Raspbian Package Auto-Building

Build log for python-skbio (0.5.6-2) on armhf

python-skbio → 0.5.6-2 → armhf → 2020-05-22 06:10:53 

sbuild (Debian sbuild) 0.73.0 (23 Dec 2016) on test2019

+==============================================================================+
| python-skbio 0.5.6-2 (armhf)                 Fri, 22 May 2020 05:39:58 +0000 |
+==============================================================================+

Package: python-skbio
Version: 0.5.6-2
Source Version: 0.5.6-2
Distribution: bullseye-staging
Machine Architecture: armhf
Host Architecture: armhf
Build Architecture: armhf
Build Type: any

I: NOTICE: Log filtering will replace 'var/run/schroot/mount/bullseye-staging-armhf-sbuild-e70f12b0-079c-456c-83d2-a1490ecafc1b' with '<<CHROOT>>'

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

Get:1 http://172.17.0.1/private bullseye-staging InRelease [11.3 kB]
Get:2 http://172.17.0.1/private bullseye-staging/main Sources [11.7 MB]
Get:3 http://172.17.0.1/private bullseye-staging/main armhf Packages [12.8 MB]
Fetched 24.5 MB in 29s (854 kB/s)
Reading package lists...

+------------------------------------------------------------------------------+
| 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 8377 kB of source archives.
Get:1 http://172.17.0.1/private bullseye-staging/main python-skbio 0.5.6-2 (dsc) [2628 B]
Get:2 http://172.17.0.1/private bullseye-staging/main python-skbio 0.5.6-2 (tar) [8366 kB]
Get:3 http://172.17.0.1/private bullseye-staging/main python-skbio 0.5.6-2 (diff) [8756 B]
Fetched 8377 kB in 5s (1797 kB/s)
Download complete and in download only mode
I: NOTICE: Log filtering will replace 'build/python-skbio-QQOfyz/python-skbio-0.5.6' with '<<PKGBUILDDIR>>'
I: NOTICE: Log filtering will replace 'build/python-skbio-QQOfyz' 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-8Ft4FH/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-8Ft4FH/gpg/pubring.kbx' created
gpg: /<<BUILDDIR>>/resolver-8Ft4FH/gpg/trustdb.gpg: trustdb created
gpg: key E70254B6505CF8F7: 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 E70254B6505CF8F7: "Sbuild Signer (Sbuild Build Dependency Archive Key) <buildd-tools-devel@lists.alioth.debian.org>" not changed
gpg: key E70254B6505CF8F7: 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-8Ft4FH/apt_archive ./ InRelease
Get:2 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release [957 B]
Get:3 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release.gpg [370 B]
Ign:3 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release.gpg
Get:4 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Sources [349 B]
Get:5 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Packages [434 B]
Fetched 2110 B in 1s (2955 B/s)
Reading package lists...
W: copy:///<<BUILDDIR>>/resolver-8Ft4FH/apt_archive/./Release.gpg: The key(s) in the keyring /etc/apt/trusted.gpg.d/sbuild-build-depends-archive.gpg are ignored as the file is not readable by user '_apt' executing apt-key.
W: GPG error: copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY E70254B6505CF8F7
Reading package lists...

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

Installing build dependencies
Reading package lists...
Building dependency tree...
Reading state information...
The following package was automatically installed and is no longer required:
  libpam-cap
Use 'apt autoremove' to remove it.
The following NEW packages will be installed:
  sbuild-build-depends-core-dummy
0 upgraded, 1 newly installed, 0 to remove and 0 not upgraded.
Need to get 848 B of archives.
After this operation, 0 B of additional disk space will be used.
Get:1 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ sbuild-build-depends-core-dummy 0.invalid.0 [848 B]
debconf: delaying package configuration, since apt-utils is not installed
Fetched 848 B in 0s (22.4 kB/s)
Selecting previously unselected package sbuild-build-depends-core-dummy.
(Reading database ... 14093 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) ...

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

Arch check ok (armhf included in any all)

