Fri Oct 11 13:22:46 UTC 2024 I: starting to build python-mne/trixie/armhf on jenkins on '2024-10-11 13:22' Fri Oct 11 13:22:46 UTC 2024 I: The jenkins build log is/was available at https://jenkins.debian.net/userContent/reproducible/debian/build_service/armhf_12/8729/console.log Fri Oct 11 13:22:46 UTC 2024 I: Downloading source for trixie/python-mne=1.8.0-1 --2024-10-11 13:22:46-- http://deb.debian.org/debian/pool/main/p/python-mne/python-mne_1.8.0-1.dsc Connecting to 46.16.76.132:3128... connected. Proxy request sent, awaiting response... 200 OK Length: 2756 (2.7K) [text/prs.lines.tag] Saving to: ‘python-mne_1.8.0-1.dsc’ 0K .. 100% 420M=0s 2024-10-11 13:22:46 (420 MB/s) - ‘python-mne_1.8.0-1.dsc’ saved [2756/2756] Fri Oct 11 13:22:46 UTC 2024 I: python-mne_1.8.0-1.dsc -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA512 Format: 3.0 (quilt) Source: python-mne Binary: python3-mne Architecture: all Version: 1.8.0-1 Maintainer: Debian Med Packaging Team Uploaders: Yaroslav Halchenko , Michael Hanke , Étienne Mollier Homepage: https://martinos.org/mne Standards-Version: 4.7.0 Vcs-Browser: https://salsa.debian.org/med-team/python-mne Vcs-Git: https://salsa.debian.org/med-team/python-mne.git Testsuite: autopkgtest-pkg-python Build-Depends: debhelper-compat (= 13), dh-sequence-python3, pybuild-plugin-pyproject, libgl1-mesa-dri, libjs-jquery, libjs-jquery-ui, python3-vtk9, python3-all, python3-coverage, python3-hatchling, python3-hatch-vcs, python3-joblib, python3-lazy-loader, python3-matplotlib, python3-nibabel, python3-numpy, python3-pooch , python3-pyqt6 , python3-pytest, python3-pytestqt , python3-pytest-cov, python3-pytest-timeout , python3-scipy, python3-setuptools, python3-sklearn, python3-tqdm , python3-tk, python3-sphinx, xauth, xvfb, yui-compressor Package-List: python3-mne deb python optional arch=all Checksums-Sha1: ee145793b440dd7dbedc7152593a830e118261e5 48096188 python-mne_1.8.0.orig.tar.xz 92fc770014021dc01830de7afabf4b134de46270 96620 python-mne_1.8.0-1.debian.tar.xz Checksums-Sha256: 92da80ac05c76be7203e8887880a4eafa720246bfed65dc484cef98c8f25d849 48096188 python-mne_1.8.0.orig.tar.xz 9ef9eb037380a980321bdceb034d2f50a079d465bf519b5d094f8c2c9917e5ef 96620 python-mne_1.8.0-1.debian.tar.xz Files: e34720648c2f53977d5adad672044950 48096188 python-mne_1.8.0.orig.tar.xz 500aa1b33b14b5c1f238cbb44582797b 96620 python-mne_1.8.0-1.debian.tar.xz Dgit: 605409808a31f71c71909ab7dad724df485ec7b0 debian archive/debian/1.8.0-1 https://git.dgit.debian.org/python-mne -----BEGIN PGP SIGNATURE----- iQJIBAEBCgAyFiEEj5GyJ8fW8rGUjII2eTz2fo8NEdoFAmbIVPgUHGVtb2xsaWVy QGRlYmlhbi5vcmcACgkQeTz2fo8NEdo41Q//U0Qi2K7YOmwqxGMhA4QA8d1YjQz8 9b/Vxy4xV88++Esd4YxWVvuZIp4GZ7CgwxGJV8nJ/P241yt1n7eRNaa4aK0x1Es/ XpzUcLzAmbNEe/uKEqmSgfTi0Xn1Cqk5qvbbHCuew0degmvQnlwQaLGJVvoQ+ta6 lYuhZ7ZPK3GEtDX2cBwCd4fM20y7coFMQhxY7VyaNEJvaF6ZgDy6nLtbz6G6t3+S a2bhpzOAtO6TqIo8TMuAypURiUP+Xx26khelYc0R0n+Vq/voKythZZS50i8jFscT 5ng+OOko0UI2IAlcMwr34vXA/UVzUbRzwAHyXoKHbaJ9orRPgb8TplrEV2gDRNjv RvyR2a3+CVa38Rf/84d8NOiHL0dfwBabtLqTzl/VbBaGkuxCkBp37TsuKEX2JDTs R1KTodgs+bQMTSlEjWihtS/yLF9Nqp/iuFavY9YQQRjuw6+hjonyfGeyy1LMfPGR r23B7YokqdEj/E2CfqCU2yOEdYeA1lcZF3WxzUM37TEDp2d9nHREoXbFsq9OI31N Tmmzr2HCTuIltYO6Qfe/oq9aJSs8YEGCJzMYxHoMAew0NB48cPNTioqC0KCi9iGA cPXMMvSVtdZpcA+9Gwh4mQYEBycwI/8TxMsh5LUlDsg/cM900jepWrLoj3E614HH c//ptNfKFYi50Pw= =giuN -----END PGP SIGNATURE----- Fri Oct 11 13:22:46 UTC 2024 I: Checking whether the package is not for us Fri Oct 11 13:22:46 UTC 2024 I: Starting 1st build on remote node virt64a-armhf-rb.debian.net. Fri Oct 11 13:22:46 UTC 2024 I: Preparing to do remote build '1' on virt64a-armhf-rb.debian.net. Fri Oct 11 14:31:42 UTC 2024 I: Deleting $TMPDIR on virt64a-armhf-rb.debian.net. I: pbuilder: network access will be disabled during build I: Current time: Fri Oct 11 01:22:54 -12 2024 I: pbuilder-time-stamp: 1728652974 I: Building the build Environment I: extracting base tarball [/var/cache/pbuilder/trixie-reproducible-base.tgz] I: copying local configuration W: --override-config is not set; not updating apt.conf Read the manpage for details. I: mounting /proc filesystem I: mounting /sys filesystem I: creating /{dev,run}/shm I: mounting /dev/pts filesystem I: redirecting /dev/ptmx to /dev/pts/ptmx I: policy-rc.d already exists I: Copying source file I: copying [python-mne_1.8.0-1.dsc] I: copying [./python-mne_1.8.0.orig.tar.xz] I: copying [./python-mne_1.8.0-1.debian.tar.xz] I: Extracting source gpgv: Signature made Fri Aug 23 09:23:04 2024 gpgv: using RSA key 8F91B227C7D6F2B1948C8236793CF67E8F0D11DA gpgv: issuer "emollier@debian.org" gpgv: Can't check signature: No public key dpkg-source: warning: cannot verify inline signature for ./python-mne_1.8.0-1.dsc: no acceptable signature found dpkg-source: info: extracting python-mne in python-mne-1.8.0 dpkg-source: info: unpacking python-mne_1.8.0.orig.tar.xz dpkg-source: info: unpacking python-mne_1.8.0-1.debian.tar.xz I: Not using root during the build. I: Installing the build-deps I: user script /srv/workspace/pbuilder/16195/tmp/hooks/D02_print_environment starting I: set BUILDDIR='/build/reproducible-path' BUILDUSERGECOS='first user,first room,first work-phone,first home-phone,first other' BUILDUSERNAME='pbuilder1' BUILD_ARCH='armhf' DEBIAN_FRONTEND='noninteractive' DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=3 ' DISTRIBUTION='trixie' HOME='/root' HOST_ARCH='armhf' IFS=' ' INVOCATION_ID='1026586b0a60428fab198adf76ec2497' LANG='C' LANGUAGE='en_US:en' LC_ALL='C' MAIL='/var/mail/root' OPTIND='1' PATH='/usr/sbin:/usr/bin:/sbin:/bin:/usr/games' PBCURRENTCOMMANDLINEOPERATION='build' PBUILDER_OPERATION='build' PBUILDER_PKGDATADIR='/usr/share/pbuilder' PBUILDER_PKGLIBDIR='/usr/lib/pbuilder' PBUILDER_SYSCONFDIR='/etc' PPID='16195' PS1='# ' PS2='> ' PS4='+ ' PWD='/' SHELL='/bin/bash' SHLVL='2' SUDO_COMMAND='/usr/bin/timeout -k 18.1h 18h /usr/bin/ionice -c 3 /usr/bin/nice /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.dqA0r5O5/pbuilderrc_xAJb --distribution trixie --hookdir /etc/pbuilder/first-build-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/trixie-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.dqA0r5O5/b1 --logfile b1/build.log python-mne_1.8.0-1.dsc' SUDO_GID='114' SUDO_UID='108' SUDO_USER='jenkins' TERM='unknown' TZ='/usr/share/zoneinfo/Etc/GMT+12' USER='root' _='/usr/bin/systemd-run' http_proxy='http://10.0.0.15:3142/' I: uname -a Linux virt64a 6.1.0-26-arm64 #1 SMP Debian 6.1.112-1 (2024-09-30) aarch64 GNU/Linux I: ls -l /bin lrwxrwxrwx 1 root root 7 Aug 4 21:30 /bin -> usr/bin I: user script /srv/workspace/pbuilder/16195/tmp/hooks/D02_print_environment finished -> Attempting to satisfy build-dependencies -> Creating pbuilder-satisfydepends-dummy package Package: pbuilder-satisfydepends-dummy Version: 0.invalid.0 Architecture: armhf Maintainer: Debian Pbuilder Team Description: Dummy package to satisfy dependencies with aptitude - created by pbuilder This package was created automatically by pbuilder to satisfy the build-dependencies of the package being currently built. Depends: debhelper-compat (= 13), dh-sequence-python3, pybuild-plugin-pyproject, libgl1-mesa-dri, libjs-jquery, libjs-jquery-ui, python3-vtk9, python3-all, python3-coverage, python3-hatchling, python3-hatch-vcs, python3-joblib, python3-lazy-loader, python3-matplotlib, python3-nibabel, python3-numpy, python3-pooch, python3-pyqt6, python3-pytest, python3-pytestqt, python3-pytest-cov, python3-pytest-timeout, python3-scipy, python3-setuptools, python3-sklearn, python3-tqdm, python3-tk, python3-sphinx, xauth, xvfb, yui-compressor dpkg-deb: building package 'pbuilder-satisfydepends-dummy' in '/tmp/satisfydepends-aptitude/pbuilder-satisfydepends-dummy.deb'. Selecting previously unselected package pbuilder-satisfydepends-dummy. (Reading database ... 