6_4.19.0-2 libtcl8.6_8.6.12+dfsg-1 libtiff5_4.4.0-5+b1 libtinfo6_6.3+20220423-2 libtirpc-common_1.3.3+ds-1 libtirpc-dev_1.3.3+ds-1 libtirpc3_1.3.3+ds-1 libtk8.6_8.6.12-1 libtool_2.4.7-4 libubsan1_12.2.0-9+rpi1 libuchardet0_0.0.7-1 libudev1_251.5-1+rpi1 libunistring2_1.0-2 libuuid1_2.38.1-1.1 libwebp7_1.2.2-2+b1 libwebpdemux2_1.2.2-2+b1 libwebpmux3_1.2.2-2+b1 libx11-6_2:1.8.1-2 libx11-data_2:1.8.1-2 libxau6_1:1.0.9-1 libxcb1_1.15-1 libxdmcp6_1:1.1.2-3 libxext6_2:1.3.4-1 libxft2_2.3.6-1 libxml2_2.9.14+dfsg-1.1 libxrender1_1:0.9.10-1.1 libxslt1.1_1.1.35-1 libxss1_1:1.2.3-1 libxxhash0_0.8.1-1 libzstd1_1.5.2+dfsg-1 linux-libc-dev_5.19.6-1+rpi1 login_1:4.12.3+dfsg1-1 logsave_1.46.6~rc1-1 lsb-base_11.4+rpi1 m4_1.4.19-1 make_4.3-4.1 man-db_2.11.0-1 mawk_1.3.4.20200120-3.1 media-types_8.0.0 mount_2.38.1-1.1 nano_6.4-1 ncurses-base_6.3+20220423-2 ncurses-bin_6.3+20220423-2 netbase_6.3 openssl_3.0.7-1 passwd_1:4.12.3+dfsg1-1 patch_2.7.6-7 perl_5.36.0-4 perl-base_5.36.0-4 perl-modules-5.34_5.34.0-5 perl-modules-5.36_5.36.0-4 pinentry-curses_1.2.0-2 po-debconf_1.0.21+nmu1 procps_2:3.3.17-7.1 python-matplotlib-data_3.5.2-4 python3_3.10.6-1 python3-all_3.10.6-1 python3-all-dev_3.10.6-1 python3-appdirs_1.4.4-3 python3-asttokens_2.0.5-2 python3-attr_22.1.0-1 python3-backcall_0.2.0-3 python3-brotli_1.0.9-2+b2 python3-cachecontrol_0.12.12-2 python3-certifi_2022.6.15-2 python3-chardet_4.0.0-2 python3-charset-normalizer_2.0.6-3 python3-cycler_0.11.0-1 python3-dateutil_2.8.2-1 python3-decorator_5.1.1-2 python3-dev_3.10.6-1 python3-distutils_3.10.8-1 python3-executing_0.8.0-1 python3-fonttools_4.37.4-1 python3-fs_2.4.16-2 python3-hdmedians_0.14.2-4+b1 python3-idna_3.3-1 python3-iniconfig_1.1.1-2 python3-ipython_8.5.0-1 python3-jedi_0.18.0-1 python3-joblib_1.2.0-4 python3-kiwisolver_1.3.2-1+b1 python3-lib2to3_3.10.8-1 python3-lockfile_1:0.12.2-2.2 python3-lxml_4.9.1-1 python3-lz4_4.0.2+dfsg-1 python3-matplotlib_3.5.2-4 python3-matplotlib-inline_0.1.6-1 python3-minimal_3.10.6-1 python3-more-itertools_8.10.0-2 python3-mpmath_1.2.1-2 python3-msgpack_1.0.3-1 python3-natsort_8.0.2-2 python3-numpy_1:1.21.5-2 python3-packaging_21.3-1.1 python3-pandas_1.3.5+dfsg-6 python3-pandas-lib_1.3.5+dfsg-6 python3-parso_0.8.1-1 python3-pexpect_4.8.0-3 python3-pickleshare_0.7.5-5 python3-pil_9.2.0-1.1 python3-pil.imagetk_9.2.0-1.1 python3-pkg-resources_65.5.0-1 python3-pluggy_1.0.0+repack-1 python3-prompt-toolkit_3.0.32-1 python3-ptyprocess_0.7.0-3 python3-pure-eval_0.2.2-1 python3-py_1.11.0-1 python3-pygments_2.12.0+dfsg-2 python3-pyparsing_3.0.7-3 python3-pytest_7.1.2-4 python3-requests_2.27.1+dfsg-1 python3-scipy_1.8.1-14+rpi1 python3-setuptools_65.5.0-1 python3-six_1.16.0-4 python3-sklearn_1.1.2+dfsg-8 python3-sklearn-lib_1.1.2+dfsg-8 python3-stack-data_0.5.0-1 python3-sympy_1.11.1-1 python3-threadpoolctl_3.1.0-1 python3-tk_3.10.8-1 python3-tomli_2.0.1-1 python3-traitlets_5.5.0-1 python3-tz_2022.6-1 python3-ufolib2_0.13.1+dfsg1-1 python3-unicodedata2_14.0.0+ds2-1 python3-urllib3_1.26.12-1 python3-wcwidth_0.2.5+dfsg1-1 python3.10_3.10.8-3 python3.10-dev_3.10.8-3 python3.10-minimal_3.10.8-3 raspbian-archive-keyring_20120528.2 readline-common_8.2-1 rpcsvc-proto_1.4.2-4 sbuild-build-depends-core-dummy_0.invalid.0 sbuild-build-depends-python-skbio-dummy_0.invalid.0 sed_4.8-1 sensible-utils_0.0.17 sgml-base_1.31 sphinx-common_4.5.0-4 sysvinit-utils_3.05-6 tar_1.34+dfsg-1 tk8.6-blt2.5_2.5.3+dfsg-4.1 tzdata_2022d-1 ucf_3.0043 unicode-data_15.0.0-1 util-linux_2.38.1-1.1 util-linux-extra_2.38.1-1.1 x11-common_1:7.7+23 xz-utils_5.2.5-2.1 zlib1g_1:1.2.11.dfsg-4.1 zlib1g-dev_1:1.2.11.dfsg-4.1

+------------------------------------------------------------------------------+
| Build                                                                        |
+------------------------------------------------------------------------------+


Unpack source
-------------

gpgv: unknown type of key resource 'trustedkeys.kbx'
gpgv: keyblock resource '/tmp/dpkg-verify-sig.EoX6qxaw/trustedkeys.kbx': General error
gpgv: Signature made Fri Dec  3 15:33:22 2021 UTC
gpgv:                using RSA key 25E3FF2D7F469DBE7D0D4E50AFCFEC8E669CE1C2
gpgv: Can't check signature: No public key
dpkg-source: warning: cannot verify signature ./python-skbio_0.5.6-6.dsc
dpkg-source: info: extracting python-skbio in /<<PKGBUILDDIR>>
dpkg-source: info: unpacking python-skbio_0.5.6.orig.tar.gz
dpkg-source: info: unpacking python-skbio_0.5.6-6.debian.tar.xz
dpkg-source: info: using patch list from debian/patches/series
dpkg-source: info: applying mathjax-path
dpkg-source: info: applying 0002-use-libsww-as-library-not-embedded-src.patch
dpkg-source: info: applying no_privacy_breach_logo.patch
dpkg-source: info: applying simde
dpkg-source: info: applying soften_test
dpkg-source: info: applying pandas1.1-ordination-fix.patch
dpkg-source: info: applying pandas1.1-valueerror.patch
dpkg-source: info: applying local_inventory
dpkg-source: info: applying sphinx_add_javascript.patch
dpkg-source: info: applying fix-dtype-precision-warnings.patch

Check disc space
----------------

Sufficient free space for build

User Environment
----------------

APT_CONFIG=/var/lib/sbuild/apt.conf
DEB_BUILD_OPTIONS=parallel=4
HOME=/sbuild-nonexistent
LC_ALL=POSIX
LOGNAME=buildd
PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games
SCHROOT_ALIAS_NAME=bookworm-staging-armhf-sbuild
SCHROOT_CHROOT_NAME=bookworm-staging-armhf-sbuild
SCHROOT_COMMAND=env
SCHROOT_GID=109
SCHROOT_GROUP=buildd
SCHROOT_SESSION_ID=bookworm-staging-armhf-sbuild-6a182a09-8ebf-4caf-968c-de99c146af21
SCHROOT_UID=104
SCHROOT_USER=buildd
SHELL=/bin/sh
TERM=linux
USER=buildd

dpkg-buildpackage
-----------------

dpkg-buildpackage: info: source package python-skbio
dpkg-buildpackage: info: source version 0.5.6-6
dpkg-buildpackage: info: source distribution unstable
 dpkg-source --before-build .
dpkg-buildpackage: info: host architecture armhf
 debian/rules clean
dh clean --with python3,sphinxdoc --buildsystem=pybuild
   debian/rules override_dh_auto_clean
make[1]: Entering directory '/<<PKGBUILDDIR>>'
if [ "`find . -name "*.pyo"`" = "" ] ; then echo no need to clean up ; else dh_auto_clean; fi
no need to clean up
make[1]: Leaving directory '/<<PKGBUILDDIR>>'
   dh_autoreconf_clean -O--buildsystem=pybuild
   debian/rules override_dh_clean
make[1]: Entering directory '/<<PKGBUILDDIR>>'
dh_clean
find ./skbio -name '*.so' -delete
find ./skbio -name '*.o' -delete
rm -rf doc/build build doc/source/generated
find doc -name *.doctree -delete
rm -rf .pybuild
rm -f scikit_bio.egg-info/PKG-INFO scikit_bio.egg-info/SOURCES.txt scikit_bio.egg-info/dependency_links.txt scikit_bio.egg-info/requires.txt scikit_bio.egg-info/top_level.txt
rm -f skbio/alignment/_ssw_wrapper.c skbio/diversity/_phylogenetic.c skbio/stats/__subsample.c skbio/metadata/_intersection.c
make[1]: Leaving directory '/<<PKGBUILDDIR>>'
 debian/rules binary-arch
dh binary-arch --with python3,sphinxdoc --buildsystem=pybuild
   dh_update_autotools_config -a -O--buildsystem=pybuild
   dh_autoreconf -a -O--buildsystem=pybuild
   debian/rules override_dh_auto_configure
make[1]: Entering directory '/<<PKGBUILDDIR>>'
rm -f skbio/alignment/_ssw_wrapper.c skbio/diversity/_phylogenetic.c skbio/stats/__subsample.c skbio/metadata/_intersection.c
dh_auto_configure
I: pybuild base:240: python3.10 setup.py config 
/usr/lib/python3/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /<<PKGBUILDDIR>>/skbio/alignment/_ssw_wrapper.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)
/usr/lib/python3/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /<<PKGBUILDDIR>>/skbio/diversity/_phylogenetic.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)
/usr/lib/python3/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /<<PKGBUILDDIR>>/skbio/metadata/_intersection.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)
/usr/lib/python3/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /<<PKGBUILDDIR>>/skbio/stats/__subsample.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)
Compiling skbio/metadata/_intersection.pyx because it changed.
Compiling skbio/stats/__subsample.pyx because it changed.
Compiling skbio/alignment/_ssw_wrapper.pyx because it changed.
Compiling skbio/diversity/_phylogenetic.pyx because it changed.
[1/4] Cythonizing skbio/alignment/_ssw_wrapper.pyx
[2/4] Cythonizing skbio/diversity/_phylogenetic.pyx
[3/4] Cythonizing skbio/metadata/_intersection.pyx
[4/4] Cythonizing skbio/stats/__subsample.pyx
running config
make[1]: Leaving directory '/<<PKGBUILDDIR>>'
   debian/rules override_dh_auto_build-arch
make[1]: Entering directory '/<<PKGBUILDDIR>>'
dh_auto_build
I: pybuild base:240: /usr/bin/python3 setup.py build 
running build
running build_py
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/benchmarks
copying benchmarks/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/benchmarks
copying benchmarks/benchmarks.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/benchmarks
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio
copying skbio/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio
copying skbio/_base.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio
copying skbio/test.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio
copying skbio/workflow.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment
copying skbio/alignment/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment
copying skbio/alignment/_repr.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment
copying skbio/alignment/_indexing.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment
copying skbio/alignment/_pairwise.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment
copying skbio/alignment/_tabular_msa.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity
copying skbio/diversity/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity
copying skbio/diversity/_block.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity
copying skbio/diversity/_driver.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity
copying skbio/diversity/_util.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io
copying skbio/io/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io
copying skbio/io/_exception.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io
copying skbio/io/_fileobject.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io
copying skbio/io/_iosources.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io
copying skbio/io/_warning.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io
copying skbio/io/registry.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io
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copying skbio/stats/distance/tests/data/dm.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/dm2.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/dm3.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/dm4.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/dm_reordered.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/exp_results.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/exp_results_different_column_order.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/exp_results_single_column.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/mantel_env_dm_vegan.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/mantel_veg_dm_vegan.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/moving_pictures_dm.tsv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/moving_pictures_mf.tsv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/pwmantel_exp_results_all_dms.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/pwmantel_exp_results_dm_dm2.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/pwmantel_exp_results_duplicate_dms.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/pwmantel_exp_results_minimal.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/pwmantel_exp_results_minimal_with_labels.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/pwmantel_exp_results_na_p_value.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
copying skbio/stats/distance/tests/data/pwmantel_exp_results_reordered_distance_matrices.txt -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/data
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/L&L_CA_data -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_biplot_descriptors -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_biplot_projected_descriptors -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_sample_data -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_sample_data_12dim -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_sample_data_2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_sample_data_3 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_sample_data_3_eigh_ref_3dim -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_sample_data_3_fsvd_ref_3dim -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_skbio -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_X -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_Y -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_biplot_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_biplot_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_sample_constraints_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_sample_constraints_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_site_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_site_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_species_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_species_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_X -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_Y -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_biplot_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_biplot_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_sample_constraints_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_sample_constraints_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_site_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_site_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_species_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_species_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/exp_PCoAEigenResults_site -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/exp_PCoAzeros_site -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_biplot_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_constraints_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_eigvals_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_propexpl_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_sites_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_species_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/varechem.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/varespec.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/data
running build_ext
building 'skbio.metadata._intersection' extension
creating build
creating build/temp.linux-armhf-cpython-310
creating build/temp.linux-armhf-cpython-310/skbio
creating build/temp.linux-armhf-cpython-310/skbio/metadata
arm-linux-gnueabihf-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/lib/python3/dist-packages/numpy/core/include -I/usr/include/python3.10 -c skbio/metadata/_intersection.c -o build/temp.linux-armhf-cpython-310/skbio/metadata/_intersection.o -DSIMDE_ENABLE_OPENMP -fopenmp-simd -O3
arm-linux-gnueabihf-gcc -shared -Wl,-O1 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-cpython-310/skbio/metadata/_intersection.o -L/usr/lib/arm-linux-gnueabihf -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/metadata/_intersection.cpython-310-arm-linux-gnueabihf.so
building 'skbio.stats.__subsample' extension
creating build/temp.linux-armhf-cpython-310/skbio/stats
arm-linux-gnueabihf-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/lib/python3/dist-packages/numpy/core/include -I/usr/lib/python3/dist-packages/numpy/core/include -I/usr/include/python3.10 -c skbio/stats/__subsample.c -o build/temp.linux-armhf-cpython-310/skbio/stats/__subsample.o -DSIMDE_ENABLE_OPENMP -fopenmp-simd -O3
In file included from /usr/lib/python3/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1969,
                 from /usr/lib/python3/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,
                 from /usr/lib/python3/dist-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from skbio/stats/__subsample.c:773:
/usr/lib/python3/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
   17 | #warning "Using deprecated NumPy API, disable it with " \
      |  ^~~~~~~
arm-linux-gnueabihf-gcc -shared -Wl,-O1 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-cpython-310/skbio/stats/__subsample.o -L/usr/lib/arm-linux-gnueabihf -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/__subsample.cpython-310-arm-linux-gnueabihf.so
building 'skbio.alignment._ssw_wrapper' extension
creating build/temp.linux-armhf-cpython-310/skbio/alignment
arm-linux-gnueabihf-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -Iskbio/alignment -I/usr/lib/python3/dist-packages/numpy/core/include -I/usr/lib/python3/dist-packages/numpy/core/include -I/usr/include/python3.10 -c skbio/alignment/_ssw_wrapper.c -o build/temp.linux-armhf-cpython-310/skbio/alignment/_ssw_wrapper.o -DSIMDE_ENABLE_OPENMP -fopenmp-simd -O3
In file included from /usr/lib/python3/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1969,
                 from /usr/lib/python3/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,
                 from /usr/lib/python3/dist-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from skbio/alignment/_ssw_wrapper.c:779:
/usr/lib/python3/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
   17 | #warning "Using deprecated NumPy API, disable it with " \
      |  ^~~~~~~
arm-linux-gnueabihf-gcc -shared -Wl,-O1 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-cpython-310/skbio/alignment/_ssw_wrapper.o -L/usr/lib/arm-linux-gnueabihf -lssw -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment/_ssw_wrapper.cpython-310-arm-linux-gnueabihf.so
building 'skbio.diversity._phylogenetic' extension
creating build/temp.linux-armhf-cpython-310/skbio/diversity
arm-linux-gnueabihf-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/lib/python3/dist-packages/numpy/core/include -I/usr/lib/python3/dist-packages/numpy/core/include -I/usr/include/python3.10 -c skbio/diversity/_phylogenetic.c -o build/temp.linux-armhf-cpython-310/skbio/diversity/_phylogenetic.o -DSIMDE_ENABLE_OPENMP -fopenmp-simd -O3
In file included from /usr/lib/python3/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1969,
                 from /usr/lib/python3/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,
                 from /usr/lib/python3/dist-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from skbio/diversity/_phylogenetic.c:773:
/usr/lib/python3/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
   17 | #warning "Using deprecated NumPy API, disable it with " \
      |  ^~~~~~~
arm-linux-gnueabihf-gcc -shared -Wl,-O1 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -ffile-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-cpython-310/skbio/diversity/_phylogenetic.o -L/usr/lib/arm-linux-gnueabihf -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity/_phylogenetic.cpython-310-arm-linux-gnueabihf.so
make[1]: Leaving directory '/<<PKGBUILDDIR>>'
   dh_auto_test -a -O--buildsystem=pybuild
I: pybuild base:240: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build; python3.10 -m pytest 
============================= test session starts ==============================
platform linux -- Python 3.10.8, pytest-7.1.2, pluggy-1.0.0+repack
rootdir: /<<PKGBUILDDIR>>
collected 2352 items