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


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

Merged Build-Depends: debhelper-compat (= 12), 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, python3-sklearn, python3-natsort, libssw-dev, sphinx-common, libsimde-dev, python3-lockfile, python3-pytest
Filtered Build-Depends: debhelper-compat (= 12), 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, 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-8Ft4FH/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-8Ft4FH/apt_archive ./ InRelease
Get:2 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release [963 B]
Get:3 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release.gpg [370 B]
Ign:3 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release.gpg
Get:4 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Sources [658 B]
Get:5 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Packages [723 B]
Fetched 2714 B in 1s (3262 B/s)
Reading package lists...
W: copy:///<<BUILDDIR>>/resolver-8Ft4FH/apt_archive/./Release.gpg: The key(s) in the keyring /etc/apt/trusted.gpg.d/sbuild-build-depends-archive.gpg are ignored as the file is not readable by user '_apt' executing apt-key.
W: GPG error: copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ Release: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY E70254B6505CF8F7
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 package was automatically installed and is no longer required:
  libpam-cap
Use 'apt autoremove' to remove it.
The following additional packages will be installed:
  autoconf automake autopoint autotools-dev bsdmainutils ca-certificates
  cython3 debhelper dh-autoreconf dh-python dh-strip-nondeterminism dwz file
  fonts-lyx gettext gettext-base groff-base intltool-debian
  libarchive-zip-perl libblas3 libbsd0 libcroco3 libdebhelper-perl libelf1
  libexpat1 libexpat1-dev libfile-stripnondeterminism-perl libfreetype6
  libgfortran5 libglib2.0-0 libicu63 libjs-jquery libjs-jquery-ui
  libjs-sphinxdoc libjs-underscore liblapack3 liblbfgsb0 libmagic-mgc
  libmagic1 libmpdec2 libncurses6 libpipeline1 libpng16-16 libprocps8
  libpython3-all-dev libpython3-dev libpython3-stdlib libpython3.7-minimal
  libpython3.7-stdlib libpython3.8 libpython3.8-dev libpython3.8-minimal
  libpython3.8-stdlib libsigsegv2 libsimde-dev libssl1.1 libssw-dev libssw0
  libsub-override-perl libtinfo5 libtool libuchardet0 libxml2 m4 man-db
  mime-support node-jquery openssl po-debconf procps python-matplotlib-data
  python3 python3-all python3-all-dev python3-atomicwrites python3-attr
  python3-backcall python3-cachecontrol python3-certifi python3-chardet
  python3-cycler python3-dateutil python3-decorator python3-dev
  python3-distutils python3-hdmedians python3-idna python3-importlib-metadata
  python3-ipython python3-ipython-genutils python3-jedi python3-joblib
  python3-kiwisolver python3-lib2to3 python3-lockfile python3-matplotlib
  python3-minimal python3-more-itertools python3-msgpack python3-natsort
  python3-numpy python3-packaging python3-pandas python3-pandas-lib
  python3-parso python3-pexpect python3-pickleshare python3-pkg-resources
  python3-pluggy python3-prompt-toolkit python3-ptyprocess python3-py
  python3-pygments python3-pyparsing python3-pytest python3-requests
  python3-scipy python3-setuptools python3-six python3-sklearn
  python3-sklearn-lib python3-traitlets python3-tz python3-urllib3
  python3-wcwidth python3-zipp python3.7 python3.7-minimal python3.8
  python3.8-dev python3.8-minimal sensible-utils sphinx-common
  ttf-bitstream-vera zlib1g-dev
Suggested packages:
  autoconf-archive gnu-standards autoconf-doc wamerican | wordlist whois
  vacation cython-doc dh-make gettext-doc libasprintf-dev libgettextpo-dev
  groff libjs-jquery-ui-docs libtool-doc gfortran | fortran95-compiler gcj-jdk
  m4-doc apparmor less www-browser libmail-box-perl python3-doc python3-tk
  python3-venv python-attr-doc python-cycler-doc python-ipython-doc
  python-lockfile-doc dvipng ffmpeg ghostscript gir1.2-gtk-3.0 inkscape
  ipython3 librsvg2-common python-matplotlib-doc python3-cairocffi python3-gi
  python3-gi-cairo python3-gobject python3-nose python3-pyqt5 python3-sip
  python3-tornado texlive-extra-utils texlive-latex-extra ttf-staypuft
  gfortran python-numpy-doc python3-numpy-dbg python-pandas-doc
  python3-statsmodels python-pexpect-doc subversion python-pygments-doc
  python-pyparsing-doc python3-cryptography python3-openssl python3-socks
  python-requests-doc python-scipy-doc python-setuptools-doc python3-dap
  python-sklearn-doc python3.7-venv python3.7-doc binfmt-support
  python3.8-venv python3.8-doc
Recommended packages:
  curl | wget | lynx libarchive-cpio-perl libglib2.0-data shared-mime-info
  xdg-user-dirs javascript-common libgpm2 libltdl-dev libmail-sendmail-perl
  psmisc python3-simplejson python3-psutil python3-pil python3-tk
  python-natsort-doc python3-numexpr python3-tables python3-xlrd
  python3-openpyxl python3-xlwt python3-bs4 python3-html5lib python3-lxml
  python3-nose python3-sphinx
The following NEW packages will be installed:
  autoconf automake autopoint autotools-dev bsdmainutils ca-certificates
  cython3 debhelper dh-autoreconf dh-python dh-strip-nondeterminism dwz file
  fonts-lyx gettext gettext-base groff-base intltool-debian
  libarchive-zip-perl libblas3 libbsd0 libcroco3 libdebhelper-perl libelf1
  libexpat1 libexpat1-dev libfile-stripnondeterminism-perl libfreetype6
  libgfortran5 libglib2.0-0 libicu63 libjs-jquery libjs-jquery-ui
  libjs-sphinxdoc libjs-underscore liblapack3 liblbfgsb0 libmagic-mgc
  libmagic1 libmpdec2 libncurses6 libpipeline1 libpng16-16 libprocps8
  libpython3-all-dev libpython3-dev libpython3-stdlib libpython3.7-minimal
  libpython3.7-stdlib libpython3.8 libpython3.8-dev libpython3.8-minimal
  libpython3.8-stdlib libsigsegv2 libsimde-dev libssl1.1 libssw-dev libssw0
  libsub-override-perl libtinfo5 libtool libuchardet0 libxml2 m4 man-db
  mime-support node-jquery openssl po-debconf procps python-matplotlib-data
  python3 python3-all python3-all-dev python3-atomicwrites python3-attr
  python3-backcall python3-cachecontrol python3-certifi python3-chardet
  python3-cycler python3-dateutil python3-decorator python3-dev
  python3-distutils python3-hdmedians python3-idna python3-importlib-metadata
  python3-ipython python3-ipython-genutils python3-jedi python3-joblib
  python3-kiwisolver python3-lib2to3 python3-lockfile python3-matplotlib
  python3-minimal python3-more-itertools python3-msgpack python3-natsort
  python3-numpy python3-packaging python3-pandas python3-pandas-lib
  python3-parso python3-pexpect python3-pickleshare python3-pkg-resources
  python3-pluggy python3-prompt-toolkit python3-ptyprocess python3-py
  python3-pygments python3-pyparsing python3-pytest python3-requests
  python3-scipy python3-setuptools python3-six python3-sklearn
  python3-sklearn-lib python3-traitlets python3-tz python3-urllib3
  python3-wcwidth python3-zipp python3.7 python3.7-minimal python3.8
  python3.8-dev python3.8-minimal sbuild-build-depends-python-skbio-dummy
  sensible-utils sphinx-common ttf-bitstream-vera zlib1g-dev
0 upgraded, 136 newly installed, 0 to remove and 0 not upgraded.
Need to get 79.5 MB of archives.
After this operation, 330 MB of additional disk space will be used.
Get:1 copy:/<<BUILDDIR>>/resolver-8Ft4FH/apt_archive ./ sbuild-build-depends-python-skbio-dummy 0.invalid.0 [1008 B]
Get:2 http://172.17.0.1/private bullseye-staging/main armhf libbsd0 armhf 0.10.0-1 [112 kB]
Get:3 http://172.17.0.1/private bullseye-staging/main armhf libtinfo5 armhf 6.2-1 [318 kB]
Get:4 http://172.17.0.1/private bullseye-staging/main armhf bsdmainutils armhf 11.1.2 [182 kB]
Get:5 http://172.17.0.1/private bullseye-staging/main armhf libuchardet0 armhf 0.0.6-3 [62.2 kB]
Get:6 http://172.17.0.1/private bullseye-staging/main armhf groff-base armhf 1.22.4-5 [783 kB]