19508 files and directories currently installed.) Preparing to unpack .../pbuilder-satisfydepends-dummy.deb ... Unpacking pbuilder-satisfydepends-dummy (0.invalid.0) ... dpkg: pbuilder-satisfydepends-dummy: dependency problems, but configuring anyway as you requested: pbuilder-satisfydepends-dummy depends on debhelper-compat (= 13); however: Package debhelper-compat is not installed. pbuilder-satisfydepends-dummy depends on dh-sequence-python3; however: Package dh-sequence-python3 is not installed. pbuilder-satisfydepends-dummy depends on pybuild-plugin-pyproject; however: Package pybuild-plugin-pyproject is not installed. pbuilder-satisfydepends-dummy depends on libgl1-mesa-dri; however: Package libgl1-mesa-dri is not installed. pbuilder-satisfydepends-dummy depends on libjs-jquery; however: Package libjs-jquery is not installed. pbuilder-satisfydepends-dummy depends on libjs-jquery-ui; however: Package libjs-jquery-ui is not installed. pbuilder-satisfydepends-dummy depends on python3-vtk9; however: Package python3-vtk9 is not installed. pbuilder-satisfydepends-dummy depends on python3-all; however: Package python3-all is not installed. pbuilder-satisfydepends-dummy depends on python3-coverage; however: Package python3-coverage is not installed. pbuilder-satisfydepends-dummy depends on python3-hatchling; however: Package python3-hatchling is not installed. pbuilder-satisfydepends-dummy depends on python3-hatch-vcs; however: Package python3-hatch-vcs is not installed. pbuilder-satisfydepends-dummy depends on python3-joblib; however: Package python3-joblib is not installed. pbuilder-satisfydepends-dummy depends on python3-lazy-loader; however: Package python3-lazy-loader is not installed. pbuilder-satisfydepends-dummy depends on python3-matplotlib; however: Package python3-matplotlib is not installed. pbuilder-satisfydepends-dummy depends on python3-nibabel; however: Package python3-nibabel is not installed. pbuilder-satisfydepends-dummy depends on python3-numpy; however: Package python3-numpy is not installed. pbuilder-satisfydepends-dummy depends on python3-pooch; however: Package python3-pooch is not installed. pbuilder-satisfydepends-dummy depends on python3-pyqt6; however: Package python3-pyqt6 is not installed. pbuilder-satisfydepends-dummy depends on python3-pytest; however: Package python3-pytest is not installed. pbuilder-satisfydepends-dummy depends on python3-pytestqt; however: Package python3-pytestqt is not installed. pbuilder-satisfydepends-dummy depends on python3-pytest-cov; however: Package python3-pytest-cov is not installed. pbuilder-satisfydepends-dummy depends on python3-pytest-timeout; however: Package python3-pytest-timeout is not installed. pbuilder-satisfydepends-dummy depends on python3-scipy; however: Package python3-scipy is not installed. pbuilder-satisfydepends-dummy depends on python3-setuptools; however: Package python3-setuptools is not installed. pbuilder-satisfydepends-dummy depends on python3-sklearn; however: Package python3-sklearn is not installed. pbuilder-satisfydepends-dummy depends on python3-tqdm; however: Package python3-tqdm is not installed. pbuilder-satisfydepends-dummy depends on python3-tk; however: Package python3-tk is not installed. pbuilder-satisfydepends-dummy depends on python3-sphinx; however: Package python3-sphinx is not installed. pbuilder-satisfydepends-dummy depends on xauth; however: Package xauth is not installed. pbuilder-satisfydepends-dummy depends on xvfb; however: Package xvfb is not installed. pbuilder-satisfydepends-dummy depends on yui-compressor; however: Package yui-compressor is not installed. Setting up pbuilder-satisfydepends-dummy (0.invalid.0) ... Reading package lists... Building dependency tree... Reading state information... Initializing package states... Writing extended state information... Building tag database... pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0) pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0) The following NEW packages will be installed: autoconf{a} automake{a} autopoint{a} autotools-dev{a} blt{a} bsdextrautils{a} ca-certificates{a} ca-certificates-java{a} debhelper{a} default-jre-headless{a} dh-autoreconf{a} dh-python{a} dh-strip-nondeterminism{a} docutils-common{a} dwz{a} file{a} fontconfig{a} fontconfig-config{a} fonts-dejavu-core{a} fonts-dejavu-mono{a} fonts-lyx{a} gettext{a} gettext-base{a} groff-base{a} hwloc-nox{a} intltool-debian{a} java-common{a} java-wrappers{a} libaec0{a} libarchive-zip-perl{a} libavahi-client3{a} libavahi-common-data{a} libavahi-common3{a} libb2-1{a} libblas3{a} libbrotli1{a} libcairo2{a} libcom-err2{a} libcups2t64{a} libcurl3t64-gnutls{a} libcurl4t64{a} libdbus-1-3{a} libdebhelper-perl{a} libdeflate0{a} libdouble-conversion3{a} libdrm-amdgpu1{a} libdrm-common{a} libdrm-radeon1{a} libdrm2{a} libduktape207{a} libedit2{a} libegl-mesa0{a} libegl1{a} libelf1t64{a} libevdev2{a} libevent-core-2.1-7t64{a} libevent-pthreads-2.1-7t64{a} libexpat1{a} libfabric1{a} libfile-stripnondeterminism-perl{a} libfontconfig1{a} libfontenc1{a} libfreetype6{a} libfribidi0{a} libgbm1{a} libgfortran5{a} libgl1{a} libgl1-mesa-dri{a} libgl2ps1.4{a} libglapi-mesa{a} libgles2{a} libglew2.2{a} libglib2.0-0t64{a} libglvnd0{a} libglx-mesa0{a} libglx0{a} libgraphite2-3{a} libgssapi-krb5-2{a} libgudev-1.0-0{a} libharfbuzz0b{a} libhdf5-103-1t64{a} libhdf5-hl-100t64{a} libhwloc-plugins{a} libhwloc15{a} libibverbs1{a} libice6{a} libicu72{a} libimagequant0{a} libinput-bin{a} libinput10{a} libjargs-java{a} libjbig0{a} libjpeg62-turbo{a} libjs-jquery{a} libjs-jquery-hotkeys{a} libjs-jquery-isonscreen{a} libjs-jquery-metadata{a} libjs-jquery-tablesorter{a} libjs-jquery-throttle-debounce{a} libjs-jquery-ui{a} libjs-sphinxdoc{a} libjs-underscore{a} libjson-perl{a} libjsoncpp25{a} libk5crypto3{a} libkeyutils1{a} libkrb5-3{a} libkrb5support0{a} liblapack3{a} liblbfgsb0{a} liblcms2-2{a} libldap-2.5-0{a} liblerc4{a} libllvm19{a} libmagic-mgc{a} libmagic1t64{a} libmd4c0{a} libmpich12{a} libmtdev1t64{a} libnetcdf19t64{a} libnghttp2-14{a} libnghttp3-9{a} libngtcp2-16{a} libngtcp2-crypto-gnutls8{a} libnl-3-200{a} libnl-route-3-200{a} libnsl2{a} libnspr4{a} libnss3{a} libogg0{a} libopengl0{a} libopenjp2-7{a} libopenmpi3t64{a} libpciaccess0{a} libpcre2-16-0{a} libpcsclite1{a} libpipeline1{a} libpixman-1-0{a} libpng16-16t64{a} libproc2-0{a} libproj25{a} libproxy1v5{a} libpsl5t64{a} libpython3-stdlib{a} libpython3.12-minimal{a} libpython3.12-stdlib{a} libpython3.12t64{a} libqhull-r8.0{a} libqt5core5t64{a} libqt5dbus5t64{a} libqt5gui5t64{a} libqt5network5t64{a} libqt5widgets5t64{a} libqt6core6t64{a} libqt6dbus6{a} libqt6gui6{a} libqt6network6{a} libqt6opengl6{a} libqt6openglwidgets6{a} libqt6printsupport6{a} libqt6sql6{a} libqt6test6{a} libqt6widgets6{a} libqt6xml6{a} libraqm0{a} librdmacm1t64{a} libreadline8t64{a} librtmp1{a} libsasl2-2{a} libsasl2-modules-db{a} libsensors-config{a} libsensors5{a} libsharpyuv0{a} libslurm41t64{a} libsm6{a} libssh2-1t64{a} libsz2{a} libtbb12{a} libtbbbind-2-5{a} libtbbmalloc2{a} libtcl8.6{a} libtheora0{a} libtiff6{a} libtirpc-common{a} libtirpc3t64{a} libtk8.6{a} libtool{a} libts0t64{a} libuchardet0{a} libunwind8{a} libvtk9.3{a} libvtk9.3-qt{a} libvulkan1{a} libwacom-common{a} libwacom9{a} libwayland-client0{a} libwayland-server0{a} libwebp7{a} libwebpdemux2{a} libwebpmux3{a} libx11-6{a} libx11-data{a} libx11-xcb1{a} libxau6{a} libxaw7{a} libxcb-cursor0{a} libxcb-dri2-0{a} libxcb-dri3-0{a} libxcb-glx0{a} libxcb-icccm4{a} libxcb-image0{a} libxcb-keysyms1{a} libxcb-present0{a} libxcb-randr0{a} libxcb-render-util0{a} libxcb-render0{a} libxcb-shape0{a} libxcb-shm0{a} libxcb-sync1{a} libxcb-util1{a} libxcb-xfixes0{a} libxcb-xinerama0{a} libxcb-xinput0{a} libxcb-xkb1{a} libxcb1{a} libxdmcp6{a} libxext6{a} libxfixes3{a} libxfont2{a} libxft2{a} libxkbcommon-x11-0{a} libxkbcommon0{a} libxkbfile1{a} libxml2{a} libxmu6{a} libxmuu1{a} libxnvctrl0{a} libxpm4{a} libxrandr2{a} libxrender1{a} libxshmfence1{a} libxslt1.1{a} libxss1{a} libxt6t64{a} libxxf86vm1{a} libz3-4{a} m4{a} man-db{a} media-types{a} mesa-libgallium{a} mpi-default-bin{a} mpich{a} netbase{a} ocl-icd-libopencl1{a} openjdk-21-jre-headless{a} openssl{a} po-debconf{a} procps{a} proj-data{a} pybuild-plugin-pyproject{a} python-babel-localedata{a} python-matplotlib-data{a} python3{a} python3-alabaster{a} python3-all{a} python3-appdirs{a} python3-attr{a} python3-autocommand{a} python3-babel{a} python3-brotli{a} python3-build{a} python3-certifi{a} python3-chardet{a} python3-charset-normalizer{a} python3-contourpy{a} python3-coverage{a} python3-cycler{a} python3-dateutil{a} python3-decorator{a} python3-defusedxml{a} python3-docutils{a} python3-fonttools{a} python3-fs{a} python3-hatch-vcs{a} python3-hatchling{a} python3-idna{a} python3-imagesize{a} python3-inflect{a} python3-iniconfig{a} python3-installer{a} python3-jaraco.context{a} python3-jaraco.functools{a} python3-jinja2{a} python3-joblib{a} python3-kiwisolver{a} python3-lazy-loader{a} python3-lxml{a} python3-lz4{a} python3-markupsafe{a} python3-matplotlib{a} python3-minimal{a} python3-more-itertools{a} python3-mpi4py{a} python3-mpmath{a} python3-nibabel{a} python3-numpy{a} python3-packaging{a} python3-pathspec{a} python3-pil{a} python3-pil.imagetk{a} python3-pkg-resources{a} python3-platformdirs{a} python3-pluggy{a} python3-pooch{a} python3-pygments{a} python3-pyparsing{a} python3-pyproject-hooks{a} python3-pyqt6{a} python3-pyqt6.sip{a} python3-pytest{a} python3-pytest-cov{a} python3-pytest-timeout{a} python3-pytestqt{a} python3-requests{a} python3-roman{a} python3-scipy{a} python3-setuptools{a} python3-setuptools-scm{a} python3-six{a} python3-sklearn{a} python3-sklearn-lib{a} python3-snowballstemmer{a} python3-sphinx{a} python3-sympy{a} python3-threadpoolctl{a} python3-tk{a} python3-toml{a} python3-tqdm{a} python3-trove-classifiers{a} python3-typeguard{a} python3-typing-extensions{a} python3-tz{a} python3-ufolib2{a} python3-urllib3{a} python3-vtk9{a} python3-wheel{a} python3-zipp{a} python3.12{a} python3.12-minimal{a} python3.12-tk{a} python3.13-tk{a} readline-common{a} sensible-utils{a} sgml-base{a} shared-mime-info{a} sphinx-common{a} tk8.6-blt2.5{a} tzdata{a} unicode-data{a} unzip{a} x11-common{a} x11-xkb-utils{a} xauth{a} xkb-data{a} xml-core{a} xserver-common{a} xvfb{a} yui-compressor{a} The following packages are RECOMMENDED but will NOT be installed: curl dbus ibverbs-providers isympy-common javascript-common krb5-locales libarchive-cpio-perl libasound2t64 libglib2.0-data libjson-xs-perl libldap-common libltdl-dev libmail-sendmail-perl libmpich-dev libpaper-utils libqt5svg5 libqt6sql6-ibase libqt6sql6-mysql libqt6sql6-odbc libqt6sql6-psql libqt6sql6-sqlite libsasl2-modules linux-sysctl-defaults lynx mesa-vulkan-drivers psmisc publicsuffix python3-bs4 python3-cssselect python3-fuse python3-html5lib python3-olefile python3-psutil python3-pydicom python3-pyqt5 python3-simplejson qt5-gtk-platformtheme qt6-gtk-platformtheme qt6-qpa-plugins qt6-translations-l10n qt6-wayland qttranslations5-l10n qtwayland5 wget xdg-user-dirs xfonts-base 0 packages upgraded, 368 newly installed, 0 to remove and 0 not upgraded. Need to get 258 MB of archives. After unpacking 1032 MB will be used. Writing extended state information... Get: 1 http://deb.debian.org/debian trixie/main armhf libjs-jquery all 3.6.1+dfsg+~3.5.14-1 [326 kB] Get: 2 http://deb.debian.org/debian trixie/main armhf libjs-jquery-hotkeys all 0~20130707+git2d51e3a9+dfsg-2.1 [11.5 kB] Get: 3 http://deb.debian.org/debian trixie/main armhf libpython3.12-minimal armhf 3.12.6-1 [800 kB] Get: 4 http://deb.debian.org/debian trixie/main armhf libexpat1 armhf 2.6.3-1 [83.2 kB] Get: 5 http://deb.debian.org/debian trixie/main armhf python3.12-minimal armhf 3.12.6-1 [1812 kB] Get: 6 http://deb.debian.org/debian trixie/main armhf python3-minimal armhf 3.12.6-1 [26.7 kB] Get: 7 http://deb.debian.org/debian trixie/main armhf media-types all 10.1.0 [26.9 kB] Get: 8 http://deb.debian.org/debian trixie/main armhf netbase all 6.4 [12.8 kB] Get: 9 http://deb.debian.org/debian trixie/main armhf tzdata all 2024a-4 [255 kB] Get: 10 http://deb.debian.org/debian trixie/main armhf libkrb5support0 armhf 1.21.3-3 [30.0 kB] Get: 11 http://deb.debian.org/debian trixie/main armhf libcom-err2 armhf 1.47.1-1 [22.1 kB] Get: 12 http://deb.debian.org/debian trixie/main armhf libk5crypto3 armhf 1.21.3-3 [75.8 kB] Get: 13 http://deb.debian.org/debian trixie/main armhf libkeyutils1 armhf 1.6.3-3 [7908 B] Get: 14 http://deb.debian.org/debian trixie/main armhf libkrb5-3 armhf 1.21.3-3 [283 kB] Get: 15 http://deb.debian.org/debian trixie/main armhf libgssapi-krb5-2 armhf 1.21.3-3 [114 kB] Get: 16 http://deb.debian.org/debian trixie/main armhf libtirpc-common all 1.3.4+ds-1.3 [10.9 kB] Get: 17 http://deb.debian.org/debian trixie/main armhf libtirpc3t64 armhf 1.3.4+ds-1.3 [71.1 kB] Get: 18 http://deb.debian.org/debian trixie/main armhf libnsl2 armhf 1.3.0-3+b2 [34.9 kB] Get: 19 http://deb.debian.org/debian trixie/main armhf readline-common all 8.2-5 [69.3 kB] Get: 20 http://deb.debian.org/debian trixie/main armhf libreadline8t64 armhf 8.2-5 [146 kB] Get: 21 http://deb.debian.org/debian trixie/main armhf libpython3.12-stdlib armhf 3.12.6-1 [1817 kB] Get: 22 http://deb.debian.org/debian