skbio/alignment/tests/test_pairwise.py .........................         [  1%]
skbio/alignment/tests/test_ssw.py ...........................            [  2%]
skbio/alignment/tests/test_tabular_msa.py .............................. [  3%]
..............F......................................................... [  6%]
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............                                                             [ 19%]
skbio/diversity/alpha/tests/test_ace.py ..                               [ 19%]
skbio/diversity/alpha/tests/test_base.py ..............F.........        [ 20%]
skbio/diversity/alpha/tests/test_chao1.py ...                            [ 20%]
skbio/diversity/alpha/tests/test_faith_pd.py FFF.FFFF                    [ 20%]
skbio/diversity/alpha/tests/test_gini.py ...                             [ 21%]
skbio/diversity/alpha/tests/test_lladser.py ..FF.....F..                 [ 21%]
skbio/diversity/beta/tests/test_unifrac.py .FFFFFFF.FFFFFFFF.FFFFF.FFFF  [ 22%]
skbio/diversity/tests/test_block.py F..........                          [ 23%]
skbio/diversity/tests/test_driver.py FFFF.FFFFF....F...FFF.......F...FF  [ 24%]
skbio/diversity/tests/test_util.py .........F                            [ 25%]
skbio/io/format/tests/test_base.py ................................      [ 26%]
skbio/io/format/tests/test_blast6.py ...........                         [ 26%]
skbio/io/format/tests/test_blast7.py ..............                      [ 27%]
skbio/io/format/tests/test_clustal.py .............                      [ 28%]
skbio/io/format/tests/test_embl.py ...............................       [ 29%]
skbio/io/format/tests/test_emptyfile.py ..                               [ 29%]
skbio/io/format/tests/test_fasta.py .F..............                     [ 30%]
skbio/io/format/tests/test_fastq.py ...............                      [ 30%]
skbio/io/format/tests/test_genbank.py .....................              [ 31%]
skbio/io/format/tests/test_gff3.py ........................              [ 32%]
skbio/io/format/tests/test_lsmat.py .......                              [ 32%]
skbio/io/format/tests/test_newick.py .......                             [ 33%]
skbio/io/format/tests/test_ordination.py .....                           [ 33%]
skbio/io/format/tests/test_phylip.py .......                             [ 33%]
skbio/io/format/tests/test_qseq.py ......                                [ 34%]
skbio/io/format/tests/test_sequence_feature_vocabulary.py ....           [ 34%]
skbio/io/format/tests/test_stockholm.py ................................ [ 35%]
...........................................                              [ 37%]
skbio/io/tests/test_iosources.py ..........                              [ 37%]
skbio/io/tests/test_registry.py ........................................ [ 39%]
......................................................                   [ 41%]
skbio/io/tests/test_util.py .........................................sss [ 43%]
sssssssssssssssssssssssssss............................................. [ 46%]
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skbio/metadata/tests/test_intersection.py ...............                [ 51%]
skbio/metadata/tests/test_interval.py .................................. [ 53%]
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skbio/metadata/tests/test_mixin.py ..................................... [ 56%]
.......F.....................................................            [ 58%]
skbio/sequence/tests/test_distance.py ..........................         [ 60%]
skbio/sequence/tests/test_dna.py ....                                    [ 60%]
skbio/sequence/tests/test_genetic_code.py ..........................     [ 61%]
skbio/sequence/tests/test_grammared_sequence.py .....F.................. [ 62%]
...................                                                      [ 63%]
skbio/sequence/tests/test_nucleotide_sequences.py ...................... [ 64%]
.......                                                                  [ 64%]
skbio/sequence/tests/test_protein.py ..........                          [ 64%]
skbio/sequence/tests/test_rna.py ....                                    [ 65%]
skbio/sequence/tests/test_sequence.py .................................. [ 66%]
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....................F...................F...F..F.F.F...F.........F...... [ 72%]
....F........................................................F...F...... [ 75%]
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skbio/stats/distance/tests/test_anosim.py ....                           [ 76%]
skbio/stats/distance/tests/test_base.py ................................ [ 77%]
...............................................................          [ 80%]
skbio/stats/distance/tests/test_bioenv.py FFF.........FF...              [ 80%]
skbio/stats/distance/tests/test_mantel.py ................F.FFFFF.FFF... [ 82%]
.........                                                                [ 82%]
skbio/stats/distance/tests/test_permanova.py ....                        [ 82%]
skbio/stats/distance/tests/test_permdisp.py ............                 [ 83%]
skbio/stats/evolve/tests/test_hommola.py ............                    [ 83%]
skbio/stats/ordination/tests/test_canonical_correspondence_analysis.py . [ 83%]
.....                                                                    [ 83%]
skbio/stats/ordination/tests/test_correspondence_analysis.py ........    [ 84%]
skbio/stats/ordination/tests/test_ordination_results.py ................ [ 84%]
                                                                         [ 84%]
skbio/stats/ordination/tests/test_principal_coordinate_analysis.py ..... [ 85%]
......                                                                   [ 85%]
skbio/stats/ordination/tests/test_redundancy_analysis.py ....            [ 85%]
skbio/stats/ordination/tests/test_util.py .........                      [ 86%]
skbio/stats/tests/test_composition.py .................................. [ 87%]
........................                                                 [ 88%]
skbio/stats/tests/test_gradient.py ..........................            [ 89%]
skbio/stats/tests/test_misc.py ...                                       [ 89%]
skbio/stats/tests/test_power.py ........................................ [ 91%]
...                                                                      [ 91%]
skbio/stats/tests/test_subsample.py .F..F.F...FF                         [ 92%]
skbio/tests/test_base.py .....                                           [ 92%]
skbio/tests/test_workflow.py .................                           [ 92%]
skbio/tree/tests/test_majority_rule.py ......                            [ 93%]
skbio/tree/tests/test_nj.py ...........                                  [ 93%]
skbio/tree/tests/test_tree.py .......................................... [ 95%]
.................................................                        [ 97%]
skbio/util/tests/test_decorator.py .......................               [ 98%]
skbio/util/tests/test_misc.py ...................                        [ 99%]
skbio/util/tests/test_testing.py ...............                         [100%]

=================================== FAILURES ===================================
________ TestTabularMSAPositionalMetadata.test_eq_from_different_source ________

self = <skbio.alignment.tests.test_tabular_msa.TestTabularMSAPositionalMetadata testMethod=test_eq_from_different_source>

    def test_eq_from_different_source(self):
        obj1 = self._positional_metadata_constructor_(
            3, positional_metadata={'foo': np.array([1, 2, 3])})
        obj2 = self._positional_metadata_constructor_(
            3, positional_metadata=pd.DataFrame({'foo': [1, 2, 3]},
                                                index=['foo', 'bar', 'baz']))
>       self.assertReallyEqual(obj1, obj2)

skbio/metadata/_testing.py:464: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/util/_testing.py:31: in assertReallyEqual
    self.assertEqual(a, b)
E   AssertionError: Tabul[75 chars]: int32>
E   Stats:
E       sequence count: 1
E       posi[38 chars]-
E   AAA != Tabul[75 chars]: int64>
E   Stats:
E       sequence count: 1
E       posi[38 chars]-
E   AAA
____________________ TestILoc.test_fancy_empty_second_axis _____________________

self = <skbio.alignment.tests.test_tabular_msa.TestILoc testMethod=test_fancy_empty_second_axis>

    def test_fancy_empty_second_axis(self):
        a = DNA("ACGT", metadata={0: 0}, positional_metadata={0: [1, 2, 3, 4]})
        b = DNA("ACGT", metadata={1: 1}, positional_metadata={1: [1, 2, 3, 4]})
        c = DNA("ACGT", metadata={2: 2}, positional_metadata={2: [1, 2, 3, 4]})
    
        msa = TabularMSA([a, b, c], metadata={3: 3},
                         positional_metadata={3: [1, 2, 3, 4]})
    
>       self.assertEqual(self.get(msa, (Ellipsis, [])),
                         TabularMSA([a[0:0], b[0:0], c[0:0]],
                                    metadata={3: 3},
                                    positional_metadata={3: np.array(
                                        [], dtype=int)}))
E       AssertionError: Tabul[86 chars]: int64>
E       Stats:
E           sequence count: 3
E           posi[30 chars]----- != Tabul[86 chars]: int32>
E       Stats:
E           sequence count: 3
E           posi[30 chars]-----

skbio/alignment/tests/test_tabular_msa.py:1660: AssertionError
_____________________ BaseTests.test_michaelis_menten_fit ______________________

self = <skbio.diversity.alpha.tests.test_base.BaseTests testMethod=test_michaelis_menten_fit>

    def test_michaelis_menten_fit(self):
>       obs = michaelis_menten_fit([22])

skbio/diversity/alpha/tests/test_base.py:176: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_base.py:687: in michaelis_menten_fit
    ymtx[i] = np.asarray([observed_otus(subsample_counts(counts, n))
skbio/diversity/alpha/_base.py:687: in <listcomp>
    ymtx[i] = np.asarray([observed_otus(subsample_counts(counts, n))
skbio/stats/_subsample.py:246: in subsample_counts
    result = _subsample_counts_without_replacement(counts, n,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'

skbio/stats/__subsample.pyx:13: ValueError
__________________________ FaithPDTests.test_faith_pd __________________________

self = <skbio.diversity.alpha.tests.test_faith_pd.FaithPDTests testMethod=test_faith_pd>

    def test_faith_pd(self):
        # expected results derived from QIIME 1.9.1, which
        # is a completely different implementation skbio's initial
        # phylogenetic diversity implementation
>       actual = faith_pd(self.b1[0], self.oids1, self.t1)

skbio/diversity/alpha/tests/test_faith_pd.py:65: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
___________________ FaithPDTests.test_faith_pd_all_observed ____________________

self = <skbio.diversity.alpha.tests.test_faith_pd.FaithPDTests testMethod=test_faith_pd_all_observed>

    def test_faith_pd_all_observed(self):
>       actual = faith_pd([1, 1, 1, 1, 1], self.oids1, self.t1)

skbio/diversity/alpha/tests/test_faith_pd.py:51: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
____________________ FaithPDTests.test_faith_pd_extra_tips _____________________

self = <skbio.diversity.alpha.tests.test_faith_pd.FaithPDTests testMethod=test_faith_pd_extra_tips>

    def test_faith_pd_extra_tips(self):
        # results are the same despite presences of unobserved tips in tree
>       actual = faith_pd(self.b1[0], self.oids1, self.t1_w_extra_tips)

skbio/diversity/alpha/tests/test_faith_pd.py:80: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
______________________ FaithPDTests.test_faith_pd_minimal ______________________

self = <skbio.diversity.alpha.tests.test_faith_pd.FaithPDTests testMethod=test_faith_pd_minimal>

    def test_faith_pd_minimal(self):
        # two tips
        tree = TreeNode.read(StringIO('(OTU1:0.25, OTU2:0.25)root;'))
>       actual = faith_pd([1, 0], ['OTU1', 'OTU2'], tree)

skbio/diversity/alpha/tests/test_faith_pd.py:96: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
___________________ FaithPDTests.test_faith_pd_none_observed ___________________

self = <skbio.diversity.alpha.tests.test_faith_pd.FaithPDTests testMethod=test_faith_pd_none_observed>

    def test_faith_pd_none_observed(self):
>       actual = faith_pd(np.array([], dtype=int),
                          np.array([], dtype=int),
                          self.t1)

skbio/diversity/alpha/tests/test_faith_pd.py:41: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
__________________ FaithPDTests.test_faith_pd_qiime_tiny_test __________________

self = <skbio.diversity.alpha.tests.test_faith_pd.FaithPDTests testMethod=test_faith_pd_qiime_tiny_test>