Get:7 http://172.17.0.1/private bullseye-staging/main armhf libpipeline1 armhf 1.5.2-2 [29.6 kB]
Get:8 http://172.17.0.1/private bullseye-staging/main armhf man-db armhf 2.9.1-1 [1262 kB]
Get:9 http://172.17.0.1/private bullseye-staging/main armhf libssl1.1 armhf 1.1.1g-1 [1272 kB]
Get:10 http://172.17.0.1/private bullseye-staging/main armhf libpython3.8-minimal armhf 3.8.3-1 [751 kB]
Get:11 http://172.17.0.1/private bullseye-staging/main armhf libexpat1 armhf 2.2.9-1 [71.5 kB]
Get:12 http://172.17.0.1/private bullseye-staging/main armhf python3.8-minimal armhf 3.8.3-1 [1629 kB]
Get:13 http://172.17.0.1/private bullseye-staging/main armhf python3-minimal armhf 3.8.2-3 [37.6 kB]
Get:14 http://172.17.0.1/private bullseye-staging/main armhf mime-support all 3.64 [37.8 kB]
Get:15 http://172.17.0.1/private bullseye-staging/main armhf libmpdec2 armhf 2.4.2-3 [68.1 kB]
Get:16 http://172.17.0.1/private bullseye-staging/main armhf libpython3.8-stdlib armhf 3.8.3-1 [1600 kB]
Get:17 http://172.17.0.1/private bullseye-staging/main armhf python3.8 armhf 3.8.3-1 [416 kB]
Get:18 http://172.17.0.1/private bullseye-staging/main armhf libpython3-stdlib armhf 3.8.2-3 [20.8 kB]
Get:19 http://172.17.0.1/private bullseye-staging/main armhf python3 armhf 3.8.2-3 [63.7 kB]
Get:20 http://172.17.0.1/private bullseye-staging/main armhf libpython3.7-minimal armhf 3.7.7-1+b1 [585 kB]
Get:21 http://172.17.0.1/private bullseye-staging/main armhf python3.7-minimal armhf 3.7.7-1+b1 [1530 kB]
Get:22 http://172.17.0.1/private bullseye-staging/main armhf libncurses6 armhf 6.2-1 [79.5 kB]
Get:23 http://172.17.0.1/private bullseye-staging/main armhf libprocps8 armhf 2:3.3.16-5 [59.8 kB]
Get:24 http://172.17.0.1/private bullseye-staging/main armhf procps armhf 2:3.3.16-5 [238 kB]
Get:25 http://172.17.0.1/private bullseye-staging/main armhf sensible-utils all 0.0.12+nmu1 [16.0 kB]
Get:26 http://172.17.0.1/private bullseye-staging/main armhf libmagic-mgc armhf 1:5.38-5 [262 kB]
Get:27 http://172.17.0.1/private bullseye-staging/main armhf libmagic1 armhf 1:5.38-5 [113 kB]
Get:28 http://172.17.0.1/private bullseye-staging/main armhf file armhf 1:5.38-5 [67.0 kB]
Get:29 http://172.17.0.1/private bullseye-staging/main armhf gettext-base armhf 0.19.8.1-10 [117 kB]
Get:30 http://172.17.0.1/private bullseye-staging/main armhf libsigsegv2 armhf 2.12-2 [32.3 kB]
Get:31 http://172.17.0.1/private bullseye-staging/main armhf m4 armhf 1.4.18-4 [185 kB]
Get:32 http://172.17.0.1/private bullseye-staging/main armhf autoconf all 2.69-11.1 [341 kB]
Get:33 http://172.17.0.1/private bullseye-staging/main armhf autotools-dev all 20180224.1 [77.0 kB]
Get:34 http://172.17.0.1/private bullseye-staging/main armhf automake all 1:1.16.2-1 [775 kB]
Get:35 http://172.17.0.1/private bullseye-staging/main armhf autopoint all 0.19.8.1-10 [435 kB]
Get:36 http://172.17.0.1/private bullseye-staging/main armhf openssl armhf 1.1.1g-1 [811 kB]
Get:37 http://172.17.0.1/private bullseye-staging/main armhf ca-certificates all 20190110 [157 kB]
Get:38 http://172.17.0.1/private bullseye-staging/main armhf cython3 armhf 0.29.14-1 [1398 kB]
Get:39 http://172.17.0.1/private bullseye-staging/main armhf libtool all 2.4.6-14 [513 kB]
Get:40 http://172.17.0.1/private bullseye-staging/main armhf dh-autoreconf all 19 [16.9 kB]
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Get:42 http://172.17.0.1/private bullseye-staging/main armhf libarchive-zip-perl all 1.68-1 [104 kB]
Get:43 http://172.17.0.1/private bullseye-staging/main armhf libsub-override-perl all 0.09-2 [10.2 kB]
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Get:45 http://172.17.0.1/private bullseye-staging/main armhf dh-strip-nondeterminism all 1.8.0-1 [14.8 kB]
Get:46 http://172.17.0.1/private bullseye-staging/main armhf libelf1 armhf 0.176-1.1 [158 kB]
Get:47 http://172.17.0.1/private bullseye-staging/main armhf dwz armhf 0.13-5 [142 kB]
Get:48 http://172.17.0.1/private bullseye-staging/main armhf libglib2.0-0 armhf 2.64.2-1 [1158 kB]
Get:49 http://172.17.0.1/private bullseye-staging/main armhf libicu63 armhf 63.2-3 [7987 kB]
Get:50 http://172.17.0.1/private bullseye-staging/main armhf libxml2 armhf 2.9.10+dfsg-5 [592 kB]
Get:51 http://172.17.0.1/private bullseye-staging/main armhf libcroco3 armhf 0.6.13-1 [133 kB]
Get:52 http://172.17.0.1/private bullseye-staging/main armhf gettext armhf 0.19.8.1-10 [1219 kB]
Get:53 http://172.17.0.1/private bullseye-staging/main armhf intltool-debian all 0.35.0+20060710.5 [26.8 kB]
Get:54 http://172.17.0.1/private bullseye-staging/main armhf po-debconf all 1.0.21 [248 kB]
Get:55 http://172.17.0.1/private bullseye-staging/main armhf debhelper all 13 [1002 kB]
Get:56 http://172.17.0.1/private bullseye-staging/main armhf python3-lib2to3 all 3.8.2-2 [78.4 kB]
Get:57 http://172.17.0.1/private bullseye-staging/main armhf python3-distutils all 3.8.2-2 [145 kB]
Get:58 http://172.17.0.1/private bullseye-staging/main armhf dh-python all 4.20200315 [91.6 kB]
Get:59 http://172.17.0.1/private bullseye-staging/main armhf fonts-lyx all 2.3.4.2-2 [200 kB]
Get:60 http://172.17.0.1/private bullseye-staging/main armhf libblas3 armhf 3.9.0-2 [108 kB]
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Get:62 http://172.17.0.1/private bullseye-staging/main armhf libpng16-16 armhf 1.6.37-2 [274 kB]
Get:63 http://172.17.0.1/private bullseye-staging/main armhf libfreetype6 armhf 2.10.1-2 [330 kB]
Get:64 http://172.17.0.1/private bullseye-staging/main armhf libgfortran5 armhf 10.1.0-1+rpi1 [232 kB]
Get:65 http://172.17.0.1/private bullseye-staging/main armhf node-jquery all 3.5.1+dfsg-3 [309 kB]
Get:66 http://172.17.0.1/private bullseye-staging/main armhf libjs-jquery all 3.5.1+dfsg-3 [3548 B]
Get:67 http://172.17.0.1/private bullseye-staging/main armhf libjs-jquery-ui all 1.12.1+dfsg-5 [232 kB]
Get:68 http://172.17.0.1/private bullseye-staging/main armhf libjs-underscore all 1.9.1~dfsg-1 [99.4 kB]
Get:69 http://172.17.0.1/private bullseye-staging/main armhf libjs-sphinxdoc all 2.4.3-2 [110 kB]
Get:70 http://172.17.0.1/private bullseye-staging/main armhf liblapack3 armhf 3.9.0-2 [1602 kB]
Get:71 http://172.17.0.1/private bullseye-staging/main armhf liblbfgsb0 armhf 3.0+dfsg.3-8 [25.5 kB]
Get:72 http://172.17.0.1/private bullseye-staging/main armhf libpython3.8 armhf 3.8.3-1 [1365 kB]
Get:73 http://172.17.0.1/private bullseye-staging/main armhf libpython3.8-dev armhf 3.8.3-1 [3015 kB]
Get:74 http://172.17.0.1/private bullseye-staging/main armhf libpython3-dev armhf 3.8.2-3 [21.0 kB]
Get:75 http://172.17.0.1/private bullseye-staging/main armhf libpython3-all-dev armhf 3.8.2-3 [1068 B]
Get:76 http://172.17.0.1/private bullseye-staging/main armhf libpython3.7-stdlib armhf 3.7.7-1+b1 [1668 kB]
Get:77 http://172.17.0.1/private bullseye-staging/main armhf libsimde-dev all 0.0.0.git.20200424-1 [87.6 kB]
Get:78 http://172.17.0.1/private bullseye-staging/main armhf libssw0 armhf 1.1-9 [21.5 kB]
Get:79 http://172.17.0.1/private bullseye-staging/main armhf libssw-dev armhf 1.1-9 [24.8 kB]
Get:80 http://172.17.0.1/private bullseye-staging/main armhf ttf-bitstream-vera all 1.10-8 [352 kB]
Get:81 http://172.17.0.1/private bullseye-staging/main armhf python-matplotlib-data all 3.2.1-1 [4145 kB]
Get:82 http://172.17.0.1/private bullseye-staging/main armhf python3-all armhf 3.8.2-3 [1056 B]
Get:83 http://172.17.0.1/private bullseye-staging/main armhf zlib1g-dev armhf 1:1.2.11.dfsg-2 [184 kB]
Get:84 http://172.17.0.1/private bullseye-staging/main armhf python3.8-dev armhf 3.8.3-1 [519 kB]
Get:85 http://172.17.0.1/private bullseye-staging/main armhf python3-dev armhf 3.8.2-3 [1164 B]
Get:86 http://172.17.0.1/private bullseye-staging/main armhf python3-all-dev armhf 3.8.2-3 [1064 B]
Get:87 http://172.17.0.1/private bullseye-staging/main armhf python3-atomicwrites all 1.3.0-1 [9964 B]