trixie/main armhf python3.12 armhf 3.12.6-1 [669 kB] Get: 23 http://deb.debian.org/debian trixie/main armhf libpython3-stdlib armhf 3.12.6-1 [9692 B] Get: 24 http://deb.debian.org/debian trixie/main armhf python3 armhf 3.12.6-1 [27.8 kB] Get: 25 http://deb.debian.org/debian trixie/main armhf sgml-base all 1.31 [15.4 kB] Get: 26 http://deb.debian.org/debian trixie/main armhf libproc2-0 armhf 2:4.0.4-6 [56.0 kB] Get: 27 http://deb.debian.org/debian trixie/main armhf procps armhf 2:4.0.4-6 [864 kB] Get: 28 http://deb.debian.org/debian trixie/main armhf sensible-utils all 0.0.24 [24.8 kB] Get: 29 http://deb.debian.org/debian trixie/main armhf openssl armhf 3.3.2-1 [1348 kB] Get: 30 http://deb.debian.org/debian trixie/main armhf ca-certificates all 20240203 [158 kB] Get: 31 http://deb.debian.org/debian trixie/main armhf libmagic-mgc armhf 1:5.45-3 [314 kB] Get: 32 http://deb.debian.org/debian trixie/main armhf libmagic1t64 armhf 1:5.45-3 [98.1 kB] Get: 33 http://deb.debian.org/debian trixie/main armhf file armhf 1:5.45-3 [42.0 kB] Get: 34 http://deb.debian.org/debian trixie/main armhf gettext-base armhf 0.22.5-2 [195 kB] Get: 35 http://deb.debian.org/debian trixie/main armhf libuchardet0 armhf 0.0.8-1+b1 [65.7 kB] Get: 36 http://deb.debian.org/debian trixie/main armhf groff-base armhf 1.23.0-5 [1091 kB] Get: 37 http://deb.debian.org/debian trixie/main armhf bsdextrautils armhf 2.40.2-8 [88.8 kB] Get: 38 http://deb.debian.org/debian trixie/main armhf libpipeline1 armhf 1.5.8-1 [35.0 kB] Get: 39 http://deb.debian.org/debian trixie/main armhf man-db armhf 2.13.0-1 [1382 kB] Get: 40 http://deb.debian.org/debian trixie/main armhf m4 armhf 1.4.19-4 [264 kB] Get: 41 http://deb.debian.org/debian trixie/main armhf autoconf all 2.72-3 [493 kB] Get: 42 http://deb.debian.org/debian trixie/main armhf autotools-dev all 20220109.1 [51.6 kB] Get: 43 http://deb.debian.org/debian trixie/main armhf automake all 1:1.16.5-1.3 [823 kB] Get: 44 http://deb.debian.org/debian trixie/main armhf autopoint all 0.22.5-2 [723 kB] Get: 45 http://deb.debian.org/debian trixie/main armhf libtcl8.6 armhf 8.6.15+dfsg-2 [934 kB] Get: 46 http://deb.debian.org/debian trixie/main armhf libbrotli1 armhf 1.1.0-2+b4 [293 kB] Get: 47 http://deb.debian.org/debian trixie/main armhf libpng16-16t64 armhf 1.6.44-2 [263 kB] Get: 48 http://deb.debian.org/debian trixie/main armhf libfreetype6 armhf 2.13.3+dfsg-1 [385 kB] Get: 49 http://deb.debian.org/debian trixie/main armhf fonts-dejavu-mono all 2.37-8 [489 kB] Get: 50 http://deb.debian.org/debian trixie/main armhf fonts-dejavu-core all 2.37-8 [840 kB] Get: 51 http://deb.debian.org/debian trixie/main armhf fontconfig-config armhf 2.15.0-1.1 [317 kB] Get: 52 http://deb.debian.org/debian trixie/main armhf libfontconfig1 armhf 2.15.0-1.1 [370 kB] Get: 53 http://deb.debian.org/debian trixie/main armhf libxau6 armhf 1:1.0.9-1+b1 [17.4 kB] Get: 54 http://deb.debian.org/debian trixie/main armhf libxdmcp6 armhf 1:1.1.2-3+b1 [23.0 kB] Get: 55 http://deb.debian.org/debian trixie/main armhf libxcb1 armhf 1.17.0-2 [140 kB] Get: 56 http://deb.debian.org/debian trixie/main armhf libx11-data all 2:1.8.7-1 [328 kB] Get: 57 http://deb.debian.org/debian trixie/main armhf libx11-6 armhf 2:1.8.7-1+b1 [739 kB] Get: 58 http://deb.debian.org/debian trixie/main armhf libxrender1 armhf 1:0.9.10-1.1+b1 [24.9 kB] Get: 59 http://deb.debian.org/debian trixie/main armhf libxft2 armhf 2.3.6-1+b1 [46.4 kB] Get: 60 http://deb.debian.org/debian trixie/main armhf libxext6 armhf 2:1.3.4-1+b1 [47.8 kB] Get: 61 http://deb.debian.org/debian trixie/main armhf x11-common all 1:7.7+23.1 [216 kB] Get: 62 http://deb.debian.org/debian trixie/main armhf libxss1 armhf 1:1.2.3-1+b1 [16.4 kB] Get: 63 http://deb.debian.org/debian trixie/main armhf libtk8.6 armhf 8.6.15-1 [697 kB] Get: 64 http://deb.debian.org/debian trixie/main armhf tk8.6-blt2.5 armhf 2.5.3+dfsg-7 [481 kB] Get: 65 http://deb.debian.org/debian trixie/main armhf blt armhf 2.5.3+dfsg-7 [6024 B] Get: 66 http://deb.debian.org/debian trixie/main armhf ca-certificates-java all 20240118 [11.6 kB] Get: 67 http://deb.debian.org/debian trixie/main armhf libdebhelper-perl all 13.20 [89.7 kB] Get: 68 http://deb.debian.org/debian trixie/main armhf libtool all 2.4.7-7 [517 kB] Get: 69 http://deb.debian.org/debian trixie/main armhf dh-autoreconf all 20 [17.1 kB] Get: 70 http://deb.debian.org/debian trixie/main armhf libarchive-zip-perl all 1.68-1 [104 kB] Get: 71 http://deb.debian.org/debian trixie/main armhf libfile-stripnondeterminism-perl all 1.14.0-1 [19.5 kB] Get: 72 http://deb.debian.org/debian trixie/main armhf dh-strip-nondeterminism all 1.14.0-1 [8448 B] Get: 73 http://deb.debian.org/debian trixie/main armhf libelf1t64 armhf 0.191-2 [183 kB] Get: 74 http://deb.debian.org/debian trixie/main armhf dwz armhf 0.15-1+b2 [106 kB] Get: 75 http://deb.debian.org/debian trixie/main armhf libicu72 armhf 72.1-5 [9075 kB] Get: 76 http://deb.debian.org/debian trixie/main armhf libxml2 armhf 2.12.7+dfsg+really2.9.14-0.1 [604 kB] Get: 77 http://deb.debian.org/debian trixie/main armhf gettext armhf 0.22.5-2 [1485 kB] Get: 78 http://deb.debian.org/debian trixie/main armhf intltool-debian all 0.35.0+20060710.6 [22.9 kB] Get: 79 http://deb.debian.org/debian trixie/main armhf po-debconf all 1.0.21+nmu1 [248 kB] Get: 80 http://deb.debian.org/debian trixie/main armhf debhelper all 13.20 [915 kB] Get: 81 http://deb.debian.org/debian trixie/main armhf java-common all 0.76 [6776 B] Get: 82 http://deb.debian.org/debian trixie/main armhf liblcms2-2 armhf 2.14-2+b1 [126 kB] Get: 83 http://deb.debian.org/debian trixie/main armhf libjpeg62-turbo armhf 1:2.1.5-3 [143 kB] Get: 84 http://deb.debian.org/debian trixie/main armhf libnspr4 armhf 2:4.35-1.1+b1 [87.2 kB] Get: 85 http://deb.debian.org/debian trixie/main armhf libnss3 armhf 2:3.105-2 [1205 kB] Get: 86 http://deb.debian.org/debian trixie/main armhf libpcsclite1 armhf 2.3.0-1 [51.3 kB] Get: 87 http://deb.debian.org/debian trixie/main armhf openjdk-21-jre-headless armhf 21.0.5~8ea-1 [35.6 MB] Get: 88 http://deb.debian.org/debian trixie/main armhf default-jre-headless armhf 2:1.21-76 [3192 B] Get: 89 http://deb.debian.org/debian trixie/main armhf python3-autocommand all 2.2.2-3 [13.6 kB] Get: 90 http://deb.debian.org/debian trixie/main armhf python3-more-itertools all 10.5.0-1 [63.8 kB] Get: 91 http://deb.debian.org/debian trixie/main armhf python3-typing-extensions all 4.12.2-2 [73.0 kB] Get: 92 http://deb.debian.org/debian trixie/main armhf python3-typeguard all 4.3.0-1 [36.5 kB] Get: 93 http://deb.debian.org/debian trixie/main armhf python3-inflect all 7.3.1-2 [32.4 kB] Get: 94 http://deb.debian.org/debian trixie/main armhf python3-jaraco.context all 6.0.0-1 [7984 B] Get: 95 http://deb.debian.org/debian trixie/main armhf python3-jaraco.functools all 4.1.0-1 [12.0 kB] Get: 96 http://deb.debian.org/debian trixie/main armhf python3-pkg-resources all 74.1.2-2 [213 kB] Get: 97 http://deb.debian.org/debian trixie/main armhf python3-zipp all 3.20.2-1 [10.3 kB] Get: 