    def test_faith_pd_qiime_tiny_test(self):
        # the following table and tree are derived from the QIIME 1.9.1
        # "tiny-test" data
        tt_table_fp = get_data_path(
            os.path.join('qiime-191-tt', 'otu-table.tsv'), 'data')
        tt_tree_fp = get_data_path(
            os.path.join('qiime-191-tt', 'tree.nwk'), 'data')
    
        self.q_table = pd.read_csv(tt_table_fp, sep='\t', skiprows=1,
                                   index_col=0)
        self.q_tree = TreeNode.read(tt_tree_fp)
    
        expected_fp = get_data_path(
            os.path.join('qiime-191-tt', 'faith-pd.txt'), 'data')
        expected = pd.read_csv(expected_fp, sep='\t', index_col=0)
        for sid in self.q_table.columns:
>           actual = faith_pd(self.q_table[sid],
                              otu_ids=self.q_table.index,
                              tree=self.q_tree)

skbio/diversity/alpha/tests/test_faith_pd.py:116: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________ FaithPDTests.test_faith_pd_root_not_observed _________________

self = <skbio.diversity.alpha.tests.test_faith_pd.FaithPDTests testMethod=test_faith_pd_root_not_observed>

    def test_faith_pd_root_not_observed(self):
        # expected values computed by hand
        tree = TreeNode.read(
            StringIO('((OTU1:0.1, OTU2:0.2):0.3, (OTU3:0.5, OTU4:0.7):1.1)'
                     'root;'))
        otu_ids = ['OTU%d' % i for i in range(1, 5)]
        # root node not observed, but branch between (OTU1, OTU2) and root
        # is considered observed
>       actual = faith_pd([1, 1, 0, 0], otu_ids, tree)

skbio/diversity/alpha/tests/test_faith_pd.py:129: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________________ LladserTests.test_lladser_ci _________________________

self = <skbio.diversity.alpha.tests.test_lladser.LladserTests testMethod=test_lladser_ci>

    def test_lladser_ci(self):
        """lladser_ci estimate using defaults contains p with 95% prob"""
        np.random.seed(12345678)
        reps = 100
        sum = 0
        for i in range(reps):
>           fake_obs, exp_p = create_fake_observation()

skbio/diversity/alpha/tests/test_lladser.py:69: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/tests/test_lladser.py:31: in create_fake_observation
    fake_obs = subsample_counts(counts, 1000)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

counts = array([9000,    1,    1, ...,    1,    1,    1], dtype=int64), n = 1000
replace = False

    @experimental(as_of="0.4.0")
    def subsample_counts(counts, n, replace=False):
        """Randomly subsample from a vector of counts, with or without replacement.
    
        Parameters
        ----------
        counts : 1-D array_like
            Vector of counts (integers) to randomly subsample from.
        n : int
            Number of items to subsample from `counts`. Must be less than or equal
            to the sum of `counts`.
        replace : bool, optional
            If ``True``, subsample with replacement. If ``False`` (the default),
            subsample without replacement.
    
        Returns
        -------
        subsampled : ndarray
            Subsampled vector of counts where the sum of the elements equals `n`
            (i.e., ``subsampled.sum() == n``). Will have the same shape as
            `counts`.
    
        Raises
        ------
        TypeError
            If `counts` cannot be safely converted to an integer datatype.
        ValueError
            If `n` is less than zero or greater than the sum of `counts`
            when `replace=False`.
        EfficiencyWarning
            If the accelerated code isn't present or hasn't been compiled.
    
        See Also
        --------
        isubsample
        skbio.diversity.alpha
    
        Notes
        -----
        If subsampling is performed without replacement (``replace=False``), a copy
        of `counts` is returned if `n` is equal to the number of items in `counts`,
        as all items will be chosen from the original vector.
    
        If subsampling is performed with replacement (``replace=True``) and `n` is
        equal to the number of items in `counts`, the subsampled vector that is
        returned may not necessarily be the same vector as `counts`.
    
        Examples
        --------
        Subsample 4 items (without replacement) from a vector of counts:
    
        >>> import numpy as np
        >>> from skbio.stats import subsample_counts
        >>> a = np.array([4, 5, 0, 2, 1])
        >>> sub = subsample_counts(a, 4)
        >>> sub.sum()
        4
        >>> sub.shape
        (5,)
    
        Trying to subsample an equal number of items (without replacement) results
        in the same vector as our input:
    
        >>> subsample_counts([0, 3, 0, 1], 4)
        array([0, 3, 0, 1])
    
        Subsample 5 items (with replacement):
    
        >>> sub = subsample_counts([1, 0, 1, 2, 2, 3, 0, 1], 5, replace=True)
        >>> sub.sum()
        5
        >>> sub.shape
        (8,)
    
        """
        if n < 0:
            raise ValueError("n cannot be negative.")
    
        counts = np.asarray(counts)
>       counts = counts.astype(int, casting='safe')
E       TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'

skbio/stats/_subsample.py:229: TypeError
_______________________ LladserTests.test_lladser_ci_f3 ________________________

self = <skbio.diversity.alpha.tests.test_lladser.LladserTests testMethod=test_lladser_ci_f3>

    def test_lladser_ci_f3(self):
        """lladser_ci estimate using f=3 contains p with 95% prob"""
    
        # Test different values of f=3 and r=14, which lie exactly on the
        # 95% interval line. For 100 reps using simple cumulative binomial
        # probs we expect to have more than 5 misses of the interval in 38%
        # of all test runs. To make this test pass reliable we thus have to
        # set a defined seed
        np.random.seed(12345678)
        reps = 100
        sum = 0
        for i in range(reps):
            # re-create the obs for every estimate, such that they are truly
            # independent events
>           fake_obs, exp_p = create_fake_observation()

skbio/diversity/alpha/tests/test_lladser.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/tests/test_lladser.py:31: in create_fake_observation
    fake_obs = subsample_counts(counts, 1000)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

counts = array([9000,    1,    1, ...,    1,    1,    1], dtype=int64), n = 1000
replace = False

    @experimental(as_of="0.4.0")
    def subsample_counts(counts, n, replace=False):
        """Randomly subsample from a vector of counts, with or without replacement.
    
        Parameters
        ----------
        counts : 1-D array_like
            Vector of counts (integers) to randomly subsample from.
        n : int
            Number of items to subsample from `counts`. Must be less than or equal
            to the sum of `counts`.
        replace : bool, optional
            If ``True``, subsample with replacement. If ``False`` (the default),
            subsample without replacement.
    
        Returns
        -------
        subsampled : ndarray
            Subsampled vector of counts where the sum of the elements equals `n`
            (i.e., ``subsampled.sum() == n``). Will have the same shape as
            `counts`.
    
        Raises
        ------
        TypeError
            If `counts` cannot be safely converted to an integer datatype.
        ValueError
            If `n` is less than zero or greater than the sum of `counts`
            when `replace=False`.
        EfficiencyWarning
            If the accelerated code isn't present or hasn't been compiled.
    
        See Also
        --------
        isubsample
        skbio.diversity.alpha
    
        Notes
        -----
        If subsampling is performed without replacement (``replace=False``), a copy
        of `counts` is returned if `n` is equal to the number of items in `counts`,
        as all items will be chosen from the original vector.
    
        If subsampling is performed with replacement (``replace=True``) and `n` is
        equal to the number of items in `counts`, the subsampled vector that is
        returned may not necessarily be the same vector as `counts`.
    
        Examples
        --------
        Subsample 4 items (without replacement) from a vector of counts:
    
        >>> import numpy as np
        >>> from skbio.stats import subsample_counts
        >>> a = np.array([4, 5, 0, 2, 1])
        >>> sub = subsample_counts(a, 4)
        >>> sub.sum()
        4
        >>> sub.shape
        (5,)
    
        Trying to subsample an equal number of items (without replacement) results
        in the same vector as our input:
    
        >>> subsample_counts([0, 3, 0, 1], 4)
        array([0, 3, 0, 1])
    
        Subsample 5 items (with replacement):
    
        >>> sub = subsample_counts([1, 0, 1, 2, 2, 3, 0, 1], 5, replace=True)
        >>> sub.sum()
        5
        >>> sub.shape
        (8,)
    
        """
        if n < 0:
            raise ValueError("n cannot be negative.")
    
        counts = np.asarray(counts)
>       counts = counts.astype(int, casting='safe')
E       TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'

skbio/stats/_subsample.py:229: TypeError
_________________________ LladserTests.test_lladser_pe _________________________

self = <skbio.diversity.alpha.tests.test_lladser.LladserTests testMethod=test_lladser_pe>

    def test_lladser_pe(self):
        """lladser_pe returns point estimates within the expected variance"""
    
        obs = lladser_pe([3], r=4)
        self.assertTrue(np.isnan(obs))
    
        np.random.seed(123456789)
>       fake_obs, exp_p = create_fake_observation()

skbio/diversity/alpha/tests/test_lladser.py:45: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/tests/test_lladser.py:31: in create_fake_observation
    fake_obs = subsample_counts(counts, 1000)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

counts = array([9000,    1,    1, ...,    1,    1,    1], dtype=int64), n = 1000
replace = False

    @experimental(as_of="0.4.0")
    def subsample_counts(counts, n, replace=False):
        """Randomly subsample from a vector of counts, with or without replacement.
    
        Parameters
        ----------
        counts : 1-D array_like
            Vector of counts (integers) to randomly subsample from.
        n : int
            Number of items to subsample from `counts`. Must be less than or equal
            to the sum of `counts`.
        replace : bool, optional
            If ``True``, subsample with replacement. If ``False`` (the default),
            subsample without replacement.
    
        Returns
        -------
        subsampled : ndarray
            Subsampled vector of counts where the sum of the elements equals `n`
            (i.e., ``subsampled.sum() == n``). Will have the same shape as
            `counts`.
    
        Raises
        ------
        TypeError
            If `counts` cannot be safely converted to an integer datatype.
        ValueError
            If `n` is less than zero or greater than the sum of `counts`
            when `replace=False`.
        EfficiencyWarning
            If the accelerated code isn't present or hasn't been compiled.
    
        See Also
        --------
        isubsample
        skbio.diversity.alpha
    
        Notes
        -----
        If subsampling is performed without replacement (``replace=False``), a copy
        of `counts` is returned if `n` is equal to the number of items in `counts`,
        as all items will be chosen from the original vector.
    
        If subsampling is performed with replacement (``replace=True``) and `n` is
        equal to the number of items in `counts`, the subsampled vector that is
        returned may not necessarily be the same vector as `counts`.
    
        Examples
        --------
        Subsample 4 items (without replacement) from a vector of counts:
    
        >>> import numpy as np
        >>> from skbio.stats import subsample_counts
        >>> a = np.array([4, 5, 0, 2, 1])
        >>> sub = subsample_counts(a, 4)
        >>> sub.sum()
        4
        >>> sub.shape
        (5,)
    
        Trying to subsample an equal number of items (without replacement) results
        in the same vector as our input:
    
        >>> subsample_counts([0, 3, 0, 1], 4)
        array([0, 3, 0, 1])
    
        Subsample 5 items (with replacement):
    
        >>> sub = subsample_counts([1, 0, 1, 2, 2, 3, 0, 1], 5, replace=True)
        >>> sub.sum()
        5
        >>> sub.shape
        (8,)
    
        """
        if n < 0:
            raise ValueError("n cannot be negative.")
    
        counts = np.asarray(counts)
>       counts = counts.astype(int, casting='safe')
E       TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'

skbio/stats/_subsample.py:229: TypeError
___________________ UnifracTests.test_unweighted_extra_tips ____________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_extra_tips>

    def test_unweighted_extra_tips(self):
        # UniFrac values are the same despite unobserved tips in the tree
        for i in range(len(self.b1)):
            for j in range(len(self.b1)):
>               actual = unweighted_unifrac(
                    self.b1[i], self.b1[j], self.oids1, self.t1_w_extra_tips)

skbio/diversity/beta/tests/test_unifrac.py:85: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
__________________ UnifracTests.test_unweighted_minimal_trees __________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_minimal_trees>

    def test_unweighted_minimal_trees(self):
        # two tips
        tree = TreeNode.read(StringIO('(OTU1:0.25, OTU2:0.25)root;'))
>       actual = unweighted_unifrac([1, 0], [0, 0], ['OTU1', 'OTU2'],
                                    tree)

skbio/diversity/beta/tests/test_unifrac.py:104: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________ UnifracTests.test_unweighted_otus_out_of_order ________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_otus_out_of_order>

    def test_unweighted_otus_out_of_order(self):
        # UniFrac API does not assert the observations are in tip order of the
        # input tree
        shuffled_ids = self.oids1[:]
        shuffled_b1 = self.b1.copy()
    
        shuffled_ids[0], shuffled_ids[-1] = shuffled_ids[-1], shuffled_ids[0]
        shuffled_b1[:, [0, -1]] = shuffled_b1[:, [-1, 0]]
    
        for i in range(len(self.b1)):
            for j in range(len(self.b1)):
>               actual = unweighted_unifrac(
                    self.b1[i], self.b1[j], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________ UnifracTests.test_unweighted_root_not_observed ________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_root_not_observed>

    def test_unweighted_root_not_observed(self):
        # expected values computed with QIIME 1.9.1 and by hand
        # root node not observed, but branch between (OTU1, OTU2) and root
        # is considered shared
>       actual = unweighted_unifrac([1, 1, 0, 0], [1, 0, 0, 0],
                                    self.oids2, self.t2)

skbio/diversity/beta/tests/test_unifrac.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_____________________ UnifracTests.test_unweighted_unifrac _____________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_unifrac>

    def test_unweighted_unifrac(self):
        # expected results derived from QIIME 1.9.1, which
        # is a completely different implementation skbio's initial
        # unweighted unifrac implementation
        # sample A versus all
>       actual = unweighted_unifrac(
            self.b1[0], self.b1[1], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:338: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________ UnifracTests.test_unweighted_unifrac_identity _________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_unifrac_identity>

    def test_unweighted_unifrac_identity(self):
        for i in range(len(self.b1)):
>           actual = unweighted_unifrac(
                self.b1[i], self.b1[i], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:174: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_____________ UnifracTests.test_unweighted_unifrac_non_overlapping _____________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_unifrac_non_overlapping>

    def test_unweighted_unifrac_non_overlapping(self):
        # these communities only share the root node
>       actual = unweighted_unifrac(
            self.b1[4], self.b1[5], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:310: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________ UnifracTests.test_unweighted_unifrac_symmetry _________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_unifrac_symmetry>

    def test_unweighted_unifrac_symmetry(self):
        for i in range(len(self.b1)):
            for j in range(len(self.b1)):
>               actual = unweighted_unifrac(
                    self.b1[i], self.b1[j], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:182: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_______________ UnifracTests.test_unweighted_unifrac_zero_counts _______________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_unweighted_unifrac_zero_counts>

    def test_unweighted_unifrac_zero_counts(self):
>       actual = unweighted_unifrac(
            [1, 1, 1, 0, 0], [0, 0, 0, 0, 0], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:320: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:146: in unweighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
____________________ UnifracTests.test_weighted_extra_tips _____________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_extra_tips>

    def test_weighted_extra_tips(self):
        # UniFrac values are the same despite unobserved tips in the tree
        for i in range(len(self.b1)):
            for j in range(len(self.b1)):
>               actual = weighted_unifrac(
                    self.b1[i], self.b1[j], self.oids1, self.t1_w_extra_tips)

skbio/diversity/beta/tests/test_unifrac.py:95: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
___________________ UnifracTests.test_weighted_minimal_trees ___________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_minimal_trees>

    def test_weighted_minimal_trees(self):
        # two tips
        tree = TreeNode.read(StringIO('(OTU1:0.25, OTU2:0.25)root;'))
>       actual = weighted_unifrac([1, 0], [0, 0], ['OTU1', 'OTU2'], tree)

skbio/diversity/beta/tests/test_unifrac.py:112: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
___________ UnifracTests.test_weighted_normalized_root_not_observed ____________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_normalized_root_not_observed>

    def test_weighted_normalized_root_not_observed(self):
        # expected values computed by hand, these disagree with QIIME 1.9.1
        # root node not observed, but branch between (OTU1, OTU2) and root
        # is considered shared
>       actual = weighted_unifrac([1, 0, 0, 0], [1, 1, 0, 0],
                                  self.oids2, self.t2, normalized=True)

skbio/diversity/beta/tests/test_unifrac.py:160: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________ UnifracTests.test_weighted_otus_out_of_order _________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_otus_out_of_order>

    def test_weighted_otus_out_of_order(self):
        # UniFrac API does not assert the observations are in tip order of the
        # input tree
        shuffled_ids = self.oids1[:]
        shuffled_b1 = self.b1.copy()
    
        shuffled_ids[0], shuffled_ids[-1] = shuffled_ids[-1], shuffled_ids[0]
        shuffled_b1[:, [0, -1]] = shuffled_b1[:, [-1, 0]]
    
        for i in range(len(self.b1)):
            for j in range(len(self.b1)):
>               actual = weighted_unifrac(
                    self.b1[i], self.b1[j], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:75: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________ UnifracTests.test_weighted_root_not_observed _________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_root_not_observed>

    def test_weighted_root_not_observed(self):
        # expected values computed by hand, these disagree with QIIME 1.9.1
        # root node not observed, but branch between (OTU1, OTU2) and root
        # is considered shared
>       actual = weighted_unifrac([1, 0, 0, 0], [1, 1, 0, 0],
                                  self.oids2, self.t2)

skbio/diversity/beta/tests/test_unifrac.py:144: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
______________________ UnifracTests.test_weighted_unifrac ______________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac>

    def test_weighted_unifrac(self):
        # expected results derived from QIIME 1.9.1, which
        # is a completely different implementation skbio's initial
        # weighted unifrac implementation
>       actual = weighted_unifrac(
            self.b1[0], self.b1[1], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:450: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________ UnifracTests.test_weighted_unifrac_identity __________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_identity>

    def test_weighted_unifrac_identity(self):
        for i in range(len(self.b1)):
>           actual = weighted_unifrac(
                self.b1[i], self.b1[i], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:405: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
____________ UnifracTests.test_weighted_unifrac_identity_normalized ____________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_identity_normalized>

    def test_weighted_unifrac_identity_normalized(self):
        for i in range(len(self.b1)):
>           actual = weighted_unifrac(
                self.b1[i], self.b1[i], self.oids1, self.t1, normalized=True)

skbio/diversity/beta/tests/test_unifrac.py:517: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
______________ UnifracTests.test_weighted_unifrac_non_overlapping ______________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_non_overlapping>

    def test_weighted_unifrac_non_overlapping(self):
        # expected results derived from QIIME 1.9.1, which
        # is a completely different implementation skbio's initial
        # weighted unifrac implementation
        # these communities only share the root node
>       actual = weighted_unifrac(
            self.b1[4], self.b1[5], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:424: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________ UnifracTests.test_weighted_unifrac_non_overlapping_normalized _________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_non_overlapping_normalized>

    def test_weighted_unifrac_non_overlapping_normalized(self):
        # these communities only share the root node
>       actual = weighted_unifrac(
            self.b1[4], self.b1[5], self.oids1, self.t1, normalized=True)

skbio/diversity/beta/tests/test_unifrac.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________ UnifracTests.test_weighted_unifrac_normalized _________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_normalized>

    def test_weighted_unifrac_normalized(self):
        # expected results derived from QIIME 1.9.1, which
        # is a completely different implementation skbio's initial
        # weighted unifrac implementation
>       actual = weighted_unifrac(
            self.b1[0], self.b1[1], self.oids1, self.t1, normalized=True)

skbio/diversity/beta/tests/test_unifrac.py:568: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________ UnifracTests.test_weighted_unifrac_symmetry __________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_symmetry>

    def test_weighted_unifrac_symmetry(self):
        for i in range(len(self.b1)):
            for j in range(len(self.b1)):
>               actual = weighted_unifrac(
                    self.b1[i], self.b1[j], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:413: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
____________ UnifracTests.test_weighted_unifrac_symmetry_normalized ____________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_symmetry_normalized>

    def test_weighted_unifrac_symmetry_normalized(self):
        for i in range(len(self.b1)):
            for j in range(len(self.b1)):
>               actual = weighted_unifrac(
                    self.b1[i], self.b1[j], self.oids1, self.t1,
                    normalized=True)

skbio/diversity/beta/tests/test_unifrac.py:525: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________ UnifracTests.test_weighted_unifrac_zero_counts ________________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_zero_counts>

    def test_weighted_unifrac_zero_counts(self):
>       actual = weighted_unifrac(
            [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], self.oids1, self.t1)

skbio/diversity/beta/tests/test_unifrac.py:430: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
__________ UnifracTests.test_weighted_unifrac_zero_counts_normalized ___________

self = <skbio.diversity.beta.tests.test_unifrac.UnifracTests testMethod=test_weighted_unifrac_zero_counts_normalized>

    def test_weighted_unifrac_zero_counts_normalized(self):
        # expected results derived from QIIME 1.9.1, which
        # is a completely different implementation skbio's initial
        # weighted unifrac implementation
>       actual = weighted_unifrac(
            [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], self.oids1, self.t1,
            normalized=True)

skbio/diversity/beta/tests/test_unifrac.py:549: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/beta/_unifrac.py:280: in weighted_unifrac
    _setup_pairwise_unifrac(u_counts, v_counts, otu_ids, tree, validate,
skbio/diversity/beta/_unifrac.py:315: in _setup_pairwise_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_______________ ParallelBetaDiversity.test_block_beta_diversity ________________

self = <skbio.diversity.tests.test_block.ParallelBetaDiversity testMethod=test_block_beta_diversity>

    def test_block_beta_diversity(self):
>       exp = beta_diversity('unweighted_unifrac', self.table1, self.sids1,
                             tree=self.tree1, otu_ids=self.oids1)

skbio/diversity/tests/test_block.py:118: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:354: in beta_diversity
    metric, counts_by_node = _setup_multiple_unweighted_unifrac(
skbio/diversity/beta/_unifrac.py:485: in _setup_multiple_unweighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________________ AlphaDiversityTests.test_empty ________________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_empty>

    def test_empty(self):
        # empty vector
        actual = alpha_diversity('observed_otus', np.array([], dtype=np.int64))
        expected = pd.Series([0])
>       assert_series_almost_equal(actual, expected)

skbio/diversity/tests/test_driver.py:160: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left = 0    0
dtype: int32, right = 0    0
dtype: int64

    def assert_series_almost_equal(left, right):
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_series_equal`'s defaults change
>       pdt.assert_series_equal(left, right,
                                check_dtype=True,
                                check_index_type=True,
                                check_series_type=True,
                                check_names=True,
                                check_exact=False,
                                check_datetimelike_compat=False,
                                obj='Series')
E       AssertionError: Attributes of Series are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:322: AssertionError
______________________ AlphaDiversityTests.test_faith_pd _______________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_faith_pd>

    def test_faith_pd(self):
        # calling faith_pd through alpha_diversity gives same results as
        # calling it directly
        expected = []
        for e in self.table1:
>           expected.append(faith_pd(e, tree=self.tree1, otu_ids=self.oids1))

skbio/diversity/tests/test_driver.py:235: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/alpha/_faith_pd.py:122: in faith_pd
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_____________________ AlphaDiversityTests.test_input_types _____________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_input_types>

    def test_input_types(self):
        list_result = alpha_diversity('observed_otus', [1, 3, 0, 1, 0])
        array_result = alpha_diversity('observed_otus',
                                       np.array([1, 3, 0, 1, 0]))
        self.assertAlmostEqual(list_result[0], 3)
        assert_series_almost_equal(list_result, array_result)
    
>       list_result = alpha_diversity('faith_pd', [1, 3, 0, 1, 0],
                                      tree=self.tree1, otu_ids=self.oids1)

skbio/diversity/tests/test_driver.py:210: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:169: in alpha_diversity
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
____________________ AlphaDiversityTests.test_invalid_input ____________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_invalid_input>

    def test_invalid_input(self):
        # number of ids doesn't match the number of samples
        self.assertRaises(ValueError, alpha_diversity, 'observed_otus',
                          self.table1, list('ABC'))
    
        # unknown metric provided
        self.assertRaises(ValueError, alpha_diversity, 'not-a-metric',
                          self.table1)
    
        # 3-D list provided as input
        self.assertRaises(ValueError, alpha_diversity, 'observed_otus',
                          [[[43]]])
    
        # negative counts
        self.assertRaises(ValueError, alpha_diversity, 'observed_otus',
                          [0, 3, -12, 42])
    
        # additional kwargs
        self.assertRaises(TypeError, alpha_diversity, 'observed_otus',
                          [0, 1], not_a_real_kwarg=42.0)
>       self.assertRaises(TypeError, alpha_diversity, 'faith_pd',
                          [0, 1], tree=self.tree1, otu_ids=['OTU1', 'OTU2'],
                          not_a_real_kwarg=42.0)

skbio/diversity/tests/test_driver.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:169: in alpha_diversity
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_______________________ AlphaDiversityTests.test_no_ids ________________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_no_ids>

    def test_no_ids(self):
        # expected values hand-calculated
        expected = pd.Series([3, 3, 3, 3])
        actual = alpha_diversity('observed_otus', self.table1)
>       assert_series_almost_equal(actual, expected)

skbio/diversity/tests/test_driver.py:254: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left = 0    3
1    3
2    3
3    3
dtype: int32
right = 0    3
1    3
2    3
3    3
dtype: int64

    def assert_series_almost_equal(left, right):
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_series_equal`'s defaults change
>       pdt.assert_series_equal(left, right,
                                check_dtype=True,
                                check_index_type=True,
                                check_series_type=True,
                                check_names=True,
                                check_exact=False,
                                check_datetimelike_compat=False,
                                obj='Series')
E       AssertionError: Attributes of Series are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:322: AssertionError
____________________ AlphaDiversityTests.test_observed_otus ____________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_observed_otus>

    def test_observed_otus(self):
        # expected values hand-calculated
        expected = pd.Series([3, 3, 3, 3], index=self.sids1)
        actual = alpha_diversity('observed_otus', self.table1, self.sids1)
>       assert_series_almost_equal(actual, expected)

skbio/diversity/tests/test_driver.py:221: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left = A    3
B    3
C    3
D    3
dtype: int32
right = A    3
B    3
C    3
D    3
dtype: int64

    def assert_series_almost_equal(left, right):
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_series_equal`'s defaults change
>       pdt.assert_series_equal(left, right,
                                check_dtype=True,
                                check_index_type=True,
                                check_series_type=True,
                                check_names=True,
                                check_exact=False,
                                check_datetimelike_compat=False,
                                obj='Series')
E       AssertionError: Attributes of Series are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:322: AssertionError
______________________ AlphaDiversityTests.test_optimized ______________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_optimized>

    def test_optimized(self):
        # calling optimized faith_pd gives same results as calling unoptimized
        # version
>       optimized = alpha_diversity('faith_pd', self.table1, tree=self.tree1,
                                    otu_ids=self.oids1)

skbio/diversity/tests/test_driver.py:259: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:169: in alpha_diversity
    counts_by_node, branch_lengths = _setup_faith_pd(
skbio/diversity/alpha/_faith_pd.py:139: in _setup_faith_pd
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________ AlphaDiversityTests.test_single_count_vector _________________

self = <skbio.diversity.tests.test_driver.AlphaDiversityTests testMethod=test_single_count_vector>

    def test_single_count_vector(self):
        actual = alpha_diversity('observed_otus', np.array([1, 0, 2]))
        expected = pd.Series([2])
>       assert_series_almost_equal(actual, expected)

skbio/diversity/tests/test_driver.py:197: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left = 0    2
dtype: int32, right = 0    2
dtype: int64

    def assert_series_almost_equal(left, right):
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_series_equal`'s defaults change
>       pdt.assert_series_equal(left, right,
                                check_dtype=True,
                                check_index_type=True,
                                check_series_type=True,
                                check_names=True,
                                check_exact=False,
                                check_datetimelike_compat=False,
                                obj='Series')
E       AssertionError: Attributes of Series are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:322: AssertionError
__________________ BetaDiversityTests.test_alt_pairwise_func ___________________

self = <skbio.diversity.tests.test_driver.BetaDiversityTests testMethod=test_alt_pairwise_func>

    def test_alt_pairwise_func(self):
        # confirm that pairwise_func is actually being used
        def not_a_real_pdist(counts, metric):
            return [[0.0, 42.0], [42.0, 0.0]]
>       dm1 = beta_diversity('unweighted_unifrac', self.table1,
                             otu_ids=self.oids1, tree=self.tree1,
                             pairwise_func=not_a_real_pdist)

skbio/diversity/tests/test_driver.py:601: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:354: in beta_diversity
    metric, counts_by_node = _setup_multiple_unweighted_unifrac(
skbio/diversity/beta/_unifrac.py:485: in _setup_multiple_unweighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
____________________ BetaDiversityTests.test_invalid_input _____________________

self = <skbio.diversity.tests.test_driver.BetaDiversityTests testMethod=test_invalid_input>

    def test_invalid_input(self):
        # number of ids doesn't match the number of samples
        error_msg = (r"Number of rows")
        with self.assertRaisesRegex(ValueError, error_msg):
            beta_diversity(self.table1, list('AB'), 'euclidean')
    
        # unknown metric provided
        error_msg = r"not-a-metric"
        with self.assertRaisesRegex(ValueError, error_msg):
            beta_diversity('not-a-metric', self.table1)
    
        # 3-D list provided as input
        error_msg = (r"Only 1-D and 2-D")
        with self.assertRaisesRegex(ValueError, error_msg):
            beta_diversity('euclidean', [[[43]]])
    
        # negative counts
        error_msg = r"negative values."
        with self.assertRaisesRegex(ValueError, error_msg):
            beta_diversity('euclidean', [[0, 1, 3, 4], [0, 3, -12, 42]])
        with self.assertRaisesRegex(ValueError, error_msg):
            beta_diversity('euclidean', [[0, 1, 3, -4], [0, 3, 12, 42]])
    