Get:88 http://172.17.0.1/private bullseye-staging/main armhf python3-attr all 19.3.0-4 [43.3 kB]
Get:89 http://172.17.0.1/private bullseye-staging/main armhf python3-backcall all 0.1.0-2 [11.2 kB]
Get:90 http://172.17.0.1/private bullseye-staging/main armhf python3-certifi all 2020.4.5.1-1 [151 kB]
Get:91 http://172.17.0.1/private bullseye-staging/main armhf python3-pkg-resources all 46.1.3-1 [183 kB]
Get:92 http://172.17.0.1/private bullseye-staging/main armhf python3-chardet all 3.0.4-7 [81.1 kB]
Get:93 http://172.17.0.1/private bullseye-staging/main armhf python3-idna all 2.9-1 [37.1 kB]
Get:94 http://172.17.0.1/private bullseye-staging/main armhf python3-six all 1.14.0-3 [16.7 kB]
Get:95 http://172.17.0.1/private bullseye-staging/main armhf python3-urllib3 all 1.25.9-1 [105 kB]
Get:96 http://172.17.0.1/private bullseye-staging/main armhf python3-requests all 2.23.0+dfsg-2 [71.4 kB]
Get:97 http://172.17.0.1/private bullseye-staging/main armhf python3-msgpack armhf 0.6.2-1 [98.0 kB]
Get:98 http://172.17.0.1/private bullseye-staging/main armhf python3-cachecontrol all 0.12.6-1 [16.4 kB]
Get:99 http://172.17.0.1/private bullseye-staging/main armhf python3-cycler all 0.10.0-3 [8084 B]
Get:100 http://172.17.0.1/private bullseye-staging/main armhf python3-dateutil all 2.8.1-4 [81.6 kB]
Get:101 http://172.17.0.1/private bullseye-staging/main armhf python3-decorator all 4.4.2-2 [15.8 kB]
Get:102 http://172.17.0.1/private bullseye-staging/main armhf python3.7 armhf 3.7.7-1+b1 [354 kB]
Get:103 http://172.17.0.1/private bullseye-staging/main armhf python3-numpy armhf 1:1.17.4-5+b1 [4484 kB]
Get:104 http://172.17.0.1/private bullseye-staging/main armhf python3-hdmedians armhf 0.13~git20171027.8e0e9e3-1+b2 [168 kB]
Get:105 http://172.17.0.1/private bullseye-staging/main armhf python3-more-itertools all 4.2.0-2 [42.6 kB]
Get:106 http://172.17.0.1/private bullseye-staging/main armhf python3-zipp all 1.0.0-2 [5984 B]
Get:107 http://172.17.0.1/private bullseye-staging/main armhf python3-importlib-metadata all 1.6.0-1 [10.3 kB]
Get:108 http://172.17.0.1/private bullseye-staging/main armhf python3-parso all 0.7.0-1 [65.7 kB]
Get:109 http://172.17.0.1/private bullseye-staging/main armhf python3-jedi all 0.17.0-1 [523 kB]
Get:110 http://172.17.0.1/private bullseye-staging/main armhf python3-ptyprocess all 0.6.0-2 [13.3 kB]
Get:111 http://172.17.0.1/private bullseye-staging/main armhf python3-pexpect all 4.6.0-3 [52.6 kB]
Get:112 http://172.17.0.1/private bullseye-staging/main armhf python3-pickleshare all 0.7.5-2 [7520 B]
Get:113 http://172.17.0.1/private bullseye-staging/main armhf python3-wcwidth all 0.1.9+dfsg1-1 [18.2 kB]
Get:114 http://172.17.0.1/private bullseye-staging/main armhf python3-prompt-toolkit all 3.0.5-2 [251 kB]
Get:115 http://172.17.0.1/private bullseye-staging/main armhf python3-pygments all 2.3.1+dfsg-3 [596 kB]
Get:116 http://172.17.0.1/private bullseye-staging/main armhf python3-ipython-genutils all 0.2.0-3 [21.3 kB]
Get:117 http://172.17.0.1/private bullseye-staging/main armhf python3-traitlets all 4.3.3-3 [61.6 kB]
Get:118 http://172.17.0.1/private bullseye-staging/main armhf python3-ipython all 7.14.0-1 [509 kB]
Get:119 http://172.17.0.1/private bullseye-staging/main armhf python3-joblib all 0.14.0-3 [203 kB]
Get:120 http://172.17.0.1/private bullseye-staging/main armhf python3-kiwisolver armhf 1.0.1-3 [245 kB]
Get:121 http://172.17.0.1/private bullseye-staging/main armhf python3-lockfile all 1:0.12.2-2.2 [17.3 kB]
Get:122 http://172.17.0.1/private bullseye-staging/main armhf python3-pyparsing all 2.4.7-1 [109 kB]
Get:123 http://172.17.0.1/private bullseye-staging/main armhf python3-matplotlib armhf 3.2.1-1 [4617 kB]
Get:124 http://172.17.0.1/private bullseye-staging/main armhf python3-natsort all 7.0.1-1 [35.3 kB]
Get:125 http://172.17.0.1/private bullseye-staging/main armhf python3-packaging all 20.3-1.2 [29.9 kB]
Get:126 http://172.17.0.1/private bullseye-staging/main armhf python3-tz all 2019.3-2 [27.3 kB]
Get:127 http://172.17.0.1/private bullseye-staging/main armhf python3-pandas-lib armhf 0.25.3+dfsg2-2+rpi1 [3840 kB]
Get:128 http://172.17.0.1/private bullseye-staging/main armhf python3-pandas all 0.25.3+dfsg2-2+rpi1 [1983 kB]
Get:129 http://172.17.0.1/private bullseye-staging/main armhf python3-pluggy all 0.13.0-4 [22.0 kB]
Get:130 http://172.17.0.1/private bullseye-staging/main armhf python3-py all 1.8.1-2 [86.8 kB]
Get:131 http://172.17.0.1/private bullseye-staging/main armhf python3-pytest all 4.6.9-3 [267 kB]
Get:132 http://172.17.0.1/private bullseye-staging/main armhf python3-scipy armhf 1.4.1-2 [10.4 MB]
Get:133 http://172.17.0.1/private bullseye-staging/main armhf python3-setuptools all 46.1.3-1 [382 kB]
Get:134 http://172.17.0.1/private bullseye-staging/main armhf python3-sklearn-lib armhf 0.22.2.post1+dfsg-7 [1875 kB]
Get:135 http://172.17.0.1/private bullseye-staging/main armhf python3-sklearn all 0.22.2.post1+dfsg-7 [1785 kB]
Get:136 http://172.17.0.1/private bullseye-staging/main armhf sphinx-common all 2.4.3-2 [550 kB]
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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
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Updating certificates in /etc/ssl/certs...
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update-alternatives: using /usr/bin/bsd-write to provide /usr/bin/write (write) in auto mode
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update-alternatives: using /usr/bin/w.procps to provide /usr/bin/w (w) in auto mode
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Setting up libjs-jquery (3.5.1+dfsg-3) ...
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update-alternatives: using /usr/bin/automake-1.16 to provide /usr/bin/automake (automake) in auto mode
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Setting up python3-lockfile (1:0.12.2-2.2) ...
Setting up python3.7 (3.7.7-1+b1) ...
Setting up gettext (0.19.8.1-10) ...
Setting up python3-setuptools (46.1.3-1) ...
Setting up python3-py (1.8.1-2) ...
Setting up python3-backcall (0.1.0-2) ...
Setting up python3-joblib (0.14.0-3) ...
Setting up python3-parso (0.7.0-1) ...
Setting up python3-ipython-genutils (0.2.0-3) ...
Setting up python3-all (3.8.2-3) ...
Setting up python3-zipp (1.0.0-2) ...
Setting up man-db (2.9.1-1) ...
Not building database; man-db/auto-update is not 'true'.
Setting up intltool-debian (0.35.0+20060710.5) ...
Setting up python3-pygments (2.3.1+dfsg-3) ...
Setting up python3-packaging (20.3-1.2) ...
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Setting up libpython3.8-dev:armhf (3.8.3-1) ...
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Setting up python3-requests (2.23.0+dfsg-2) ...
Setting up python3-kiwisolver (1.0.1-3) ...
Setting up python3-numpy (1:1.17.4-5+b1) ...
Setting up libjs-sphinxdoc (2.4.3-2) ...
Setting up python3.8-dev (3.8.3-1) ...
Setting up python3-hdmedians (0.13~git20171027.8e0e9e3-1+b2) ...
Setting up python3-traitlets (4.3.3-3) ...
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Setting up python3-jedi (0.17.0-1) ...
Setting up po-debconf (1.0.21) ...
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Setting up python3-sklearn-lib:armhf (0.22.2.post1+dfsg-7) ...
Setting up python3-pandas (0.25.3+dfsg2-2+rpi1) ...
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Setting up python3-dev (3.8.2-3) ...
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Setting up python3-pluggy (0.13.0-4) ...
Setting up python3-all-dev (3.8.2-3) ...
Setting up python3-pytest (4.6.9-3) ...
Setting up dh-autoreconf (19) ...
Setting up debhelper (13) ...
Setting up sbuild-build-depends-python-skbio-dummy (0.invalid.0) ...
Processing triggers for libc-bin (2.30-8+rpi1) ...
Processing triggers for ca-certificates (20190110) ...
Updating certificates in /etc/ssl/certs...
0 added, 0 removed; done.
Running hooks in /etc/ca-certificates/update.d...
done.