98 http://deb.debian.org/debian trixie/main armhf python3-setuptools all 74.1.2-2 [736 kB] Get: 99 http://deb.debian.org/debian trixie/main armhf dh-python all 6.20240824 [109 kB] Get: 100 http://deb.debian.org/debian trixie/main armhf xml-core all 0.19 [20.1 kB] Get: 101 http://deb.debian.org/debian trixie/main armhf docutils-common all 0.21.2+dfsg-2 [128 kB] Get: 102 http://deb.debian.org/debian trixie/main armhf fontconfig armhf 2.15.0-1.1 [461 kB] Get: 103 http://deb.debian.org/debian trixie/main armhf fonts-lyx all 2.4.2.1-1 [190 kB] Get: 104 http://deb.debian.org/debian trixie/main armhf libhwloc15 armhf 2.11.2-1 [134 kB] Get: 105 http://deb.debian.org/debian trixie/main armhf hwloc-nox armhf 2.11.2-1 [201 kB] Get: 106 http://deb.debian.org/debian trixie/main armhf unzip armhf 6.0-28 [152 kB] Get: 107 http://deb.debian.org/debian trixie/main armhf java-wrappers all 0.5 [8848 B] Get: 108 http://deb.debian.org/debian trixie/main armhf libaec0 armhf 1.1.3-1 [21.5 kB] Get: 109 http://deb.debian.org/debian trixie/main armhf libavahi-common-data armhf 0.8-13+b2 [112 kB] Get: 110 http://deb.debian.org/debian trixie/main armhf libavahi-common3 armhf 0.8-13+b2 [40.2 kB] Get: 111 http://deb.debian.org/debian trixie/main armhf libdbus-1-3 armhf 1.14.10-4+b1 [181 kB] Get: 112 http://deb.debian.org/debian trixie/main armhf libavahi-client3 armhf 0.8-13+b2 [43.4 kB] Get: 113 http://deb.debian.org/debian trixie/main armhf libb2-1 armhf 0.98.1-1.1+b1 [21.9 kB] Get: 114 http://deb.debian.org/debian trixie/main armhf libblas3 armhf 3.12.0-3 [108 kB] Get: 115 http://deb.debian.org/debian trixie/main armhf libpixman-1-0 armhf 0.42.2-1+b1 [476 kB] Get: 116 http://deb.debian.org/debian trixie/main armhf libxcb-render0 armhf 1.17.0-2 [114 kB] Get: 117 http://deb.debian.org/debian trixie/main armhf libxcb-shm0 armhf 1.17.0-2 [105 kB] Get: 118 http://deb.debian.org/debian trixie/main armhf libcairo2 armhf 1.18.2-2 [443 kB] Get: 119 http://deb.debian.org/debian trixie/main armhf libcups2t64 armhf 2.4.10-2 [216 kB] Get: 120 http://deb.debian.org/debian trixie/main armhf libsasl2-modules-db armhf 2.1.28+dfsg1-8 [18.2 kB] Get: 121 http://deb.debian.org/debian trixie/main armhf libsasl2-2 armhf 2.1.28+dfsg1-8 [50.2 kB] Get: 122 http://deb.debian.org/debian trixie/main armhf libldap-2.5-0 armhf 2.5.18+dfsg-3 [163 kB] Get: 123 http://deb.debian.org/debian trixie/main armhf libnghttp2-14 armhf 1.63.0-1 [62.9 kB] Get: 124 http://deb.debian.org/debian trixie/main armhf libnghttp3-9 armhf 1.4.0-1 [55.1 kB] Get: 125 http://deb.debian.org/debian trixie/main armhf libngtcp2-16 armhf 1.6.0-1 [118 kB] Get: 126 http://deb.debian.org/debian trixie/main armhf libngtcp2-crypto-gnutls8 armhf 1.6.0-1 [17.1 kB] Get: 127 http://deb.debian.org/debian trixie/main armhf libpsl5t64 armhf 0.21.2-1.1 [55.6 kB] Get: 128 http://deb.debian.org/debian trixie/main armhf librtmp1 armhf 2.4+20151223.gitfa8646d.1-2+b4 [53.2 kB] Get: 129 http://deb.debian.org/debian trixie/main armhf libssh2-1t64 armhf 1.11.0-7 [199 kB] Get: 130 http://deb.debian.org/debian trixie/main armhf libcurl3t64-gnutls armhf 8.10.1-1 [308 kB] Get: 131 http://deb.debian.org/debian trixie/main armhf libcurl4t64 armhf 8.10.1-1 [303 kB] Get: 132 http://deb.debian.org/debian trixie/main armhf libdeflate0 armhf 1.22-1 [36.3 kB] Get: 133 http://deb.debian.org/debian trixie/main armhf libdouble-conversion3 armhf 3.3.0-1+b1 [38.5 kB] Get: 134 http://deb.debian.org/debian trixie/main armhf libdrm-common all 2.4.123-1 [8084 B] Get: 135 http://deb.debian.org/debian trixie/main armhf libdrm2 armhf 2.4.123-1 [34.1 kB] Get: 136 http://deb.debian.org/debian trixie/main armhf libdrm-amdgpu1 armhf 2.4.123-1 [20.4 kB] Get: 137 http://deb.debian.org/debian trixie/main armhf libdrm-radeon1 armhf 2.4.123-1 [19.6 kB] Get: 138 http://deb.debian.org/debian trixie/main armhf libduktape207 armhf 2.7.0-2+b1 [114 kB] Get: 139 http://deb.debian.org/debian trixie/main armhf libedit2 armhf 3.1-20240808-1 [77.9 kB] Get: 140 http://deb.debian.org/debian trixie/main armhf libwayland-server0 armhf 1.23.0-1 [27.6 kB] Get: 141 http://deb.debian.org/debian trixie/main armhf libxcb-randr0 armhf 1.17.0-2 [115 kB] Get: 142 http://deb.debian.org/debian trixie/main armhf libglapi-mesa armhf 24.2.4-1 [44.7 kB] Get: 143 http://deb.debian.org/debian trixie/main armhf libz3-4 armhf 4.8.12-3.1+b2 [6324 kB] Get: 144 http://deb.debian.org/debian trixie/main armhf libllvm19 armhf 1:19.1.1-1 [23.8 MB] Get: 145 http://deb.debian.org/debian trixie/main armhf libsensors-config all 1:3.6.0-10 [14.6 kB] Get: 146 http://deb.debian.org/debian trixie/main armhf libsensors5 armhf 1:3.6.0-10 [32.0 kB] Get: 147 http://deb.debian.org/debian trixie/main armhf libx11-xcb1 armhf 2:1.8.7-1+b1 [232 kB] Get: 148 http://deb.debian.org/debian trixie/main armhf libxcb-dri2-0 armhf 1.17.0-2 [106 kB] Get: 149 http://deb.debian.org/debian trixie/main armhf libxcb-dri3-0 armhf 1.17.0-2 [106 kB] Get: 150 http://deb.debian.org/debian trixie/main armhf libxcb-present0 armhf 1.17.0-2 [105 kB] Get: 151 http://deb.debian.org/debian trixie/main armhf libxcb-sync1 armhf 1.17.0-2 [108 kB] Get: 152 http://deb.debian.org/debian trixie/main armhf libxcb-xfixes0 armhf 1.17.0-2 [109 kB] Get: 153 http://deb.debian.org/debian trixie/main armhf libxshmfence1 armhf 1.3-1+b1 [8628 B] Get: 154 http://deb.debian.org/debian trixie/main armhf mesa-libgallium armhf 24.2.4-1 [7085 kB] Get: 155 http://deb.debian.org/debian trixie/main armhf libgbm1 armhf 24.2.4-1 [38.9 kB] Get: 156 http://deb.debian.org/debian trixie/main armhf libwayland-client0 armhf 1.23.0-1 [20.9 kB] Get: 157 http://deb.debian.org/debian trixie/main armhf libegl-mesa0 armhf 24.2.4-1 [110 kB] Get: 158 http://deb.debian.org/debian trixie/main armhf libevdev2 armhf 1.13.3+dfsg-1 [26.2 kB] Get: 159 http://deb.debian.org/debian trixie/main armhf libevent-core-2.1-7t64 armhf 2.1.12-stable-10 [122 kB] Get: 160 http://deb.debian.org/debian trixie/main armhf libevent-pthreads-2.1-7t64 armhf 2.1.12-stable-10 [53.6 kB] Get: 161 http://deb.debian.org/debian trixie/main armhf libnl-3-200 armhf 3.7.0-0.3 [51.7 kB] Get: 162 http://deb.debian.org/debian trixie/main armhf libnl-route-3-200 armhf 3.7.0-0.3 [153 kB] Get: 163 http://deb.debian.org/debian trixie/main armhf libibverbs1 armhf 52.0-2 [54.8 kB] Get: 164 http://deb.debian.org/debian trixie/main armhf librdmacm1t64 armhf 52.0-2 [62.1 kB] Get: 165 http://deb.debian.org/debian trixie/main armhf libfabric1 armhf 1.17.0-3+b1 [386 kB] Get: 166 http://deb.debian.org/debian trixie/main armhf libfontenc1 armhf 1:1.1.8-1 [20.6 kB] Get: 167 http://deb.debian.org/debian trixie/main armhf libfribidi0 armhf 1.0.15-1 [70.0 kB] Get: 168 http://deb.debian.org/debian trixie/main armhf libgfortran5 armhf 14.2.0-3 [262 kB] Get: 169 http://deb.debian.org/debian trixie/main armhf libglvnd0 armhf 1.7.0-1+b1 [52.2 kB] Get: 170 http://deb.debian.org/debian trixie/main armhf libxcb-glx0 armhf 1.17.0-2 [120 kB] Get: 171 http://deb.debian.org/debian trixie/main armhf libxfixes3 armhf 