        # additional kwargs
        error_msg = r"keyword argument"
        with self.assertRaisesRegex(TypeError, error_msg):
            beta_diversity('euclidean', [[0, 1, 3], [0, 3, 12]],
                           not_a_real_kwarg=42.0)
        with self.assertRaisesRegex(TypeError, error_msg):
>           beta_diversity('unweighted_unifrac', [[0, 1, 3], [0, 3, 12]],
                           not_a_real_kwarg=42.0, tree=self.tree1,
                           otu_ids=['O1', 'O2', 'O3'])

skbio/diversity/tests/test_driver.py:323: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:354: in beta_diversity
    metric, counts_by_node = _setup_multiple_unweighted_unifrac(
skbio/diversity/beta/_unifrac.py:485: in _setup_multiple_unweighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
__________________ BetaDiversityTests.test_unweighted_unifrac __________________

self = <skbio.diversity.tests.test_driver.BetaDiversityTests testMethod=test_unweighted_unifrac>

    def test_unweighted_unifrac(self):
        # TODO: update npt.assert_almost_equal calls to use DistanceMatrix
        # near-equality testing when that support is available
        # expected values calculated by hand
>       dm1 = beta_diversity('unweighted_unifrac', self.table1, self.sids1,
                             otu_ids=self.oids1, tree=self.tree1)

skbio/diversity/tests/test_driver.py:528: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:354: in beta_diversity
    metric, counts_by_node = _setup_multiple_unweighted_unifrac(
skbio/diversity/beta/_unifrac.py:485: in _setup_multiple_unweighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
___________________ BetaDiversityTests.test_weighted_unifrac ___________________

self = <skbio.diversity.tests.test_driver.BetaDiversityTests testMethod=test_weighted_unifrac>

    def test_weighted_unifrac(self):
        # TODO: update npt.assert_almost_equal calls to use DistanceMatrix
        # near-equality testing when that support is available
        # expected values calculated by hand
>       dm1 = beta_diversity('weighted_unifrac', self.table1, self.sids1,
                             otu_ids=self.oids1, tree=self.tree1)

skbio/diversity/tests/test_driver.py:547: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:363: in beta_diversity
    metric, counts_by_node = _setup_multiple_weighted_unifrac(
skbio/diversity/beta/_unifrac.py:521: in _setup_multiple_weighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_____________ BetaDiversityTests.test_weighted_unifrac_normalized ______________

self = <skbio.diversity.tests.test_driver.BetaDiversityTests testMethod=test_weighted_unifrac_normalized>

    def test_weighted_unifrac_normalized(self):
        # TODO: update npt.assert_almost_equal calls to use DistanceMatrix
        # near-equality testing when that support is available
        # expected values calculated by hand
>       dm1 = beta_diversity('weighted_unifrac', self.table1, self.sids1,
                             otu_ids=self.oids1, tree=self.tree1,
                             normalized=True)

skbio/diversity/tests/test_driver.py:567: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_driver.py:363: in beta_diversity
    metric, counts_by_node = _setup_multiple_weighted_unifrac(
skbio/diversity/beta/_unifrac.py:521: in _setup_multiple_weighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
______________ TestPartialBetaDiversity.test_id_pairs_as_iterable ______________

self = <skbio.diversity.tests.test_driver.TestPartialBetaDiversity testMethod=test_id_pairs_as_iterable>

    def test_id_pairs_as_iterable(self):
        id_pairs = iter([('B', 'C'), ])
>       dm = partial_beta_diversity('unweighted_unifrac', self.table1,
                                    self.sids1, otu_ids=self.oids1,
                                    tree=self.tree1, id_pairs=id_pairs)

skbio/diversity/tests/test_driver.py:670: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3/dist-packages/decorator.py:232: in fun
    return caller(func, *(extras + args), **kw)
skbio/util/_decorator.py:260: in wrapped_f
    return func(*args[1:], **kwargs)
skbio/diversity/_driver.py:250: in partial_beta_diversity
    metric, counts_by_node = _setup_multiple_unweighted_unifrac(
skbio/diversity/beta/_unifrac.py:485: in _setup_multiple_unweighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
___________ TestPartialBetaDiversity.test_unweighted_unifrac_partial ___________

self = <skbio.diversity.tests.test_driver.TestPartialBetaDiversity testMethod=test_unweighted_unifrac_partial>

    def test_unweighted_unifrac_partial(self):
        # TODO: update npt.assert_almost_equal calls to use DistanceMatrix
        # near-equality testing when that support is available
        # expected values calculated by hand
>       dm = partial_beta_diversity('unweighted_unifrac', self.table1,
                                    self.sids1, otu_ids=self.oids1,
                                    tree=self.tree1, id_pairs=[('B', 'C'), ])

skbio/diversity/tests/test_driver.py:689: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3/dist-packages/decorator.py:232: in fun
    return caller(func, *(extras + args), **kw)
skbio/util/_decorator.py:260: in wrapped_f
    return func(*args[1:], **kwargs)
skbio/diversity/_driver.py:250: in partial_beta_diversity
    metric, counts_by_node = _setup_multiple_unweighted_unifrac(
skbio/diversity/beta/_unifrac.py:485: in _setup_multiple_unweighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________ TestPartialBetaDiversity.test_weighted_unifrac_partial_full __________

self = <skbio.diversity.tests.test_driver.TestPartialBetaDiversity testMethod=test_weighted_unifrac_partial_full>

    def test_weighted_unifrac_partial_full(self):
        # TODO: update npt.assert_almost_equal calls to use DistanceMatrix
        # near-equality testing when that support is available
        # expected values calculated by hand
>       dm1 = partial_beta_diversity('weighted_unifrac', self.table1,
                                     self.sids1, otu_ids=self.oids1,
                                     tree=self.tree1, id_pairs=[('A', 'B'),
                                                                ('A', 'C'),
                                                                ('B', 'C')])

skbio/diversity/tests/test_driver.py:706: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3/dist-packages/decorator.py:232: in fun
    return caller(func, *(extras + args), **kw)
skbio/util/_decorator.py:260: in wrapped_f
    return func(*args[1:], **kwargs)
skbio/diversity/_driver.py:259: in partial_beta_diversity
    metric, counts_by_node = _setup_multiple_weighted_unifrac(
skbio/diversity/beta/_unifrac.py:521: in _setup_multiple_weighted_unifrac
    _setup_multiple_unifrac(counts, otu_ids, tree, validate)
skbio/diversity/beta/_unifrac.py:452: in _setup_multiple_unifrac
    _vectorize_counts_and_tree(counts, otu_ids, tree)
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
________________ ValidationTests.test_vectorize_counts_and_tree ________________

self = <skbio.diversity.tests.test_util.ValidationTests testMethod=test_vectorize_counts_and_tree>

    def test_vectorize_counts_and_tree(self):
        t = TreeNode.read(io.StringIO("((a:1, b:2)c:3)root;"))
        counts = np.array([[0, 1], [1, 5], [10, 1]])
        count_array, indexed, branch_lengths = \
>           _vectorize_counts_and_tree(counts, np.array(['a', 'b']), t)

skbio/diversity/tests/test_util.py:249: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/diversity/_util.py:130: in _vectorize_counts_and_tree
    counts_by_node = _nodes_by_counts(counts, otu_ids, tree_index)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'int'

skbio/diversity/_phylogenetic.pyx:184: ValueError
_________________________ SnifferTests.test_positives __________________________

self = <skbio.io.format.tests.test_fasta.SnifferTests testMethod=test_positives>

    def test_positives(self):
        for fp in self.positive_fps:
>           self.assertEqual(_fasta_sniffer(fp), (True, {}))
E           AssertionError: Tuples differ: (False, {}) != (True, {})
E           
E           First differing element 0:
E           False
E           True
E           
E           - (False, {})
E           + (True, {})

skbio/io/format/tests/test_fasta.py:153: AssertionError
__________ TestPositionalMetadataMixin.test_eq_from_different_source ___________

self = <skbio.metadata.tests.test_mixin.TestPositionalMetadataMixin testMethod=test_eq_from_different_source>

    def test_eq_from_different_source(self):
        obj1 = self._positional_metadata_constructor_(
            3, positional_metadata={'foo': np.array([1, 2, 3])})
        obj2 = self._positional_metadata_constructor_(
            3, positional_metadata=pd.DataFrame({'foo': [1, 2, 3]},
                                                index=['foo', 'bar', 'baz']))
>       self.assertReallyEqual(obj1, obj2)

skbio/metadata/_testing.py:464: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/util/_testing.py:31: in assertReallyEqual
    self.assertEqual(a, b)
E   AssertionError: <skbi[60 chars]p.<locals>.ExamplePositionalMetadataMixin object at 0xac910988> != <skbi[60 chars]p.<locals>.ExamplePositionalMetadataMixin object at 0xac910850>
_______________________ TestGrammaredSequence.test_degap _______________________

self = <skbio.sequence.tests.test_grammared_sequence.TestGrammaredSequence testMethod=test_degap>

    def test_degap(self):
        kw = {
            'metadata': {
                'id': 'some_id',
                'description': 'some description',
            },
        }
    
        self.assertEqual(
            ExampleGrammaredSequence(
                "", positional_metadata={'qual': []}, **kw).degap(),
            ExampleGrammaredSequence(
                "", positional_metadata={'qual': []}, **kw))
    
        self.assertEqual(
            ExampleGrammaredSequence(
                "ABCXYZ",
                positional_metadata={'qual': np.arange(6)},
                **kw).degap(),
            ExampleGrammaredSequence(
                "ABCXYZ",
                positional_metadata={'qual': np.arange(6)},
                **kw))
    
>       self.assertEqual(
            ExampleGrammaredSequence(
                "ABC-XYZ",
                positional_metadata={'qual': np.arange(7)},
                **kw).degap(),
            ExampleGrammaredSequence(
                "ABCXYZ",
                positional_metadata={'qual': [0, 1, 2, 4, 5, 6]},
                **kw))
E       AssertionError: Examp[165 chars]: int32>
E       Stats:
E           length: 6
E           has gaps: Fa[95 chars]BCXYZ != Examp[165 chars]: int64>
E       Stats:
E           length: 6
E           has gaps: Fa[95 chars]BCXYZ

skbio/sequence/tests/test_grammared_sequence.py:525: AssertionError
_________ TestSequencePositionalMetadata.test_eq_from_different_source _________

self = <skbio.sequence.tests.test_sequence.TestSequencePositionalMetadata testMethod=test_eq_from_different_source>

    def test_eq_from_different_source(self):
        obj1 = self._positional_metadata_constructor_(
            3, positional_metadata={'foo': np.array([1, 2, 3])})
        obj2 = self._positional_metadata_constructor_(
            3, positional_metadata=pd.DataFrame({'foo': [1, 2, 3]},
                                                index=['foo', 'bar', 'baz']))
>       self.assertReallyEqual(obj1, obj2)

skbio/metadata/_testing.py:464: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/util/_testing.py:31: in assertReallyEqual
    self.assertEqual(a, b)
E   AssertionError: Seque[66 chars]pe: int32>
E   Stats:
E       length: 3
E   -------------------------
E   0 ZZZ != Seque[66 chars]pe: int64>
E   Stats:
E       length: 3
E   -------------------------
E   0 ZZZ
_____ TestSequence.test_eq_sequences_from_different_sources_compare_equal ______

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_eq_sequences_from_different_sources_compare_equal>

    def test_eq_sequences_from_different_sources_compare_equal(self):
        # sequences that have the same data but are constructed from different
        # types of data should compare equal
        im = IntervalMetadata(4)
        im.add([(0, 2)], metadata={'gene': 'sagB'})
        seq1 = Sequence('ACGT', metadata={'id': 'foo', 'desc': 'abc'},
                        positional_metadata={'quality': (1, 2, 3, 4)},
                        interval_metadata=im)
        seq2 = Sequence(np.array([65, 67, 71, 84], dtype=np.uint8),
                        metadata={'id': 'foo', 'desc': 'abc'},
                        positional_metadata={'quality': np.array([1, 2, 3,
                                                                  4])},
                        interval_metadata=im)
>       self.assertTrue(seq1 == seq2)
E       AssertionError: False is not true

skbio/sequence/tests/test_sequence.py:622: AssertionError
____________ TestSequence.test_getitem_with_boolean_vector_has_qual ____________

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_getitem_with_boolean_vector_has_qual>

    def test_getitem_with_boolean_vector_has_qual(self):
        s = "0123456789abcdef"
        length = len(s)
        seq = Sequence(s, metadata={'id': 'id11', 'description': 'dsc11'},
                       positional_metadata={'quality': np.arange(length)})
    
        eseq = Sequence("13579bdf",
                        metadata={'id': 'id11', 'description': 'dsc11'},
                        positional_metadata={'quality': [1, 3, 5, 7, 9, 11,
                                                         13, 15]})
    
>       self.assertEqual(seq[np.array([False, True] * 8)], eseq)
E       AssertionError: Seque[130 chars]: int32>
E       Stats:
E           length: 8
E       ----------------[19 chars]79bdf != Seque[130 chars]: int64>
E       Stats:
E           length: 8
E       ----------------[19 chars]79bdf

skbio/sequence/tests/test_sequence.py:932: AssertionError
__________ TestSequence.test_getitem_with_int_has_positional_metadata __________

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_getitem_with_int_has_positional_metadata>

    def test_getitem_with_int_has_positional_metadata(self):
        s = "Sequence string !1@2#3?.,"
        length = len(s)
        seq = Sequence(s, metadata={'id': 'id', 'description': 'dsc'},
                       positional_metadata={'quality': np.arange(length)})
    
        eseq = Sequence("S", {'id': 'id', 'description': 'dsc'},
                        positional_metadata={'quality': np.array([0])})
        self.assertEqual(seq[0], eseq)
    
        eseq = Sequence(",", metadata={'id': 'id', 'description': 'dsc'},
                        positional_metadata={'quality':
                                             np.array([len(seq) - 1])})
        self.assertEqual(seq[len(seq) - 1], eseq)
    
        eseq = Sequence("t", metadata={'id': 'id', 'description': 'dsc'},
                        positional_metadata={'quality': [10]})
>       self.assertEqual(seq[10], eseq)
E       AssertionError: Seque[126 chars]: int32>
E       Stats:
E           length: 1
E       -----------------------------
E       0 t != Seque[126 chars]: int64>
E       Stats:
E           length: 1
E       -----------------------------
E       0 t

skbio/sequence/tests/test_sequence.py:689: AssertionError
___ TestSequence.test_getitem_with_iterable_of_mixed_has_positional_metadata ___

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_getitem_with_iterable_of_mixed_has_positional_metadata>

    def test_getitem_with_iterable_of_mixed_has_positional_metadata(self):
        s = "0123456789abcdef"
        length = len(s)
        seq = Sequence(s, metadata={'id': 'id7', 'description': 'dsc7'},
                       positional_metadata={'quality': np.arange(length)})
    
        def generator():
            yield slice(0, 4)
            yield slice(200, 400)
            yield -1
            yield slice(-2, -4, -1)
            yield 9
    
        eseq = Sequence("0123fed9",
                        metadata={'id': 'id7', 'description': 'dsc7'},
                        positional_metadata={'quality': [0, 1, 2, 3, 15, 14,
                                                         13, 9]})
>       self.assertEqual(seq[[0, 1, 2, 3, 15, 14, 13, 9]], eseq)
E       AssertionError: Seque[128 chars]: int32>
E       Stats:
E           length: 8
E       ----------------[19 chars]3fed9 != Seque[128 chars]: int64>
E       Stats:
E           length: 8
E       ----------------[19 chars]3fed9

skbio/sequence/tests/test_sequence.py:853: AssertionError
______ TestSequence.test_getitem_with_numpy_index_has_positional_metadata ______