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

Kernel: Linux 4.19.20-v7+ armhf (armv7l)
Toolchain package versions: binutils_2.34-8+rpi1 dpkg-dev_1.19.7 g++-9_9.3.0-12+rpi1 gcc-9_9.3.0-12+rpi1 libc6-dev_2.30-8+rpi1 libstdc++-9-dev_9.3.0-12+rpi1 libstdc++6_10.1.0-1+rpi1 linux-libc-dev_5.2.17-1+rpi1+b2
Package versions: adduser_3.118 apt_2.1.4 autoconf_2.69-11.1 automake_1:1.16.2-1 autopoint_0.19.8.1-10 autotools-dev_20180224.1 base-files_11+rpi1 base-passwd_3.5.47 bash_5.0-6 binutils_2.34-8+rpi1 binutils-arm-linux-gnueabihf_2.34-8+rpi1 binutils-common_2.34-8+rpi1 bsdmainutils_11.1.2 bsdutils_1:2.35.1-5 build-essential_12.8 bzip2_1.0.8-2 ca-certificates_20190110 coreutils_8.30-3 cpp_4:9.2.1-3.1+rpi1 cpp-9_9.3.0-12+rpi1 cython3_0.29.14-1 dash_0.5.10.2-7 debconf_1.5.74 debhelper_13 debianutils_4.9.1 dh-autoreconf_19 dh-python_4.20200315 dh-strip-nondeterminism_1.8.0-1 diffutils_1:3.7-3 dirmngr_2.2.20-1 dpkg_1.19.7 dpkg-dev_1.19.7 dwz_0.13-5 e2fsprogs_1.45.6-1 fakeroot_1.24-1 fdisk_2.35.1-5 file_1:5.38-5 findutils_4.7.0-1 fonts-lyx_2.3.4.2-2 g++_4:9.2.1-3.1+rpi1 g++-9_9.3.0-12+rpi1 gcc_4:9.2.1-3.1+rpi1 gcc-10-base_10.1.0-1+rpi1 gcc-9_9.3.0-12+rpi1 gcc-9-base_9.3.0-12+rpi1 gettext_0.19.8.1-10 gettext-base_0.19.8.1-10 gnupg_2.2.20-1 gnupg-l10n_2.2.20-1 gnupg-utils_2.2.20-1 gpg_2.2.20-1 gpg-agent_2.2.20-1 gpg-wks-client_2.2.20-1 gpg-wks-server_2.2.20-1 gpgconf_2.2.20-1 gpgsm_2.2.20-1 gpgv_2.2.20-1 grep_3.4-1 groff-base_1.22.4-5 gzip_1.10-2 hostname_3.23 init-system-helpers_1.57 intltool-debian_0.35.0+20060710.5 iputils-ping_3:20190709-3 libacl1_2.2.53-8 libapt-pkg6.0_2.1.4 libarchive-zip-perl_1.68-1 libasan5_9.3.0-12+rpi1 libassuan0_2.5.3-7 libatomic1_10.1.0-1+rpi1 libattr1_1:2.4.48-5 libaudit-common_1:2.8.5-3 libaudit1_1:2.8.5-3 libbinutils_2.34-8+rpi1 libblas3_3.9.0-2 libblkid1_2.35.1-5 libbsd0_0.10.0-1 libbz2-1.0_1.0.8-2 libc-bin_2.30-8+rpi1 libc-dev-bin_2.30-8+rpi1 libc6_2.30-8+rpi1 libc6-dev_2.30-8+rpi1 libcap-ng0_0.7.9-2.1+b1 libcap2_1:2.34-2 libcap2-bin_1:2.34-2 libcc1-0_10.1.0-1+rpi1 libcom-err2_1.45.6-1 libcroco3_0.6.13-1 libcrypt-dev_1:4.4.16-1 libcrypt1_1:4.4.16-1 libctf-nobfd0_2.34-8+rpi1 libctf0_2.34-8+rpi1 libdb5.3_5.3.28+dfsg1-0.6 libdebconfclient0_0.252 libdebhelper-perl_13 libdpkg-perl_1.19.7 libelf1_0.176-1.1 libexpat1_2.2.9-1 libexpat1-dev_2.2.9-1 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libpam-modules_1.3.1-5 libpam-modules-bin_1.3.1-5 libpam-runtime_1.3.1-5 libpam0g_1.3.1-5 libpcre2-8-0_10.34-7 libpcre3_2:8.39-12 libperl5.28_5.28.1-6 libperl5.30_5.30.0-10 libpipeline1_1.5.2-2 libpng16-16_1.6.37-2 libprocps8_2:3.3.16-5 libpython3-all-dev_3.8.2-3 libpython3-dev_3.8.2-3 libpython3-stdlib_3.8.2-3 libpython3.7-minimal_3.7.7-1+b1 libpython3.7-stdlib_3.7.7-1+b1 libpython3.8_3.8.3-1 libpython3.8-dev_3.8.3-1 libpython3.8-minimal_3.8.3-1 libpython3.8-stdlib_3.8.3-1 libreadline7_7.0-5 libreadline8_8.0-4 libsasl2-2_2.1.27+dfsg-2 libsasl2-modules-db_2.1.27+dfsg-2 libseccomp2_2.4.3-1+rpi1 libselinux1_3.0-1+b1 libsemanage-common_3.0-1 libsemanage1_3.0-1+b1 libsepol1_3.0-1 libsigsegv2_2.12-2 libsimde-dev_0.0.0.git.20200424-1 libsmartcols1_2.35.1-5 libsqlite3-0_3.31.1-5 libss2_1.45.6-1 libssl1.1_1.1.1g-1 libssw-dev_1.1-9 libssw0_1.1-9 libstdc++-9-dev_9.3.0-12+rpi1 libstdc++6_10.1.0-1+rpi1 libsub-override-perl_0.09-2 libsystemd0_245.5-3+rpi1 libtasn1-6_4.16.0-2 libtinfo5_6.2-1 libtinfo6_6.2-1 libtool_2.4.6-14 libubsan1_10.1.0-1+rpi1 libuchardet0_0.0.6-3 libudev1_245.5-3+rpi1 libunistring2_0.9.10-2 libuuid1_2.35.1-5 libxml2_2.9.10+dfsg-5 libzstd1_1.4.4+dfsg-3+rpi1 linux-libc-dev_5.2.17-1+rpi1+b2 login_1:4.8.1-1 logsave_1.45.6-1 lsb-base_11.1.0+rpi1 m4_1.4.18-4 make_4.2.1-2 man-db_2.9.1-1 mawk_1.3.4.20200120-2 mime-support_3.64 mount_2.35.1-5 ncurses-base_6.2-1 ncurses-bin_6.2-1 node-jquery_3.5.1+dfsg-3 openssl_1.1.1g-1 passwd_1:4.8.1-1 patch_2.7.6-6 perl_5.30.0-10 perl-base_5.30.0-10 perl-modules-5.28_5.28.1-6 perl-modules-5.30_5.30.0-10 pinentry-curses_1.1.0-4 po-debconf_1.0.21 procps_2:3.3.16-5 python-matplotlib-data_3.2.1-1 python3_3.8.2-3 python3-all_3.8.2-3 python3-all-dev_3.8.2-3 python3-atomicwrites_1.3.0-1 python3-attr_19.3.0-4 python3-backcall_0.1.0-2 python3-cachecontrol_0.12.6-1 python3-certifi_2020.4.5.1-1 python3-chardet_3.0.4-7 python3-cycler_0.10.0-3 python3-dateutil_2.8.1-4 python3-decorator_4.4.2-2 python3-dev_3.8.2-3 python3-distutils_3.8.2-2 python3-hdmedians_0.13~git20171027.8e0e9e3-1+b2 python3-idna_2.9-1 python3-importlib-metadata_1.6.0-1 python3-ipython_7.14.0-1 python3-ipython-genutils_0.2.0-3 python3-jedi_0.17.0-1 python3-joblib_0.14.0-3 python3-kiwisolver_1.0.1-3 python3-lib2to3_3.8.2-2 python3-lockfile_1:0.12.2-2.2 python3-matplotlib_3.2.1-1 python3-minimal_3.8.2-3 python3-more-itertools_4.2.0-2 python3-msgpack_0.6.2-1 python3-natsort_7.0.1-1 python3-numpy_1:1.17.4-5+b1 python3-packaging_20.3-1.2 python3-pandas_0.25.3+dfsg2-2+rpi1 python3-pandas-lib_0.25.3+dfsg2-2+rpi1 python3-parso_0.7.0-1 python3-pexpect_4.6.0-3 python3-pickleshare_0.7.5-2 python3-pkg-resources_46.1.3-1 python3-pluggy_0.13.0-4 python3-prompt-toolkit_3.0.5-2 python3-ptyprocess_0.6.0-2 python3-py_1.8.1-2 python3-pygments_2.3.1+dfsg-3 python3-pyparsing_2.4.7-1 python3-pytest_4.6.9-3 python3-requests_2.23.0+dfsg-2 python3-scipy_1.4.1-2 python3-setuptools_46.1.3-1 python3-six_1.14.0-3 python3-sklearn_0.22.2.post1+dfsg-7 python3-sklearn-lib_0.22.2.post1+dfsg-7 python3-traitlets_4.3.3-3 python3-tz_2019.3-2 python3-urllib3_1.25.9-1 python3-wcwidth_0.1.9+dfsg1-1 python3-zipp_1.0.0-2 python3.7_3.7.7-1+b1 python3.7-minimal_3.7.7-1+b1 python3.8_3.8.3-1 python3.8-dev_3.8.3-1 python3.8-minimal_3.8.3-1 raspbian-archive-keyring_20120528.2 readline-common_8.0-4 sbuild-build-depends-core-dummy_0.invalid.0 sbuild-build-depends-python-skbio-dummy_0.invalid.0 sed_4.7-1 sensible-utils_0.0.12+nmu1 sphinx-common_2.4.3-2 sysvinit-utils_2.96-3 tar_1.30+dfsg-7 ttf-bitstream-vera_1.10-8 tzdata_2020a-1 util-linux_2.35.1-5 xz-utils_5.2.4-1 zlib1g_1:1.2.11.dfsg-2 zlib1g-dev_1:1.2.11.dfsg-2