1:6.0.0-2+b1 [18.6 kB] Get: 172 http://deb.debian.org/debian trixie/main armhf libxxf86vm1 armhf 1:1.1.4-1+b2 [20.2 kB] Get: 173 http://deb.debian.org/debian trixie/main armhf libvulkan1 armhf 1.3.290.0-1 [103 kB] Get: 174 http://deb.debian.org/debian trixie/main armhf libgl1-mesa-dri armhf 24.2.4-1 [41.0 kB] Get: 175 http://deb.debian.org/debian trixie/main armhf libglx-mesa0 armhf 24.2.4-1 [132 kB] Get: 176 http://deb.debian.org/debian trixie/main armhf libglx0 armhf 1.7.0-1+b1 [32.6 kB] Get: 177 http://deb.debian.org/debian trixie/main armhf libgl1 armhf 1.7.0-1+b1 [91.1 kB] Get: 178 http://deb.debian.org/debian trixie/main armhf libgl2ps1.4 armhf 1.4.2+dfsg1-2 [36.5 kB] Get: 179 http://deb.debian.org/debian trixie/main armhf libgles2 armhf 1.7.0-1+b1 [17.7 kB] Get: 180 http://deb.debian.org/debian trixie/main armhf libglew2.2 armhf 2.2.0-4+b1 [172 kB] Get: 181 http://deb.debian.org/debian trixie/main armhf libglib2.0-0t64 armhf 2.82.1-1 [1325 kB] Get: 182 http://deb.debian.org/debian trixie/main armhf libgraphite2-3 armhf 1.3.14-2 [63.2 kB] Get: 183 http://deb.debian.org/debian trixie/main armhf libgudev-1.0-0 armhf 238-5 [12.6 kB] Get: 184 http://deb.debian.org/debian trixie/main armhf libharfbuzz0b armhf 9.0.0-1 [417 kB] Get: 185 http://deb.debian.org/debian trixie/main armhf libsz2 armhf 1.1.3-1 [7724 B] Get: 186 http://deb.debian.org/debian trixie/main armhf libhdf5-103-1t64 armhf 1.10.10+repack-4 [1196 kB] Get: 187 http://deb.debian.org/debian trixie/main armhf libhdf5-hl-100t64 armhf 1.10.10+repack-4 [62.3 kB] Get: 188 http://deb.debian.org/debian trixie/main armhf libpciaccess0 armhf 0.17-3+b1 [49.3 kB] Get: 189 http://deb.debian.org/debian trixie/main armhf libxnvctrl0 armhf 535.171.04-1 [12.8 kB] Get: 190 http://deb.debian.org/debian trixie/main armhf ocl-icd-libopencl1 armhf 2.3.2-1+b1 [37.3 kB] Get: 191 http://deb.debian.org/debian trixie/main armhf libhwloc-plugins armhf 2.11.2-1 [16.1 kB] Get: 192 http://deb.debian.org/debian trixie/main armhf libice6 armhf 2:1.0.10-1+b1 [50.1 kB] Get: 193 http://deb.debian.org/debian trixie/main armhf libimagequant0 armhf 2.18.0-1+b1 [30.7 kB] Get: 194 http://deb.debian.org/debian trixie/main armhf libwacom-common all 2.13.0-1 [98.0 kB] Get: 195 http://deb.debian.org/debian trixie/main armhf libwacom9 armhf 2.13.0-1 [20.8 kB] Get: 196 http://deb.debian.org/debian trixie/main armhf libinput-bin armhf 1.26.2-1 [23.6 kB] Get: 197 http://deb.debian.org/debian trixie/main armhf libmtdev1t64 armhf 1.1.6-1.2 [21.0 kB] Get: 198 http://deb.debian.org/debian trixie/main armhf libinput10 armhf 1.26.2-1 [112 kB] Get: 199 http://deb.debian.org/debian trixie/main armhf libjargs-java all 1.0.0-5 [14.9 kB] Get: 200 http://deb.debian.org/debian trixie/main armhf libjbig0 armhf 2.1-6.1+b1 [27.3 kB] Get: 201 http://deb.debian.org/debian trixie/main armhf libjs-jquery-isonscreen all 1.2.0-1.1 [3196 B] Get: 202 http://deb.debian.org/debian trixie/main armhf libjs-jquery-metadata all 12-4 [6532 B] Get: 203 http://deb.debian.org/debian trixie/main armhf libjs-jquery-tablesorter all 1:2.31.3+dfsg1-4 [184 kB] Get: 204 http://deb.debian.org/debian trixie/main armhf libjs-jquery-throttle-debounce all 1.1+dfsg.1-2 [12.2 kB] Get: 205 http://deb.debian.org/debian trixie/main armhf libjs-jquery-ui all 1.13.2+dfsg-1 [250 kB] Get: 206 http://deb.debian.org/debian trixie/main armhf libjs-underscore all 1.13.4~dfsg+~1.11.4-3 [116 kB] Get: 207 http://deb.debian.org/debian trixie/main armhf libjs-sphinxdoc all 7.4.7-3 [158 kB] Get: 208 http://deb.debian.org/debian trixie/main armhf libjson-perl all 4.10000-1 [87.5 kB] Get: 209 http://deb.debian.org/debian trixie/main armhf libjsoncpp25 armhf 1.9.5-6+b2 [69.9 kB] Get: 210 http://deb.debian.org/debian trixie/main armhf liblapack3 armhf 3.12.0-3 [1803 kB] Get: 211 http://deb.debian.org/debian trixie/main armhf liblbfgsb0 armhf 3.0+dfsg.4-1+b1 [25.5 kB] Get: 212 http://deb.debian.org/debian trixie/main armhf liblerc4 armhf 4.0.0+ds-4+b1 [137 kB] Get: 213 http://deb.debian.org/debian trixie/main armhf libmd4c0 armhf 0.5.2-2+b1 [43.8 kB] Get: 214 http://deb.debian.org/debian trixie/main armhf libmpich12 armhf 4.2.0-14 [1498 kB] Get: 215 http://deb.debian.org/debian trixie/main armhf libnetcdf19t64 armhf 1:4.9.2-7 [421 kB] Get: 216 http://deb.debian.org/debian trixie/main armhf libogg0 armhf 1.3.5-3+b1 [21.9 kB] Get: 217 http://deb.debian.org/debian trixie/main armhf libopenjp2-7 armhf 2.5.0-2+b3 [170 kB] Get: 218 http://deb.debian.org/debian trixie/main armhf libopenmpi3t64 armhf 4.1.6-13.3 [2216 kB] Get: 219 http://deb.debian.org/debian trixie/main armhf libpcre2-16-0 armhf 10.42-4+b1 [212 kB] Get: 220 http://deb.debian.org/debian trixie/main armhf proj-data all 9.5.0-1 [6293 kB] Get: 221 http://deb.debian.org/debian trixie/main armhf libsharpyuv0 armhf 1.4.0-0.1 [111 kB] Get: 222 http://deb.debian.org/debian trixie/main armhf libwebp7 armhf 1.4.0-0.1 [265 kB] Get: 223 http://deb.debian.org/debian trixie/main armhf libtiff6 armhf 4.5.1+git230720-5 [302 kB] Get: 224 http://deb.debian.org/debian trixie/main armhf libproj25 armhf 9.5.0-1 [1202 kB] Get: 225 http://deb.debian.org/debian trixie/main armhf libproxy1v5 armhf 0.5.8-1 [23.5 kB] Get: 226 http://deb.debian.org/debian trixie/main armhf libpython3.12t64 armhf 3.12.6-1 [1847 kB] Get: 227 http://deb.debian.org/debian trixie/main armhf libqhull-r8.0 armhf 2020.2-6+b1 [219 kB] Get: 228 http://deb.debian.org/debian trixie/main armhf shared-mime-info armhf 2.4-5+b1 [753 kB] Get: 229 http://deb.debian.org/debian trixie/main armhf libqt5core5t64 armhf 5.15.13+dfsg-4 [1596 kB] Get: 230 http://deb.debian.org/debian trixie/main armhf libqt5dbus5t64 armhf 5.15.13+dfsg-4 [192 kB] Get: 231 http://deb.debian.org/debian trixie/main armhf libegl1 armhf 1.7.0-1+b1 [29.1 kB] Get: 232 http://deb.debian.org/debian trixie/main armhf libqt5network5t64 armhf 5.15.13+dfsg-4 [603 kB] Get: 233 http://deb.debian.org/debian trixie/main armhf libsm6 armhf 2:1.2.3-1+b1 [31.7 kB] Get: 234 http://deb.debian.org/debian trixie/main armhf libxcb-icccm4 armhf 0.4.1-1.2 [25.3 kB] Get: 235 http://deb.debian.org/debian trixie/main armhf libxcb-util1 armhf 0.4.0-1+b1 [22.2 kB] Get: 236 http://deb.debian.org/debian trixie/main armhf libxcb-image0 armhf 0.4.0-2+b1 [21.1 kB] Get: 237 http://deb.debian.org/debian trixie/main armhf libxcb-keysyms1 armhf 0.4.0-1+b2 [15.8 kB] Get: 238 http://deb.debian.org/debian trixie/main armhf libxcb-render-util0 armhf 0.3.9-1+b1 [17.4 kB] Get: 239 http://deb.debian.org/debian trixie/main armhf libxcb-shape0 armhf 1.17.0-2 [105 kB] Get: 240 http://deb.debian.org/debian trixie/main armhf libxcb-xinerama0 armhf 1.17.0-2 [105 kB] Get: 241 http://deb.debian.org/debian trixie/main armhf libxcb-xinput0 armhf 1.17.0-2 [127 kB] Get: 242 http://deb.debian.org/debian trixie/main armhf libxcb-xkb1 armhf 1.17.0-2 [126 kB] Get: 243 http://deb.debian.org/debian trixie/main armhf xkb-data all 2.42-1 [790 kB] Get: 244 http://deb.debian.org/debian trixie/main armhf libxkbcommon0 armhf 1.6.0-1+b1 [96.9 