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_getitem_with_numpy_index_has_positional_metadata>

    def test_getitem_with_numpy_index_has_positional_metadata(self):
        s = "0123456789abcdef"
        length = len(s)
        seq = Sequence(s, metadata={'id': 'id9', 'description': 'dsc9'},
                       positional_metadata={'quality': np.arange(length)})
    
        eseq = Sequence("0123fed9",
                        metadata={'id': 'id9', 'description': 'dsc9'},
                        positional_metadata={'quality': [0, 1, 2, 3, 15, 14,
                                                         13, 9]})
>       self.assertEqual(seq[np.array([0, 1, 2, 3, 15, 14, 13, 9])], eseq)
E       AssertionError: Seque[128 chars]: int32>
E       Stats:
E           length: 8
E       ----------------[19 chars]3fed9 != Seque[128 chars]: int64>
E       Stats:
E           length: 8
E       ----------------[19 chars]3fed9

skbio/sequence/tests/test_sequence.py:887: AssertionError
_________ TestSequence.test_getitem_with_slice_has_positional_metadata _________

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_getitem_with_slice_has_positional_metadata>

    def test_getitem_with_slice_has_positional_metadata(self):
        s = "0123456789abcdef"
        length = len(s)
        seq = Sequence(s, metadata={'id': 'id3', 'description': 'dsc3'},
                       positional_metadata={'quality': np.arange(length)})
    
        eseq = Sequence("012", metadata={'id': 'id3', 'description': 'dsc3'},
                        positional_metadata={'quality': np.arange(3)})
        self.assertEqual(seq[0:3], eseq)
        self.assertEqual(seq[:3], eseq)
        self.assertEqual(seq[:3:1], eseq)
    
        eseq = Sequence("def", metadata={'id': 'id3', 'description': 'dsc3'},
                        positional_metadata={'quality': [13, 14, 15]})
>       self.assertEqual(seq[-3:], eseq)
E       AssertionError: Seque[128 chars]: int32>
E       Stats:
E           length: 3
E       ----------------[14 chars]0 def != Seque[128 chars]: int64>
E       Stats:
E           length: 3
E       ----------------[14 chars]0 def

skbio/sequence/tests/test_sequence.py:747: AssertionError
____ TestSequence.test_getitem_with_tuple_of_mixed_with_positional_metadata ____

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_getitem_with_tuple_of_mixed_with_positional_metadata>

    def test_getitem_with_tuple_of_mixed_with_positional_metadata(self):
        s = "0123456789abcdef"
        length = len(s)
        seq = Sequence(s, metadata={'id': 'id5', 'description': 'dsc5'},
                       positional_metadata={'quality': np.arange(length)})
    
        eseq = Sequence("00000", metadata={'id': 'id5', 'description': 'dsc5'},
                        positional_metadata={'quality': [0, 0, 0, 0, 0]})
>       self.assertEqual(seq[0, 0, 0, 0, 0], eseq)
E       AssertionError: Seque[128 chars]: int32>
E       Stats:
E           length: 5
E       ----------------[16 chars]00000 != Seque[128 chars]: int64>
E       Stats:
E           length: 5
E       ----------------[16 chars]00000

skbio/sequence/tests/test_sequence.py:803: AssertionError
___________________ TestSequence.test_init_invalid_sequence ____________________
TypeError: Can cannot create a sequence with 'int32'

During handling of the above exception, another exception occurred:

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_init_invalid_sequence>

    def test_init_invalid_sequence(self):
        # invalid dtype (numpy.ndarray input)
        with self.assertRaises(TypeError):
            # int64
            Sequence(np.array([1, 2, 3]))
        with self.assertRaises(TypeError):
            # |S21
            Sequence(np.array([1, "23", 3]))
        with self.assertRaises(TypeError):
            # object
            Sequence(np.array([1, {}, ()]))
    
        # invalid input type (non-numpy.ndarray input)
        with self.assertRaisesRegex(TypeError, r'tuple'):
            Sequence(('a', 'b', 'c'))
        with self.assertRaisesRegex(TypeError, r'list'):
            Sequence(['a', 'b', 'c'])
        with self.assertRaisesRegex(TypeError, r'set'):
            Sequence({'a', 'b', 'c'})
        with self.assertRaisesRegex(TypeError, r'dict'):
            Sequence({'a': 42, 'b': 43, 'c': 44})
        with self.assertRaisesRegex(TypeError, r'int'):
            Sequence(42)
        with self.assertRaisesRegex(TypeError, r'float'):
            Sequence(4.2)
>       with self.assertRaisesRegex(TypeError, r'int64'):
E       AssertionError: "int64" does not match "Can cannot create a sequence with 'int32'"

skbio/sequence/tests/test_sequence.py:476: AssertionError
________________ TestSequence.test_iter_has_positional_metadata ________________

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_iter_has_positional_metadata>

    def test_iter_has_positional_metadata(self):
        tested = False
        seq = Sequence("0123456789", metadata={'id': 'a', 'desc': 'b'},
                       positional_metadata={'qual': np.arange(10)})
        for i, s in enumerate(seq):
            tested = True
>           self.assertEqual(s, Sequence(str(i),
                                         metadata={'id': 'a', 'desc': 'b'},
                                         positional_metadata={'qual': [i]}))
E           AssertionError: Seque[107 chars]ype: int32>
E           Stats:
E               length: 1
E           --------------------------
E           0 0 != Seque[107 chars]ype: int64>
E           Stats:
E               length: 1
E           --------------------------
E           0 0

skbio/sequence/tests/test_sequence.py:1036: AssertionError
______________ TestSequence.test_reversed_has_positional_metadata ______________

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_reversed_has_positional_metadata>

    def test_reversed_has_positional_metadata(self):
        tested = False
        seq = Sequence("0123456789", metadata={'id': 'a', 'desc': 'b'},
                       positional_metadata={'qual': np.arange(10)})
        for i, s in enumerate(reversed(seq)):
            tested = True
>           self.assertEqual(s, Sequence(str(9 - i),
                                         metadata={'id': 'a', 'desc': 'b'},
                                         positional_metadata={'qual':
                                                              [9 - i]}))
E           AssertionError: Seque[107 chars]ype: int32>
E           Stats:
E               length: 1
E           --------------------------
E           0 9 != Seque[107 chars]ype: int64>
E           Stats:
E               length: 1
E           --------------------------
E           0 9

skbio/sequence/tests/test_sequence.py:1056: AssertionError
_________________ TestSequence.test_slice_positional_metadata __________________

self = <skbio.sequence.tests.test_sequence.TestSequence testMethod=test_slice_positional_metadata>

    def test_slice_positional_metadata(self):
        seq = Sequence('ABCDEFGHIJ',
                       positional_metadata={'foo': np.arange(10),
                                            'bar': np.arange(100, 110)})
>       self.assertTrue(pd.DataFrame({'foo': [0], 'bar': [100]}).equals(
                        seq._slice_positional_metadata(0)))
E       AssertionError: False is not true

skbio/sequence/tests/test_sequence.py:715: AssertionError
_________________ BIOENVTests.test_bioenv_all_columns_explicit _________________

self = <skbio.stats.distance.tests.test_bioenv.BIOENVTests testMethod=test_bioenv_all_columns_explicit>

    def test_bioenv_all_columns_explicit(self):
        # Test with all columns being specified.
        obs = bioenv(self.dm, self.df, columns=self.cols)
>       assert_data_frame_almost_equal(obs, self.exp_results)

skbio/stats/distance/tests/test_bioenv.py:102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =                                                     size  correlation
vars                                            ...LAY, ELEVATION, SOIL_MOISTU...    10     0.607143
TOT_ORG_CARB, SILT_CLAY, ELEVATION, SOIL_MOISTU...    11     0.539286
right =                                                     size  correlation
vars                                            ...LAY, ELEVATION, SOIL_MOISTU...    10     0.607143
TOT_ORG_CARB, SILT_CLAY, ELEVATION, SOIL_MOISTU...    11     0.539286
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="size") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
_________________ BIOENVTests.test_bioenv_all_columns_implicit _________________

self = <skbio.stats.distance.tests.test_bioenv.BIOENVTests testMethod=test_bioenv_all_columns_implicit>

    def test_bioenv_all_columns_implicit(self):
        # Test with all columns in data frame (implicitly).
        obs = bioenv(self.dm, self.df)
>       assert_data_frame_almost_equal(obs, self.exp_results)

skbio/stats/distance/tests/test_bioenv.py:92: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =                                                     size  correlation
vars                                            ...LAY, ELEVATION, SOIL_MOISTU...    10     0.607143
TOT_ORG_CARB, SILT_CLAY, ELEVATION, SOIL_MOISTU...    11     0.539286
right =                                                     size  correlation
vars                                            ...LAY, ELEVATION, SOIL_MOISTU...    10     0.607143
TOT_ORG_CARB, SILT_CLAY, ELEVATION, SOIL_MOISTU...    11     0.539286
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="size") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
________________ BIOENVTests.test_bioenv_different_column_order ________________

self = <skbio.stats.distance.tests.test_bioenv.BIOENVTests testMethod=test_bioenv_different_column_order>

    def test_bioenv_different_column_order(self):
        # Specifying columns in a different order will change the row labels in
        # the results data frame as the column subsets will be reordered, but
        # the actual results (e.g., correlation coefficients) shouldn't change.
        obs = bioenv(self.dm, self.df, columns=self.cols[::-1])
>       assert_data_frame_almost_equal(
            obs,
            self.exp_results_different_column_order)

skbio/stats/distance/tests/test_bioenv.py:119: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =                                                     size  correlation
vars                                            ... PH, ANNUAL_SEASON_PRECPT, ...    10     0.607143
LATITUDE, LONGITUDE, CMIN_RATE, PH, ANNUAL_SEAS...    11     0.539286
right =                                                     size  correlation
vars                                            ... PH, ANNUAL_SEASON_PRECPT, ...    10     0.607143
LATITUDE, LONGITUDE, CMIN_RATE, PH, ANNUAL_SEAS...    11     0.539286
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="size") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
____________________ BIOENVTests.test_bioenv_single_column _____________________

self = <skbio.stats.distance.tests.test_bioenv.BIOENVTests testMethod=test_bioenv_single_column>

    def test_bioenv_single_column(self):
        obs = bioenv(self.dm, self.df, columns=['PH'])
>       assert_data_frame_almost_equal(obs, self.exp_results_single_column)

skbio/stats/distance/tests/test_bioenv.py:112: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =       size  correlation
vars                   
PH       1         0.75
right =       size  correlation
vars                   
PH       1         0.75
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="size") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
____________________ BIOENVTests.test_bioenv_vegan_example _____________________

self = <skbio.stats.distance.tests.test_bioenv.BIOENVTests testMethod=test_bioenv_vegan_example>

    def test_bioenv_vegan_example(self):
        # The correlation coefficient in the first row of the
        # results (rho=0.2516) is different from the correlation coefficient
        # computed by vegan (rho=0.2513). This seems to occur due to
        # differences in numerical precision when calculating the Euclidean
        # distances, which affects the rank calculations in Spearman
        # (specifically, dealing with ties). The ranked distances end up being
        # slightly different between vegan and our implementation because some
        # distances are treated as ties in vegan but treated as distinct values
        # in our implementation. This explains the difference in rho values. I
        # verified that using Pearson correlation instead of Spearman on the
        # same distances yields *very* similar results. Thus, the discrepancy
        # seems to stem from differences when computing ranks/ties.
        obs = bioenv(self.dm_vegan, self.df_vegan)
>       assert_data_frame_almost_equal(obs, self.exp_results_vegan, rtol=1e-3)

skbio/stats/distance/tests/test_bioenv.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =                           size  correlation
vars                                       
P                            1...l                4     0.361875
log(N), P, Ca, pH, Al        5     0.321552
log(N), P, K, Ca, pH, Al     6     0.282181
right =                           size  correlation
vars                                       
P                            1...l                4     0.361875
log(N), P, Ca, pH, Al        5     0.321552
log(N), P, K, Ca, pH, Al     6     0.282181
rtol = 0.001

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="size") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
____________________ PairwiseMantelTests.test_duplicate_dms ____________________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_duplicate_dms>

    def test_duplicate_dms(self):
        obs = pwmantel((self.minx_dm, self.minx_dm, self.minx_dm),
                       alternative='less')
>       assert_data_frame_almost_equal(obs, self.exp_results_duplicate_dms)

skbio/stats/distance/tests/test_mantel.py:376: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...   1.0      1.0  3  pearson           999        less
1   2          1.0      1.0  3  pearson           999        less
right =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...   1.0      1.0  3  pearson           999        less
1   2          1.0      1.0  3  pearson           999        less
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
_________________ PairwiseMantelTests.test_filepaths_as_input __________________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_filepaths_as_input>

    def test_filepaths_as_input(self):
        dms = [
            get_data_path('dm.txt'),
            get_data_path('dm2.txt'),
        ]
        np.random.seed(0)
    
        obs = pwmantel(dms)
>       assert_data_frame_almost_equal(obs, self.exp_results_dm_dm2)

skbio/stats/distance/tests/test_mantel.py:465: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                                          
0   1     0.702031    0.001  6  pearson           999   two-sided
right =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                                          
0   1     0.702031    0.001  6  pearson           999   two-sided
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
______________________ PairwiseMantelTests.test_id_lookup ______________________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_id_lookup>

    def test_id_lookup(self):
        # Matrices have mismatched IDs but a lookup is provided.
        self.minx_dm_extra.ids = ['a', 'b', 'c', 'foo']
        self.minz_dm_extra.ids = ['d', 'e', 'f', 'bar']
        lookup = {'a': '0', 'b': '1', 'c': '2', 'foo': 'foo',
                  'd': '0', 'e': '1', 'f': '2', 'bar': 'bar',
                  '0': '0', '1': '1', '2': '2'}
    
        x = self.minx_dm_extra.filter(['b', 'a', 'foo', 'c'])
        y = self.miny_dm.filter(['0', '2', '1'])
        z = self.minz_dm_extra.filter(['bar', 'e', 'f', 'd'])
    
        x_copy = x.copy()
        y_copy = y.copy()
        z_copy = z.copy()
    
        np.random.seed(0)
    
        obs = pwmantel((x, y, z), alternative='greater', strict=False,
                       lookup=lookup)
>       assert_data_frame_almost_equal(
            obs,
            self.exp_results_reordered_distance_matrices)

skbio/stats/distance/tests/test_mantel.py:431: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.822  3  pearson           999     greater
right =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.822  3  pearson           999     greater
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
_______________ PairwiseMantelTests.test_many_filepaths_as_input _______________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_many_filepaths_as_input>

    def test_many_filepaths_as_input(self):
        dms = [
            get_data_path('dm2.txt'),
            get_data_path('dm.txt'),
            get_data_path('dm4.txt'),
            get_data_path('dm3.txt')
        ]
        np.random.seed(0)
    
        obs = pwmantel(dms)
>       assert_data_frame_almost_equal(obs, self.exp_results_all_dms)

skbio/stats/distance/tests/test_mantel.py:477: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...920688    0.004  6  pearson           999   two-sided
2   3     0.717297    0.003  6  pearson           999   two-sided
right =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...920688    0.004  6  pearson           999   two-sided
2   3     0.717297    0.003  6  pearson           999   two-sided
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
______________ PairwiseMantelTests.test_minimal_compatible_input _______________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_minimal_compatible_input>

    def test_minimal_compatible_input(self):
        # Matrices are already in the correct order and have matching IDs.
        np.random.seed(0)
    