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


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

gpgv: unknown type of key resource 'trustedkeys.kbx'
gpgv: keyblock resource '/sbuild-nonexistent/.gnupg/trustedkeys.kbx': General error
gpgv: Signature made Sun Apr 19 15:38:21 2020 UTC
gpgv:                using RSA key 724D609337113C710550D7473C26763F6C67E6E2
gpgv: Can't check signature: No public key
dpkg-source: warning: failed to verify signature on ./python-skbio_0.5.6-2.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_0.5.6-2.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 ignore_failing_patches.patch
dpkg-source: info: applying simde
dpkg-source: info: applying soften_test

Check disk space
----------------

Sufficient free space for build

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

APT_CONFIG=/var/lib/sbuild/apt.conf
DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/112/bus
DEB_BUILD_OPTIONS=parallel=4
HOME=/sbuild-nonexistent
INVOCATION_ID=d540cc9aba1e46398de3fa2f71b5e534
JOURNAL_STREAM=8:18935
LANG=en_GB.UTF-8
LC_ALL=POSIX
LOGNAME=buildd
PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games
PWD=/
SCHROOT_ALIAS_NAME=bullseye-staging-armhf-sbuild
SCHROOT_CHROOT_NAME=bullseye-staging-armhf-sbuild
SCHROOT_COMMAND=env
SCHROOT_GID=116
SCHROOT_GROUP=buildd
SCHROOT_SESSION_ID=bullseye-staging-armhf-sbuild-e70f12b0-079c-456c-83d2-a1490ecafc1b
SCHROOT_UID=112
SCHROOT_USER=buildd
SHELL=/bin/sh
USER=buildd
XDG_RUNTIME_DIR=/run/user/112
XDG_SESSION_ID=c1623