kB] Get: 245 http://deb.debian.org/debian trixie/main armhf libxkbcommon-x11-0 armhf 1.6.0-1+b1 [14.5 kB] Get: 246 http://deb.debian.org/debian trixie/main armhf libqt5gui5t64 armhf 5.15.13+dfsg-4 [2682 kB] Get: 247 http://deb.debian.org/debian trixie/main armhf libqt5widgets5t64 armhf 5.15.13+dfsg-4 [2121 kB] Get: 248 http://deb.debian.org/debian trixie/main armhf libqt6core6t64 armhf 6.6.2+dfsg-12 [1512 kB] Get: 249 http://deb.debian.org/debian trixie/main armhf libqt6dbus6 armhf 6.6.2+dfsg-12 [226 kB] Get: 250 http://deb.debian.org/debian trixie/main armhf libopengl0 armhf 1.7.0-1+b1 [31.9 kB] Get: 251 http://deb.debian.org/debian trixie/main armhf libts0t64 armhf 1.22-1.1 [57.6 kB] Get: 252 http://deb.debian.org/debian trixie/main armhf libxcb-cursor0 armhf 0.1.4-1+b1 [16.2 kB] Get: 253 http://deb.debian.org/debian trixie/main armhf libqt6gui6 armhf 6.6.2+dfsg-12 [2581 kB] Get: 254 http://deb.debian.org/debian trixie/main armhf libqt6network6 armhf 6.6.2+dfsg-12 [619 kB] Get: 255 http://deb.debian.org/debian trixie/main armhf libqt6opengl6 armhf 6.6.2+dfsg-12 [356 kB] Get: 256 http://deb.debian.org/debian trixie/main armhf libqt6widgets6 armhf 6.6.2+dfsg-12 [2242 kB] Get: 257 http://deb.debian.org/debian trixie/main armhf libqt6openglwidgets6 armhf 6.6.2+dfsg-12 [46.7 kB] Get: 258 http://deb.debian.org/debian trixie/main armhf libqt6printsupport6 armhf 6.6.2+dfsg-12 [198 kB] Get: 259 http://deb.debian.org/debian trixie/main armhf libqt6sql6 armhf 6.6.2+dfsg-12 [123 kB] Get: 260 http://deb.debian.org/debian trixie/main armhf libqt6test6 armhf 6.6.2+dfsg-12 [155 kB] Get: 261 http://deb.debian.org/debian trixie/main armhf libqt6xml6 armhf 6.6.2+dfsg-12 [75.3 kB] Get: 262 http://deb.debian.org/debian trixie/main armhf libraqm0 armhf 0.10.1-1+b1 [11.8 kB] Get: 263 http://deb.debian.org/debian trixie/main armhf libslurm41t64 armhf 24.05.2-1 [682 kB] Get: 264 http://deb.debian.org/debian trixie/main armhf libtbbmalloc2 armhf 2021.12.0-1 [42.4 kB] Get: 265 http://deb.debian.org/debian trixie/main armhf libtbbbind-2-5 armhf 2021.12.0-1 [13.0 kB] Get: 266 http://deb.debian.org/debian trixie/main armhf libtbb12 armhf 2021.12.0-1 [74.1 kB] Get: 267 http://deb.debian.org/debian trixie/main armhf libtheora0 armhf 1.1.1+dfsg.1-17 [130 kB] Get: 268 http://deb.debian.org/debian trixie/main armhf libunwind8 armhf 1.6.2-3.1 [43.8 kB] Get: 269 http://deb.debian.org/debian trixie/main armhf libvtk9.3 armhf 9.3.0+dfsg1-1+b2 [19.3 MB] Get: 270 http://deb.debian.org/debian trixie/main armhf libvtk9.3-qt armhf 9.3.0+dfsg1-1+b2 [148 kB] Get: 271 http://deb.debian.org/debian trixie/main armhf libwebpdemux2 armhf 1.4.0-0.1 [110 kB] Get: 272 http://deb.debian.org/debian trixie/main armhf libwebpmux3 armhf 1.4.0-0.1 [120 kB] Get: 273 http://deb.debian.org/debian trixie/main armhf libxt6t64 armhf 1:1.2.1-1.2 [159 kB] Get: 274 http://deb.debian.org/debian trixie/main armhf libxmu6 armhf 2:1.1.3-3+b2 [50.9 kB] Get: 275 http://deb.debian.org/debian trixie/main armhf libxpm4 armhf 1:3.5.17-1+b1 [50.0 kB] Get: 276 http://deb.debian.org/debian trixie/main armhf libxaw7 armhf 2:1.0.14-1+b2 [165 kB] Get: 277 http://deb.debian.org/debian trixie/main armhf libxfont2 armhf 1:2.0.6-1+b1 [116 kB] Get: 278 http://deb.debian.org/debian trixie/main armhf libxkbfile1 armhf 1:1.1.0-1+b1 [66.4 kB] Get: 279 http://deb.debian.org/debian trixie/main armhf libxmuu1 armhf 2:1.1.3-3+b2 [21.2 kB] Get: 280 http://deb.debian.org/debian trixie/main armhf libxrandr2 armhf 2:1.5.4-1 [33.0 kB] Get: 281 http://deb.debian.org/debian trixie/main armhf libxslt1.1 armhf 1.1.35-1.1 [212 kB] Get: 282 http://deb.debian.org/debian trixie/main armhf mpich armhf 4.2.0-14 [223 kB] Get: 283 http://deb.debian.org/debian trixie/main armhf mpi-default-bin armhf 1.17 [2368 B] Get: 284 http://deb.debian.org/debian trixie/main armhf python3-packaging all 24.1-1 [45.8 kB] Get: 285 http://deb.debian.org/debian trixie/main armhf python3-pyproject-hooks all 1.1.0-2 [11.3 kB] Get: 286 http://deb.debian.org/debian trixie/main armhf python3-toml all 0.10.2-1 [16.2 kB] Get: 287 http://deb.debian.org/debian trixie/main armhf python3-wheel all 0.44.0-2 [53.4 kB] Get: 288 http://deb.debian.org/debian trixie/main armhf python3-build all 1.2.2-1 [36.0 kB] Get: 289 http://deb.debian.org/debian trixie/main armhf python3-installer all 0.7.0+dfsg1-3 [18.6 kB] Get: 290 http://deb.debian.org/debian trixie/main armhf pybuild-plugin-pyproject all 6.20240824 [11.2 kB] Get: 291 http://deb.debian.org/debian trixie/main armhf python-babel-localedata all 2.14.0-1 [5701 kB] Get: 292 http://deb.debian.org/debian trixie/main armhf python-matplotlib-data all 3.8.3-3 [2731 kB] Get: 293 http://deb.debian.org/debian trixie/main armhf python3-alabaster all 0.7.16-0.1 [27.9 kB] Get: 294 http://deb.debian.org/debian trixie/main armhf python3-all armhf 3.12.6-1 [1040 B] Get: 295 http://deb.debian.org/debian trixie/main armhf python3-appdirs all 1.4.4-4 [12.5 kB] Get: 296 http://deb.debian.org/debian trixie/main armhf python3-attr all 23.2.0-2 [65.5 kB] Get: 297 http://deb.debian.org/debian trixie/main armhf python3-tz all 2024.1-2 [30.9 kB] Get: 298 http://deb.debian.org/debian trixie/main armhf python3-babel all 2.14.0-1 [111 kB] Get: 299 http://deb.debian.org/debian trixie/main armhf python3-brotli armhf 1.1.0-2+b4 [303 kB] Get: 300 http://deb.debian.org/debian trixie/main armhf python3-certifi all 2024.8.30-1 [159 kB] Get: 301 http://deb.debian.org/debian trixie/main armhf python3-chardet all 5.2.0+dfsg-1 [107 kB] Get: 302 http://deb.debian.org/debian trixie/main armhf python3-charset-normalizer armhf 3.3.2-4 [109 kB] Get: 303 http://deb.debian.org/debian trixie/main armhf python3-numpy armhf 1:1.26.4+ds-11 [3340 kB] Get: 304 http://deb.debian.org/debian trixie/main armhf python3-contourpy armhf 1.3.0-2 [181 kB] Get: 305 http://deb.debian.org/debian trixie/main armhf python3-coverage armhf 7.6.0+dfsg1-1 [173 kB] Get: 306 http://deb.debian.org/debian trixie/main armhf python3-cycler all 0.12.1-1 [9496 B] Get: 307 http://deb.debian.org/debian trixie/main armhf python3-dateutil all 2.9.0-3 [79.3 kB] Get: 308 http://deb.debian.org/debian trixie/main armhf python3-decorator all 5.1.1-5 [15.1 kB] Get: 309 http://deb.debian.org/debian trixie/main armhf python3-defusedxml all 0.7.1-2 [43.3 kB] Get: 310 http://deb.debian.org/debian trixie/main armhf python3-roman all 4.2-1 [10.4 kB] Get: 311 http://deb.debian.org/debian trixie/main armhf python3-docutils all 0.21.2+dfsg-2 [403 kB] Get: 312 http://deb.debian.org/debian trixie/main armhf python3-scipy armhf 1.13.1-5 [15.2 MB] Get: 313 http://deb.debian.org/debian trixie/main armhf python3-ufolib2 all 0.16.0+dfsg1-1 [32.9 kB] Get: 314 http://deb.debian.org/debian trixie/main armhf python3-mpmath all 1.3.0-1 [419 kB] Get: 315 http://deb.debian.org/debian trixie/main armhf python3-sympy all 1.13.2-1 [4147 kB] Get: 316 http://deb.debian.org/debian trixie/main armhf python3-six all 1.16.0-7 [16.4 kB] Get: 317 http://deb.debian.org/debian trixie/main armhf python3-fs