        # input as DistanceMatrix instances
        obs = pwmantel(self.min_dms, alternative='greater')
>       assert_data_frame_almost_equal(obs, self.exp_results_minimal)

skbio/stats/distance/tests/test_mantel.py:355: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.835  3  pearson           999     greater
right =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.835  3  pearson           999     greater
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
________ PairwiseMantelTests.test_minimal_compatible_input_with_labels _________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_minimal_compatible_input_with_labels>

    def test_minimal_compatible_input_with_labels(self):
        np.random.seed(0)
    
        obs = pwmantel(self.min_dms, alternative='greater',
                       labels=('minx', 'miny', 'minz'))
>       assert_data_frame_almost_equal(
            obs,
            self.exp_results_minimal_with_labels)

skbio/stats/distance/tests/test_mantel.py:369: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =            statistic  p-value  n   method  permutations alternative
dm1  dm2                                          ...9743    1.000  3  pearson           999     greater
miny minz  -0.841698    0.835  3  pearson           999     greater
right =            statistic  p-value  n   method  permutations alternative
dm1  dm2                                          ...9743    1.000  3  pearson           999     greater
miny minz  -0.841698    0.835  3  pearson           999     greater
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
_____________________ PairwiseMantelTests.test_na_p_value ______________________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_na_p_value>

    def test_na_p_value(self):
        obs = pwmantel((self.miny_dm, self.minx_dm), method='spearman',
                       permutations=0)
>       assert_data_frame_almost_equal(obs, self.exp_results_na_p_value)

skbio/stats/distance/tests/test_mantel.py:381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n    method  permutations alternative
dm1 dm2                                                           
0   1          0.5      NaN  3  spearman             0   two-sided
right =          statistic  p-value  n    method  permutations alternative
dm1 dm2                                                           
0   1          0.5      NaN  3  spearman             0   two-sided
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
_____________ PairwiseMantelTests.test_reordered_distance_matrices _____________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_reordered_distance_matrices>

    def test_reordered_distance_matrices(self):
        # Matrices have matching IDs but they all have different ordering.
        x = self.minx_dm.filter(['1', '0', '2'])
        y = self.miny_dm.filter(['0', '2', '1'])
        z = self.minz_dm.filter(['1', '2', '0'])
    
        np.random.seed(0)
    
        obs = pwmantel((x, y, z), alternative='greater')
>       assert_data_frame_almost_equal(
            obs,
            self.exp_results_reordered_distance_matrices)

skbio/stats/distance/tests/test_mantel.py:392: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.822  3  pearson           999     greater
right =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.822  3  pearson           999     greater
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
_______________________ PairwiseMantelTests.test_strict ________________________

self = <skbio.stats.distance.tests.test_mantel.PairwiseMantelTests testMethod=test_strict>

    def test_strict(self):
        # Matrices have some matching and nonmatching IDs, with different
        # ordering.
        x = self.minx_dm_extra.filter(['1', '0', 'foo', '2'])
        y = self.miny_dm.filter(['0', '2', '1'])
        z = self.minz_dm_extra.filter(['bar', '1', '2', '0'])
    
        np.random.seed(0)
    
        # strict=False should discard IDs that aren't found in both matrices
        obs = pwmantel((x, y, z), alternative='greater', strict=False)
>       assert_data_frame_almost_equal(
            obs,
            self.exp_results_reordered_distance_matrices)

skbio/stats/distance/tests/test_mantel.py:407: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

left =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.822  3  pearson           999     greater
right =          statistic  p-value  n   method  permutations alternative
dm1 dm2                                             ...989743    1.000  3  pearson           999     greater
1   2    -0.841698    0.822  3  pearson           999     greater
rtol = 1e-05

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right, rtol=1e-5):
        """Raise AssertionError if ``pd.DataFrame`` objects are not "almost equal".
    
        Wrapper of ``pd.util.testing.assert_frame_equal``. Floating point values
        are considered "almost equal" if they are within a threshold defined by
        ``assert_frame_equal``. This wrapper uses a number of
        checks that are turned off by default in ``assert_frame_equal`` in order to
        perform stricter comparisons (for example, ensuring the index and column
        types are the same). It also does not consider empty ``pd.DataFrame``
        objects equal if they have a different index.
    
        Other notes:
    
        * Index (row) and column ordering must be the same for objects to be equal.
        * NaNs (``np.nan``) in the same locations are considered equal.
    
        This is a helper function intended to be used in unit tests that need to
        compare ``pd.DataFrame`` objects.
    
        Parameters
        ----------
        left, right : pd.DataFrame
            ``pd.DataFrame`` objects to compare.
    
        Raises
        ------
        AssertionError
            If `left` and `right` are not "almost equal".
    
        See Also
        --------
        pandas.util.testing.assert_frame_equal
    
        """
        # pass all kwargs to ensure this function has consistent behavior even if
        # `assert_frame_equal`'s defaults change
>       pdt.assert_frame_equal(left, right,
                               check_dtype=True,
                               check_index_type=True,
                               check_column_type=True,
                               check_frame_type=True,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False,
                               rtol=rtol)
E       AssertionError: Attributes of DataFrame.iloc[:, 2] (column name="n") are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:304: AssertionError
_____________ SubsampleCountsTests.test_subsample_counts_nonrandom _____________

self = <skbio.stats.tests.test_subsample.SubsampleCountsTests testMethod=test_subsample_counts_nonrandom>

    def test_subsample_counts_nonrandom(self):
        a = np.array([0, 5, 0])
    
        # Subsample same number of items that are in input (without
        # replacement).
        npt.assert_equal(subsample_counts(a, 5), a)
    
        # Can only choose from one bin.
        exp = np.array([0, 2, 0])
>       npt.assert_equal(subsample_counts(a, 2), exp)

skbio/stats/tests/test_subsample.py:38: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/stats/_subsample.py:246: in subsample_counts
    result = _subsample_counts_without_replacement(counts, n,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'

skbio/stats/__subsample.pyx:13: ValueError
________ SubsampleCountsTests.test_subsample_counts_without_replacement ________

self = <skbio.stats.tests.test_subsample.SubsampleCountsTests testMethod=test_subsample_counts_without_replacement>

    def test_subsample_counts_without_replacement(self):
        # Selecting 2 counts from the vector 1000 times yields each of the two
        # possible results at least once each.
        a = np.array([2, 0, 1])
        actual = set()
        for i in range(1000):
>           obs = subsample_counts(a, 2)

skbio/stats/tests/test_subsample.py:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
skbio/stats/_subsample.py:246: in subsample_counts
    result = _subsample_counts_without_replacement(counts, n,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

>   ???
E   ValueError: Buffer dtype mismatch, expected 'int64_t' but got 'long'

skbio/stats/__subsample.pyx:13: ValueError
____________________ ISubsampleTests.test_isubsample_simple ____________________

self = <skbio.stats.tests.test_subsample.ISubsampleTests testMethod=test_isubsample_simple>

    def test_isubsample_simple(self):
        maximum = 10
    
        def bin_f(x):
            return x['SequenceID'].rsplit('_', 1)[0]
    
        # note, the result here is sorted by sequence_id but is in heap order
        # by the random values associated to each sequence
        exp = sorted([('a', {'SequenceID': 'a_5', 'Sequence': 'AATTGGCC-a5'}),
                      ('a', {'SequenceID': 'a_1', 'Sequence': 'AATTGGCC-a1'}),
                      ('a', {'SequenceID': 'a_4', 'Sequence': 'AATTGGCC-a4'}),
                      ('a', {'SequenceID': 'a_3', 'Sequence': 'AATTGGCC-a3'}),
                      ('a', {'SequenceID': 'a_2', 'Sequence': 'AATTGGCC-a2'}),
                      ('b', {'SequenceID': 'b_2', 'Sequence': 'AATTGGCC-b2'}),
                      ('b', {'SequenceID': 'b_1', 'Sequence': 'AATTGGCC-b1'}),
                      ('c', {'SequenceID': 'c_3', 'Sequence': 'AATTGGCC-c3'}),
                      ('c', {'SequenceID': 'c_2', 'Sequence': 'AATTGGCC-c2'}),
                      ('c', {'SequenceID': 'c_1', 'Sequence': 'AATTGGCC-c1'})],
                     key=lambda x: x[0])
        obs = isubsample(self.mock_sequence_iter(self.sequences), maximum,
                         bin_f=bin_f)
>       self.assertEqual(sorted(obs, key=lambda x: x[0]), exp)
E       AssertionError: Lists differ: [('a'[15 chars]: 'a_3', 'Sequence': 'AATTGGCC-a3'}), ('a', {'[499 chars]3'})] != [('a'[15 chars]: 'a_5', 'Sequence': 'AATTGGCC-a5'}), ('a', {'[499 chars]1'})]
E       
E       First differing element 0:
E       ('a', {'SequenceID': 'a_3', 'Sequence': 'AATTGGCC-a3'})
E       ('a', {'SequenceID': 'a_5', 'Sequence': 'AATTGGCC-a5'})
E       
E       Diff is 1356 characters long. Set self.maxDiff to None to see it.

skbio/stats/tests/test_subsample.py:158: AssertionError
______________ ISubsampleTests.test_per_sample_sequences_complex _______________

self = <skbio.stats.tests.test_subsample.ISubsampleTests testMethod=test_per_sample_sequences_complex>

    def test_per_sample_sequences_complex(self):
        maximum = 2
    
        def bin_f(x):
            return x['SequenceID'].rsplit('_', 1)[0]
    
        exp = sorted([('a', {'SequenceID': 'a_2', 'Sequence': 'AATTGGCC-a2'}),
                      ('a', {'SequenceID': 'a_3', 'Sequence': 'AATTGGCC-a3'}),
                      ('b', {'SequenceID': 'b_2', 'Sequence': 'AATTGGCC-b2'}),
                      ('b', {'SequenceID': 'b_1', 'Sequence': 'AATTGGCC-b1'}),
                      ('c', {'SequenceID': 'c_1', 'Sequence': 'AATTGGCC-c1'}),
                      ('c', {'SequenceID': 'c_2', 'Sequence': 'AATTGGCC-c2'})],
                     key=lambda x: x[0])
        obs = isubsample(self.mock_sequence_iter(self.sequences), maximum,
                         bin_f=bin_f, buf_size=1)
>       self.assertEqual(sorted(obs, key=lambda x: x[0]), exp)
E       AssertionError: Lists differ: [('a'[15 chars]: 'a_5', 'Sequence': 'AATTGGCC-a5'}), ('a', {'[271 chars]3'})] != [('a'[15 chars]: 'a_2', 'Sequence': 'AATTGGCC-a2'}), ('a', {'[271 chars]2'})]
E       
E       First differing element 0:
E       ('a', {'SequenceID': 'a_5', 'Sequence': 'AATTGGCC-a5'})
E       ('a', {'SequenceID': 'a_2', 'Sequence': 'AATTGGCC-a2'})
E       
E       Diff is 1124 characters long. Set self.maxDiff to None to see it.

skbio/stats/tests/test_subsample.py:197: AssertionError
______________ ISubsampleTests.test_per_sample_sequences_min_seqs ______________

self = <skbio.stats.tests.test_subsample.ISubsampleTests testMethod=test_per_sample_sequences_min_seqs>

    def test_per_sample_sequences_min_seqs(self):
        maximum = 10
        minimum = 3
    
        def bin_f(x):
            return x['SequenceID'].rsplit('_', 1)[0]
    
        # note, the result here is sorted by sequence_id but is in heap order
        # by the random values associated to each sequence
        exp = sorted([('a', {'SequenceID': 'a_5', 'Sequence': 'AATTGGCC-a5'}),
                      ('a', {'SequenceID': 'a_1', 'Sequence': 'AATTGGCC-a1'}),
                      ('a', {'SequenceID': 'a_4', 'Sequence': 'AATTGGCC-a4'}),
                      ('a', {'SequenceID': 'a_3', 'Sequence': 'AATTGGCC-a3'}),
                      ('a', {'SequenceID': 'a_2', 'Sequence': 'AATTGGCC-a2'}),
                      ('c', {'SequenceID': 'c_3', 'Sequence': 'AATTGGCC-c3'}),
                      ('c', {'SequenceID': 'c_2', 'Sequence': 'AATTGGCC-c2'}),
                      ('c', {'SequenceID': 'c_1', 'Sequence': 'AATTGGCC-c1'})],
                     key=lambda x: x[0])
        obs = isubsample(self.mock_sequence_iter(self.sequences), maximum,
                         minimum, bin_f=bin_f)
>       self.assertEqual(sorted(obs, key=lambda x: x[0]), exp)
E       AssertionError: Lists differ: [('a'[15 chars]: 'a_3', 'Sequence': 'AATTGGCC-a3'}), ('a', {'[385 chars]3'})] != [('a'[15 chars]: 'a_5', 'Sequence': 'AATTGGCC-a5'}), ('a', {'[385 chars]1'})]
E       
E       First differing element 0:
E       ('a', {'SequenceID': 'a_3', 'Sequence': 'AATTGGCC-a3'})
E       ('a', {'SequenceID': 'a_5', 'Sequence': 'AATTGGCC-a5'})
E       
E       Diff is 1062 characters long. Set self.maxDiff to None to see it.

skbio/stats/tests/test_subsample.py:180: AssertionError
=============================== warnings summary ===============================
skbio/stats/ordination/tests/test_redundancy_analysis.py:12
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_redundancy_analysis.py:12: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
    import pandas.util.testing as pdt

.pybuild/cpython3_3.10_skbio/build/skbio/alignment/tests/test_tabular_msa.py::TestLoc::test_multiindex_complicated_axis
.pybuild/cpython3_3.10_skbio/build/skbio/alignment/tests/test_tabular_msa.py::TestLoc::test_multiindex_complicated_axis_empty_selection
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment/_indexing.py:116: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
    indexable = np.asarray(list(indexable))

.pybuild/cpython3_3.10_skbio/build/skbio/alignment/tests/test_tabular_msa.py::TestGapFrequencies::test_no_positions_relative
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/alignment/_tabular_msa.py:1653: RuntimeWarning: invalid value encountered in true_divide
    gap_freqs /= length

.pybuild/cpython3_3.10_skbio/build/skbio/diversity/alpha/tests/test_base.py::BaseTests::test_pielou_e
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity/alpha/_base.py:793: RuntimeWarning: invalid value encountered in double_scalars
    return shannon(counts, base=np.e) / np.log(observed_otus(counts))