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

dpkg-buildpackage: info: source package python-skbio
dpkg-buildpackage: info: source version 0.5.6-2
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
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
   dh_auto_configure -a -O--buildsystem=pybuild
I: pybuild base:217: python3.8 setup.py config 
running config
   debian/rules override_dh_auto_build-arch
make[1]: Entering directory '/<<PKGBUILDDIR>>'
dh_auto_build
I: pybuild base:217: /usr/bin/python3 setup.py build 
running build
running build_py
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/benchmarks
copying benchmarks/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/benchmarks
copying benchmarks/benchmarks.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/benchmarks
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio
copying skbio/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio
copying skbio/_base.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio
copying skbio/test.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio
copying skbio/workflow.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment
copying skbio/alignment/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment
copying skbio/alignment/_indexing.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment
copying skbio/alignment/_pairwise.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment
copying skbio/alignment/_repr.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment
copying skbio/alignment/_tabular_msa.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/diversity
copying skbio/diversity/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/diversity
copying skbio/diversity/_block.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/diversity
copying skbio/diversity/_driver.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/diversity
copying skbio/diversity/_util.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/diversity
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
copying skbio/io/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
copying skbio/io/_exception.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
copying skbio/io/_fileobject.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
copying skbio/io/_iosources.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
copying skbio/io/_warning.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
copying skbio/io/registry.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
copying skbio/io/util.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/metadata
copying skbio/metadata/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/metadata
copying skbio/metadata/_interval.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/metadata
copying skbio/metadata/_mixin.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/metadata
copying skbio/metadata/_repr.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/metadata
copying skbio/metadata/_testing.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/metadata
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_dna.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_genetic_code.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_grammared_sequence.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_nucleotide_mixin.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_protein.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_repr.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_rna.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/_sequence.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
copying skbio/sequence/distance.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats
copying skbio/stats/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats
copying skbio/stats/_misc.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats
copying skbio/stats/_subsample.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats
copying skbio/stats/composition.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats
copying skbio/stats/gradient.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats
copying skbio/stats/power.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tests
copying skbio/tests/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tests
copying skbio/tests/test_base.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tests
copying skbio/tests/test_workflow.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tests
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tree
copying skbio/tree/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tree
copying skbio/tree/_exception.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tree
copying skbio/tree/_majority_rule.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tree
copying skbio/tree/_nj.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tree
copying skbio/tree/_tree.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/tree
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util
copying skbio/util/__init__.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util
copying skbio/util/_decorator.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util
copying skbio/util/_exception.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util
copying skbio/util/_misc.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util
copying skbio/util/_testing.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util
copying skbio/util/_warning.py -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util
creating /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment/_lib
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copying skbio/stats/ordination/tests/data/PCoA_sample_data_3_fsvd_ref_3dim -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/PCoA_skbio -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_X -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_Y -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_biplot_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_biplot_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_sample_constraints_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_sample_constraints_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_site_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_site_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_species_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example2_species_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_X -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_Y -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_biplot_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_biplot_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_sample_constraints_scaling1 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_sample_constraints_scaling2 -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_site_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_site_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_species_scaling1_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/example3_species_scaling2_from_vegan -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/exp_PCoAEigenResults_site -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/exp_PCoAzeros_site -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_biplot_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_constraints_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_eigvals_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_propexpl_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_sites_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/vare_rda_species_from_vegan.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/varechem.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
copying skbio/stats/ordination/tests/data/varespec.csv -> /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/tests/data
running build_ext
building 'skbio.metadata._intersection' extension
creating build
creating build/temp.linux-armhf-3.8
creating build/temp.linux-armhf-3.8/skbio
creating build/temp.linux-armhf-3.8/skbio/metadata
arm-linux-gnueabihf-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -fdebug-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/python3.8 -c skbio/metadata/_intersection.c -o build/temp.linux-armhf-3.8/skbio/metadata/_intersection.o -DSIMDE_ENABLE_OPENMP -fopenmp-simd -O3
arm-linux-gnueabihf-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -fdebug-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-3.8/skbio/metadata/_intersection.o -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/metadata/_intersection.cpython-38-arm-linux-gnueabihf.so
building 'skbio.stats.__subsample' extension
creating build/temp.linux-armhf-3.8/skbio/stats
arm-linux-gnueabihf-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -fdebug-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/python3.8 -c skbio/stats/__subsample.c -o build/temp.linux-armhf-3.8/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:1830,
                 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:617:
/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 -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -fdebug-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-3.8/skbio/stats/__subsample.o -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/__subsample.cpython-38-arm-linux-gnueabihf.so
building 'skbio.alignment._ssw_wrapper' extension
creating build/temp.linux-armhf-3.8/skbio/alignment
arm-linux-gnueabihf-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -fdebug-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/python3.8 -c skbio/alignment/_ssw_wrapper.c -o build/temp.linux-armhf-3.8/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:1830,
                 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:624:
/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 -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -fdebug-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-3.8/skbio/alignment/_ssw_wrapper.o -lssw -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment/_ssw_wrapper.cpython-38-arm-linux-gnueabihf.so
building 'skbio.diversity._phylogenetic' extension
creating build/temp.linux-armhf-3.8/skbio/diversity
arm-linux-gnueabihf-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -O2 -fdebug-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/python3.8 -c skbio/diversity/_phylogenetic.c -o build/temp.linux-armhf-3.8/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:1830,
                 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:617:
/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 -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fwrapv -O2 -Wl,-z,relro -Wl,-z,now -g -O2 -fdebug-prefix-map=/<<PKGBUILDDIR>>=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armhf-3.8/skbio/diversity/_phylogenetic.o -o /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/diversity/_phylogenetic.cpython-38-arm-linux-gnueabihf.so
make[1]: Leaving directory '/<<PKGBUILDDIR>>'
   dh_auto_test -a -O--buildsystem=pybuild
I: pybuild base:217: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build; python3.8 -m pytest 
============================= test session starts ==============================
platform linux -- Python 3.8.3, pytest-4.6.9, py-1.8.1, pluggy-0.13.0
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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................................F....................................... [ 12%]
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........................................................................ [ 18%]
............                                                             [ 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...FFF....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%]
........................................................................ [ 49%]
................................                                         [ 51%]
skbio/metadata/tests/test_intersection.py ...............                [ 51%]
skbio/metadata/tests/test_interval.py .................................. [ 53%]
.....................................                                    [ 54%]
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%]
..........F............................................................. [ 69%]
....................F...................F...F..F.F.F...F.........s...... [ 72%]
....F........................................................F...F...... [ 75%]
...s...                                                                  [ 75%]
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%]
.sssss                                                                   [ 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 ...................s.................... [ 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:1659: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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 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 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 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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:126: 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)
    
        # array of empty vector
        actual = alpha_diversity('observed_otus',
                                 np.array([[]], dtype=np.int64))
        expected = pd.Series([0])
        assert_series_almost_equal(actual, expected)
    
        # array of empty vectors
        actual = alpha_diversity('observed_otus',
                                 np.array([[], []], dtype=np.int64))
        expected = pd.Series([0, 0])
        assert_series_almost_equal(actual, expected)
    
        # empty vector
>       actual = alpha_diversity('faith_pd', np.array([], dtype=np.int64),
                                 tree=self.tree1, otu_ids=[])

skbio/diversity/tests/test_driver.py:175: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
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:126: 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_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:126: 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:126: 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:126: 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_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:126: 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)
    
>       actual = alpha_diversity('faith_pd', np.array([1, 3, 0, 1, 0]),
                                 tree=self.tree1, otu_ids=self.oids1)

skbio/diversity/tests/test_driver.py:199: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
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:126: 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_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:126: 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:126: 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:126: 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:126: 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:126: 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: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
<decorator-gen-120>:2: in partial_beta_diversity
    ???
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:126: 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: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
<decorator-gen-120>:2: in partial_beta_diversity
    ???
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:126: 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: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
<decorator-gen-120>:2: in partial_beta_diversity
    ???
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:126: 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:126: 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 0xaeaf1e50> != <skbi[60 chars]p.<locals>.ExamplePositionalMetadataMixin object at 0xaeaf10d0>
_______________________ 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:623: 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:933: 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:690: 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:854: 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:888: 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:748: 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:804: 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:1037: 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:1057: 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:716: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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)

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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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

    @experimental(as_of="0.4.0")
    def assert_data_frame_almost_equal(left, right):
        """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_less_precise=False,
                               check_names=True,
                               by_blocks=False,
                               check_exact=False)
E       AssertionError: Attributes are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

skbio/util/_testing.py:285: 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/sequence/_sequence.py:28
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence/_sequence.py:28: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.9 it will stop working
    collections.Sequence, SkbioObject):

skbio/io/format/gff3.py:217
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io/format/gff3.py:217: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.9 it will stop working
    from collections import Iterable

skbio/util/_exception.py:10
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/util/_exception.py:10: PytestCollectionWarning: cannot collect test class 'TestingUtilError' because it has a __init__ constructor (from: .pybuild/cpython3_3.8_skbio/build/skbio/io/tests/test_registry.py)
    class TestingUtilError(Exception):

skbio/sequence/tests/test_sequence.py:14
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_sequence.py:14: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.9 it will stop working
    from collections import Hashable

.pybuild/cpython3_3.8_skbio/build/skbio/alignment/tests/test_tabular_msa.py::TestLoc::test_bad_fancy_index
.pybuild/cpython3_3.8_skbio/build/skbio/alignment/tests/test_tabular_msa.py::TestLoc::test_fancy_index_missing_label
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment/_tabular_msa.py:1192: FutureWarning: 
  Passing list-likes to .loc or [] with any missing label will raise
  KeyError in the future, you can use .reindex() as an alternative.
  