.pybuild/cpython3_3.10_skbio/build/skbio/diversity/alpha/tests/test_base.py::BaseTests::test_pielou_e
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity/alpha/_base.py:869: RuntimeWarning: invalid value encountered in true_divide
    freqs = counts / counts.sum()

.pybuild/cpython3_3.10_skbio/build/skbio/diversity/alpha/tests/test_base.py::BaseTests::test_pielou_e
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity/alpha/_base.py:793: RuntimeWarning: divide by zero encountered in log
    return shannon(counts, base=np.e) / np.log(observed_otus(counts))

.pybuild/cpython3_3.10_skbio/build/skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_invalid_input
.pybuild/cpython3_3.10_skbio/build/skbio/diversity/tests/test_util.py::ValidationTests::test_validate_counts_matrix_unequal_lengths
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/diversity/_util.py:56: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
    counts = np.asarray(counts)

.pybuild/cpython3_3.10_skbio/build/skbio/diversity/tests/test_block.py: 1 warning
.pybuild/cpython3_3.10_skbio/build/skbio/diversity/tests/test_driver.py: 9 warnings
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/util/_decorator.py:254: DeprecationWarning: partial_beta_diversity is deprecated as of scikit-bio version 0.5.0, and will be removed in version 0.6.0. The return type is unstable. Developer caution is advised. The resulting DistanceMatrix object will include zeros when distance has not been calculated, and therefore can be misleading.
    warnings.warn('%s is deprecated as of scikit-bio version %s, and '

.pybuild/cpython3_3.10_skbio/build/skbio/diversity/tests/test_driver.py::BetaDiversityTests::test_qualitative_bug_issue_1549
  /usr/lib/python3/dist-packages/sklearn/metrics/pairwise.py:2008: DataConversionWarning: Data was converted to boolean for metric jaccard
    warnings.warn(msg, DataConversionWarning)

.pybuild/cpython3_3.10_skbio/build/skbio/diversity/tests/test_util.py::ValidationTests::test_validate_counts_vector_invalid_input
  /usr/lib/python3/dist-packages/numpy/lib/type_check.py:300: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
    return imag(x) == 0

.pybuild/cpython3_3.10_skbio/build/skbio/io/format/tests/test_fasta.py::SnifferTests::test_positives
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io/registry.py:918: FormatIdentificationWarning: '_fasta_sniffer' has encountered a problem.
  Please send the following to our issue tracker at
  https://github.com/biocore/scikit-bio/issues
  
  Traceback (most recent call last):
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io/registry.py", line 914, in wrapped_sniffer
      return sniffer(fh)
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io/format/fasta.py", line 648, in _fasta_sniffer
      for _ in zip(range(num_records), parser):
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io/format/fasta.py", line 868, in _parse_fasta_raw
      yield data_parser(data_chunks), id_, desc
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io/format/fasta.py", line 662, in _sniffer_data_parser
      _parse_quality_scores(chunks)
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/io/format/fasta.py", line 883, in _parse_quality_scores
      quality = np.asarray(qual_str.split(), dtype=int)
  OverflowError: Python int too large to convert to C long
  
    warn("'%s' has encountered a problem.\nPlease"

.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/util/_decorator.py:254: DeprecationWarning: has_nondegenerates is deprecated as of scikit-bio version 0.5.0, and will be removed in version 0.6.0. Renamed to has_definites
    warnings.warn('%s is deprecated as of scikit-bio version %s, and '

.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_protein.py::TestProtein::test_nondegenerate_chars
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_protein.py::TestProtein::test_nondegenerate_chars
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/util/_decorator.py:254: DeprecationWarning: nondegenerate_chars is deprecated as of scikit-bio version 0.5.0, and will be removed in version 0.6.0. Renamed to definite_chars
    warnings.warn('%s is deprecated as of scikit-bio version %s, and '

.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/util/_decorator.py:254: DeprecationWarning: nondegenerates is deprecated as of scikit-bio version 0.5.0, and will be removed in version 0.6.0. Renamed to definites
    warnings.warn('%s is deprecated as of scikit-bio version %s, and '

.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_sequence.py::TestSequence::test_frequencies_empty_sequence
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/sequence/_sequence.py:1819: RuntimeWarning: invalid value encountered in true_divide
    obs_counts = obs_counts / len(self)

.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_sequence.py::TestSequence::test_init_invalid_sequence
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_sequence.py:461: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
    Sequence(np.array([1, {}, ()]))

.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_sequence.py::TestSequence::test_munge_to_index_array_valid_iterable
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/sequence/tests/test_sequence.py:2214: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
    for c in (lambda x: x, list, tuple, lambda x: np.array(tuple(x)),

.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_pearson
  /usr/lib/python3/dist-packages/scipy/stats/_stats_py.py:4068: PearsonRConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
    warnings.warn(PearsonRConstantInputWarning())

.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_spearman
  /usr/lib/python3/dist-packages/scipy/stats/_stats_py.py:4529: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
    warnings.warn(SpearmanRConstantInputWarning())

.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_eq_groups
.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_no_permuations
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/_principal_coordinate_analysis.py:143: RuntimeWarning: The result contains negative eigenvalues. Please compare their magnitude with the magnitude of some of the largest positive eigenvalues. If the negative ones are smaller, it's probably safe to ignore them, but if they are large in magnitude, the results won't be useful. See the Notes section for more details. The smallest eigenvalue is -13.541846544269625 and the largest is 16.5872013747859.
    warn(

.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_mixedgroups
.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_uneq_groups
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/_principal_coordinate_analysis.py:143: RuntimeWarning: The result contains negative eigenvalues. Please compare their magnitude with the magnitude of some of the largest positive eigenvalues. If the negative ones are smaller, it's probably safe to ignore them, but if they are large in magnitude, the results won't be useful. See the Notes section for more details. The smallest eigenvalue is -15.629533932395216 and the largest is 20.90315427793891.
    warn(

.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_null
.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_not_distance_matrix
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/_principal_coordinate_analysis.py:188: RuntimeWarning: invalid value encountered in true_divide
    proportion_explained = eigvals / sum_eigenvalues

.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_null
.pybuild/cpython3_3.10_skbio/build/skbio/stats/tests/test_composition.py::AncomTests::test_ancom_no_signal
  /usr/lib/python3/dist-packages/scipy/stats/_stats_py.py:3659: F_onewayConstantInputWarning: Each of the input arrays is constant;the F statistic is not defined or infinite
    warnings.warn(F_onewayConstantInputWarning())

.pybuild/cpython3_3.10_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_confirm_betadispr_results
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/_principal_coordinate_analysis.py:143: RuntimeWarning: The result contains negative eigenvalues. Please compare their magnitude with the magnitude of some of the largest positive eigenvalues. If the negative ones are smaller, it's probably safe to ignore them, but if they are large in magnitude, the results won't be useful. See the Notes section for more details. The smallest eigenvalue is -0.007758325147780715 and the largest is 2.707792948946925.
    warn(

.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_ordination_results.py::TestOrdinationResults::test_plot_no_metadata
.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_ordination_results.py::TestOrdinationResults::test_plot_with_categorical_metadata_and_plot_options
.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_ordination_results.py::TestOrdinationResults::test_plot_with_numeric_metadata_and_plot_options
.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_ordination_results.py::TestOrdinationResults::test_png
.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_ordination_results.py::TestOrdinationResults::test_repr_png
.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_ordination_results.py::TestOrdinationResults::test_repr_svg
.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/tests/test_ordination_results.py::TestOrdinationResults::test_svg
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/stats/ordination/_ordination_results.py:285: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations.
    fig.tight_layout()

.pybuild/cpython3_3.10_skbio/build/skbio/util/tests/test_decorator.py::TestDeprecated::test_function_output
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build/skbio/util/_decorator.py:254: DeprecationWarning: f is deprecated as of scikit-bio version 0.1.0, and will be removed in version 0.1.4. You should now use skbio.g().
    warnings.warn('%s is deprecated as of scikit-bio version %s, and '

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
FAILED skbio/alignment/tests/test_tabular_msa.py::TestTabularMSAPositionalMetadata::test_eq_from_different_source
FAILED skbio/alignment/tests/test_tabular_msa.py::TestILoc::test_fancy_empty_second_axis
FAILED skbio/diversity/alpha/tests/test_base.py::BaseTests::test_michaelis_menten_fit
FAILED skbio/diversity/alpha/tests/test_faith_pd.py::FaithPDTests::test_faith_pd
FAILED skbio/diversity/alpha/tests/test_faith_pd.py::FaithPDTests::test_faith_pd_all_observed
FAILED skbio/diversity/alpha/tests/test_faith_pd.py::FaithPDTests::test_faith_pd_extra_tips
FAILED skbio/diversity/alpha/tests/test_faith_pd.py::FaithPDTests::test_faith_pd_minimal
FAILED skbio/diversity/alpha/tests/test_faith_pd.py::FaithPDTests::test_faith_pd_none_observed
FAILED skbio/diversity/alpha/tests/test_faith_pd.py::FaithPDTests::test_faith_pd_qiime_tiny_test
FAILED skbio/diversity/alpha/tests/test_faith_pd.py::FaithPDTests::test_faith_pd_root_not_observed
FAILED skbio/diversity/alpha/tests/test_lladser.py::LladserTests::test_lladser_ci
FAILED skbio/diversity/alpha/tests/test_lladser.py::LladserTests::test_lladser_ci_f3
FAILED skbio/diversity/alpha/tests/test_lladser.py::LladserTests::test_lladser_pe
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_extra_tips
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_minimal_trees
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_otus_out_of_order
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_root_not_observed
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_unifrac
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_unifrac_identity
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_unifrac_non_overlapping
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_unifrac_symmetry
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_unweighted_unifrac_zero_counts
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_extra_tips
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_minimal_trees
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_normalized_root_not_observed
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_otus_out_of_order
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_root_not_observed
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_identity
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_identity_normalized
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_non_overlapping
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_non_overlapping_normalized
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_normalized
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_symmetry
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_symmetry_normalized
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_zero_counts
FAILED skbio/diversity/beta/tests/test_unifrac.py::UnifracTests::test_weighted_unifrac_zero_counts_normalized
FAILED skbio/diversity/tests/test_block.py::ParallelBetaDiversity::test_block_beta_diversity
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_empty
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_faith_pd
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_input_types
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_invalid_input
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_no_ids
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_observed_otus
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_optimized
FAILED skbio/diversity/tests/test_driver.py::AlphaDiversityTests::test_single_count_vector
FAILED skbio/diversity/tests/test_driver.py::BetaDiversityTests::test_alt_pairwise_func
FAILED skbio/diversity/tests/test_driver.py::BetaDiversityTests::test_invalid_input
FAILED skbio/diversity/tests/test_driver.py::BetaDiversityTests::test_unweighted_unifrac
FAILED skbio/diversity/tests/test_driver.py::BetaDiversityTests::test_weighted_unifrac
FAILED skbio/diversity/tests/test_driver.py::BetaDiversityTests::test_weighted_unifrac_normalized
FAILED skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_id_pairs_as_iterable
FAILED skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_unweighted_unifrac_partial
FAILED skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_weighted_unifrac_partial_full
FAILED skbio/diversity/tests/test_util.py::ValidationTests::test_vectorize_counts_and_tree
FAILED skbio/io/format/tests/test_fasta.py::SnifferTests::test_positives - As...
FAILED skbio/metadata/tests/test_mixin.py::TestPositionalMetadataMixin::test_eq_from_different_source
FAILED skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_degap
FAILED skbio/sequence/tests/test_sequence.py::TestSequencePositionalMetadata::test_eq_from_different_source
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_eq_sequences_from_different_sources_compare_equal
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_getitem_with_boolean_vector_has_qual
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_getitem_with_int_has_positional_metadata
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_getitem_with_iterable_of_mixed_has_positional_metadata
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_getitem_with_numpy_index_has_positional_metadata
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_getitem_with_slice_has_positional_metadata
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_getitem_with_tuple_of_mixed_with_positional_metadata
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_init_invalid_sequence
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_iter_has_positional_metadata
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_reversed_has_positional_metadata
FAILED skbio/sequence/tests/test_sequence.py::TestSequence::test_slice_positional_metadata
FAILED skbio/stats/distance/tests/test_bioenv.py::BIOENVTests::test_bioenv_all_columns_explicit
FAILED skbio/stats/distance/tests/test_bioenv.py::BIOENVTests::test_bioenv_all_columns_implicit
FAILED skbio/stats/distance/tests/test_bioenv.py::BIOENVTests::test_bioenv_different_column_order
FAILED skbio/stats/distance/tests/test_bioenv.py::BIOENVTests::test_bioenv_single_column
FAILED skbio/stats/distance/tests/test_bioenv.py::BIOENVTests::test_bioenv_vegan_example
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_duplicate_dms
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_filepaths_as_input
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_id_lookup
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_many_filepaths_as_input
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_minimal_compatible_input
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_minimal_compatible_input_with_labels
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_na_p_value
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_reordered_distance_matrices
FAILED skbio/stats/distance/tests/test_mantel.py::PairwiseMantelTests::test_strict
FAILED skbio/stats/tests/test_subsample.py::SubsampleCountsTests::test_subsample_counts_nonrandom
FAILED skbio/stats/tests/test_subsample.py::SubsampleCountsTests::test_subsample_counts_without_replacement
FAILED skbio/stats/tests/test_subsample.py::ISubsampleTests::test_isubsample_simple
FAILED skbio/stats/tests/test_subsample.py::ISubsampleTests::test_per_sample_sequences_complex
FAILED skbio/stats/tests/test_subsample.py::ISubsampleTests::test_per_sample_sequences_min_seqs
===== 89 failed, 2233 passed, 30 skipped, 66 warnings in 599.56s (0:09:59) =====
E: pybuild pybuild:379: test: plugin distutils failed with: exit code=1: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.10_skbio/build; python3.10 -m pytest 
dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p 3.10 returned exit code 13
make: *** [debian/rules:14: binary-arch] Error 25
dpkg-buildpackage: error: debian/rules binary-arch subprocess returned exit status 2
--------------------------------------------------------------------------------
Build finished at 2022-11-21T02:53:19Z

Finished
--------


+------------------------------------------------------------------------------+
| Cleanup                                                                      |
+------------------------------------------------------------------------------+

Purging /<<BUILDDIR>>
Not cleaning session: cloned chroot in use
E: Build failure (dpkg-buildpackage died)

+------------------------------------------------------------------------------+
| Summary                                                                      |
+------------------------------------------------------------------------------+

Build Architecture: armhf
Build-Space: 0
Build-Time: 793
Distribution: bookworm-staging
Fail-Stage: build
Host Architecture: armhf
Install-Time: 2036
Job: python-skbio_0.5.6-6
Machine Architecture: armhf
Package: python-skbio
Package-Time: 2901
Source-Version: 0.5.6-6
Space: 0
Status: failed
Version: 0.5.6-6
--------------------------------------------------------------------------------
Finished at 2022-11-21T02:53:19Z
Build needed 00:00:00, 0k disc space
Raspbian Package Auto-Building

Build log for python-skbio (0.5.6-6) on armhf