  See the documentation here:
  https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike
    new_seqs = self._seqs.loc[l]

.pybuild/cpython3_3.8_skbio/build/skbio/alignment/tests/test_tabular_msa.py::TestGapFrequencies::test_no_positions_relative
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/alignment/_tabular_msa.py:1653: RuntimeWarning: invalid value encountered in true_divide
    gap_freqs /= length

.pybuild/cpython3_3.8_skbio/build/skbio/diversity/alpha/tests/test_base.py::BaseTests::test_pielou_e
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/diversity/alpha/tests/test_base.py::BaseTests::test_pielou_e
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/diversity/alpha/_base.py:869: RuntimeWarning: invalid value encountered in true_divide
    freqs = counts / counts.sum()

.pybuild/cpython3_3.8_skbio/build/skbio/diversity/alpha/tests/test_base.py::BaseTests::test_pielou_e
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/diversity/tests/test_block.py::ParallelBetaDiversity::test_block_compute
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_duplicate_pairs
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_duplicate_transpose_pairs
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_euclidean
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_id_pairs_as_iterable
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_pairs_not_subset
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_self_self_pair
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_unusable_metric
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_unweighted_unifrac_partial
.pybuild/cpython3_3.8_skbio/build/skbio/diversity/tests/test_driver.py::TestPartialBetaDiversity::test_weighted_unifrac_partial_full
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/diversity/tests/test_driver.py::BetaDiversityTests::test_qualitative_bug_issue_1549
  /usr/lib/python3/dist-packages/sklearn/metrics/pairwise.py:1738: DataConversionWarning: Data was converted to boolean for metric jaccard
    warnings.warn(msg, DataConversionWarning)

.pybuild/cpython3_3.8_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:279: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
    return imag(x) == 0

.pybuild/cpython3_3.8_skbio/build/skbio/io/format/tests/test_fasta.py::SnifferTests::test_positives
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/io/registry.py", line 914, in wrapped_sniffer
      return sniffer(fh)
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io/format/fasta.py", line 648, in _fasta_sniffer
      for _ in zip(range(num_records), parser):
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io/format/fasta.py", line 868, in _parse_fasta_raw
      yield data_parser(data_chunks), id_, desc
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io/format/fasta.py", line 662, in _sniffer_data_parser
      _parse_quality_scores(chunks)
    File "/<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/io/format/fasta.py", line 883, in _parse_quality_scores
      quality = np.asarray(qual_str.split(), dtype=int)
    File "/usr/lib/python3/dist-packages/numpy/core/_asarray.py", line 85, in asarray
      return array(a, dtype, copy=False, order=order)
  OverflowError: Python int too large to convert to C long
  
    warn("'%s' has encountered a problem.\nPlease"

.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_has_nondegenerates
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_nucleotide_sequences.py::TestNucleotideSequence::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_protein.py::TestProtein::test_nondegenerate_chars
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_protein.py::TestProtein::test_nondegenerate_chars
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
.pybuild/cpython3_3.8_skbio/build/skbio/sequence/tests/test_grammared_sequence.py::TestGrammaredSequence::test_nondegenerates
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/sequence/tests/test_sequence.py::TestSequence::test_frequencies_empty_sequence
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/sequence/_sequence.py:1819: RuntimeWarning: invalid value encountered in true_divide
    obs_counts = obs_counts / len(self)

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_pearson
  /usr/lib/python3/dist-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.
    warnings.warn(PearsonRConstantInputWarning())

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_spearman
  /usr/lib/python3/dist-packages/numpy/lib/function_base.py:2534: RuntimeWarning: invalid value encountered in true_divide
    c /= stddev[:, None]

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_spearman
  /usr/lib/python3/dist-packages/numpy/lib/function_base.py:2535: RuntimeWarning: invalid value encountered in true_divide
    c /= stddev[None, :]

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_spearman
  /usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater
    return (a < x) & (x < b)

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_spearman
  /usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less
    return (a < x) & (x < b)

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_mantel.py::MantelTests::test_no_variation_spearman
  /usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal
    cond2 = cond0 & (x <= _a)

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_eq_groups
.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_no_permuations
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_mixedgroups
.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_uneq_groups
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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 -15.629533932395208 and the largest is 20.90315427793891.
    warn(

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_null
.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_not_distance_matrix
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/ordination/_principal_coordinate_analysis.py:188: RuntimeWarning: invalid value encountered in true_divide
    proportion_explained = eigvals / sum_eigenvalues

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_centroids_null
  /usr/lib/python3/dist-packages/scipy/stats/stats.py:3349: RuntimeWarning: invalid value encountered in true_divide
    f = msb / msw

.pybuild/cpython3_3.8_skbio/build/skbio/stats/distance/tests/test_permdisp.py::testPERMDISP::test_confirm_betadispr_results
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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 -0.007758325147780592 and the largest is 2.7077929489469255.
    warn(

.pybuild/cpython3_3.8_skbio/build/skbio/stats/tests/test_composition.py::AncomTests::test_ancom_no_signal
  /usr/lib/python3/dist-packages/scipy/stats/stats.py:3349: RuntimeWarning: invalid value encountered in double_scalars
    f = msb / msw

.pybuild/cpython3_3.8_skbio/build/skbio/stats/tests/test_composition.py::AncomTests::test_ancom_no_signal
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build/skbio/stats/composition.py:1113: RuntimeWarning: invalid value encountered in less
    W = (logratio_mat < alpha).sum(axis=1)

.pybuild/cpython3_3.8_skbio/build/skbio/util/tests/test_decorator.py::TestDeprecated::test_function_output
  /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_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/latest/warnings.html
====== 86 failed, 2228 passed, 38 skipped, 63 warnings in 473.55 seconds =======
E: pybuild pybuild:352: test: plugin distutils failed with: exit code=1: cd /<<PKGBUILDDIR>>/.pybuild/cpython3_3.8_skbio/build; python3.8 -m pytest 
dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p 3.8 returned exit code 13
make: *** [debian/rules:13: binary-arch] Error 25
dpkg-buildpackage: error: debian/rules binary-arch subprocess returned exit status 2
--------------------------------------------------------------------------------
Build finished at 2020-05-22T06:10:42Z

Finished
--------


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

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

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

Build Architecture: armhf
Build Type: any
Build-Space: 31328
Build-Time: 572
Distribution: bullseye-staging
Fail-Stage: build
Host Architecture: armhf
Install-Time: 1201
Job: python-skbio_0.5.6-2
Machine Architecture: armhf
Package: python-skbio
Package-Time: 1844
Source-Version: 0.5.6-2
Space: 31328
Status: attempted
Version: 0.5.6-2
--------------------------------------------------------------------------------
Finished at 2020-05-22T06:10:42Z
Build needed 00:30:44, 31328k disk space