all 2.4.16-4 [95.4 kB] Get: 318 http://deb.debian.org/debian trixie/main armhf python3-lxml armhf 5.3.0-1 [1152 kB] Get: 319 http://deb.debian.org/debian trixie/main armhf python3-lz4 armhf 4.0.2+dfsg-1+b4 [23.4 kB] Get: 320 http://deb.debian.org/debian trixie/main armhf unicode-data all 15.1.0-1 [8547 kB] Get: 321 http://deb.debian.org/debian trixie/main armhf python3-fonttools armhf 4.46.0-1+b1 [1336 kB] Get: 322 http://deb.debian.org/debian trixie/main armhf python3-pathspec all 0.12.1-1 [28.1 kB] Get: 323 http://deb.debian.org/debian trixie/main armhf python3-pluggy all 1.5.0-1 [26.9 kB] Get: 324 http://deb.debian.org/debian trixie/main armhf python3-trove-classifiers all 2024.9.12-1 [10.2 kB] Get: 325 http://deb.debian.org/debian trixie/main armhf python3-hatchling all 1.25.0-1 [53.8 kB] Get: 326 http://deb.debian.org/debian trixie/main armhf python3-setuptools-scm all 8.1.0-1 [40.5 kB] Get: 327 http://deb.debian.org/debian trixie/main armhf python3-hatch-vcs all 0.4.0-1 [8336 B] Get: 328 http://deb.debian.org/debian trixie/main armhf python3-idna all 3.8-2 [41.6 kB] Get: 329 http://deb.debian.org/debian trixie/main armhf python3-imagesize all 1.4.1-1 [6688 B] Get: 330 http://deb.debian.org/debian trixie/main armhf python3-iniconfig all 1.1.1-2 [6396 B] Get: 331 http://deb.debian.org/debian trixie/main armhf python3-markupsafe armhf 2.1.5-1+b1 [13.2 kB] Get: 332 http://deb.debian.org/debian trixie/main armhf python3-jinja2 all 3.1.3-1 [119 kB] Get: 333 http://deb.debian.org/debian trixie/main armhf python3-joblib all 1.3.2-2 [218 kB] Get: 334 http://deb.debian.org/debian trixie/main armhf python3-kiwisolver armhf 1.4.7-1 [52.1 kB] Get: 335 http://deb.debian.org/debian trixie/main armhf python3-lazy-loader all 0.4-1 [13.5 kB] Get: 336 http://deb.debian.org/debian trixie/main armhf python3-pil armhf 10.4.0-1 [465 kB] Get: 337 http://deb.debian.org/debian trixie/main armhf python3.12-tk armhf 3.12.6-1 [109 kB] Get: 338 http://deb.debian.org/debian trixie/main armhf python3.13-tk armhf 3.13.0-1 [99.6 kB] Get: 339 http://deb.debian.org/debian trixie/main armhf python3-tk armhf 3.12.6-1 [9428 B] Get: 340 http://deb.debian.org/debian trixie/main armhf python3-pil.imagetk armhf 10.4.0-1 [78.8 kB] Get: 341 http://deb.debian.org/debian trixie/main armhf python3-pyparsing all 3.1.2-1 [146 kB] Get: 342 http://deb.debian.org/debian trixie/main armhf python3-matplotlib armhf 3.8.3-3 [5500 kB] Get: 343 http://deb.debian.org/debian trixie/main armhf python3-mpi4py armhf 4.0.0-8 [644 kB] Get: 344 http://deb.debian.org/debian trixie/main armhf python3-nibabel all 5.2.1-2 [2659 kB] Get: 345 http://deb.debian.org/debian trixie/main armhf python3-platformdirs all 4.3.6-1 [16.6 kB] Get: 346 http://deb.debian.org/debian trixie/main armhf python3-urllib3 all 2.0.7-2 [111 kB] Get: 347 http://deb.debian.org/debian trixie/main armhf python3-requests all 2.32.3+dfsg-1 [71.9 kB] Get: 348 http://deb.debian.org/debian trixie/main armhf python3-pooch all 1.8.2-1 [58.4 kB] Get: 349 http://deb.debian.org/debian trixie/main armhf python3-pygments all 2.18.0+dfsg-1 [836 kB] Get: 350 http://deb.debian.org/debian trixie/main armhf python3-pyqt6.sip armhf 13.8.0-1 [42.3 kB] Get: 351 http://deb.debian.org/debian trixie/main armhf python3-pyqt6 armhf 6.7.1-1 [1994 kB] Get: 352 http://deb.debian.org/debian trixie/main armhf python3-pytest all 8.3.3-1 [249 kB] Get: 353 http://deb.debian.org/debian trixie/main armhf python3-pytest-cov all 5.0.0-1 [26.8 kB] Get: 354 http://deb.debian.org/debian trixie/main armhf python3-pytest-timeout all 2.3.1-1 [21.9 kB] Get: 355 http://deb.debian.org/debian trixie/main armhf python3-pytestqt all 4.3.1-1 [41.5 kB] Get: 356 http://deb.debian.org/debian trixie/main armhf python3-threadpoolctl all 3.1.0-1 [21.2 kB] Get: 357 http://deb.debian.org/debian trixie/main armhf python3-sklearn-lib armhf 1.4.2+dfsg-6 [3163 kB] Get: 358 http://deb.debian.org/debian trixie/main armhf python3-sklearn all 1.4.2+dfsg-6 [2248 kB] Get: 359 http://deb.debian.org/debian trixie/main armhf python3-snowballstemmer all 2.2.0-4 [58.0 kB] Get: 360 http://deb.debian.org/debian trixie/main armhf sphinx-common all 7.4.7-3 [731 kB] Get: 361 http://deb.debian.org/debian trixie/main armhf python3-sphinx all 7.4.7-3 [588 kB] Get: 362 http://deb.debian.org/debian trixie/main armhf python3-tqdm all 4.66.5-1 [90.1 kB] Get: 363 http://deb.debian.org/debian trixie/main armhf python3-vtk9 armhf 9.3.0+dfsg1-1+b2 [6284 kB] Get: 364 http://deb.debian.org/debian trixie/main armhf x11-xkb-utils armhf 7.7+9 [145 kB] Get: 365 http://deb.debian.org/debian trixie/main armhf xauth armhf 1:1.1.2-1 [33.2 kB] Get: 366 http://deb.debian.org/debian trixie/main armhf xserver-common all 2:21.1.13-2 [2393 kB] Get: 367 http://deb.debian.org/debian trixie/main armhf xvfb armhf 2:21.1.13-2 [3029 kB] Get: 368 http://deb.debian.org/debian trixie/main armhf yui-compressor all 2.4.8-3 [604 kB] Fetched 258 MB in 5s (48.7 MB/s) debconf: delaying package configuration, since apt-utils is not installed Selecting previously unselected package libjs-jquery. (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 19508 files and directories currently installed.) Preparing to unpack .../libjs-jquery_3.6.1+dfsg+~3.5.14-1_all.deb ... Unpacking libjs-jquery (3.6.1+dfsg+~3.5.14-1) ... Selecting previously unselected package libjs-jquery-hotkeys. Preparing to unpack .../libjs-jquery-hotkeys_0~20130707+git2d51e3a9+dfsg-2.1_all.deb ... Unpacking libjs-jquery-hotkeys (0~20130707+git2d51e3a9+dfsg-2.1) ... Selecting previously unselected package libpython3.12-minimal:armhf. Preparing to unpack .../libpython3.12-minimal_3.12.6-1_armhf.deb ... Unpacking libpython3.12-minimal:armhf (3.12.6-1) ... Selecting previously unselected package libexpat1:armhf. Preparing to unpack .../libexpat1_2.6.3-1_armhf.deb ... Unpacking libexpat1:armhf (2.6.3-1) ... Selecting previously unselected package python3.12-minimal. Preparing to unpack .../python3.12-minimal_3.12.6-1_armhf.deb ... Unpacking python3.12-minimal (3.12.6-1) ... Setting up libpython3.12-minimal:armhf (3.12.6-1) ... Setting up libexpat1:armhf (2.6.3-1) ... Setting up python3.12-minimal (3.12.6-1) ... Selecting previously unselected package python3-minimal. (Reading database ... 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Selecting previously unselected package libgssapi-krb5-2:armhf. Preparing to unpack .../09-libgssapi-krb5-2_1.21.3-3_armhf.deb ... Unpacking libgssapi-krb5-2:armhf (1.21.3-3) ... Selecting previously unselected package libtirpc-common. Preparing to unpack .../10-libtirpc-common_1.3.4+ds-1.3_all.deb ... Unpacking libtirpc-common (1.3.4+ds-1.3) ... Selecting previously unselected package libtirpc3t64:armhf. Preparing to unpack .../11-libtirpc3t64_1.3.4+ds-1.3_armhf.deb ... Adding 'diversion of /lib/arm-linux-gnueabihf/libtirpc.so.3 to /lib/arm-linux-gnueabihf/libtirpc.so.3.usr-is-merged by libtirpc3t64' Adding 'diversion of /lib/arm-linux-gnueabihf/libtirpc.so.3.0.0 to /lib/arm-linux-gnueabihf/libtirpc.so.3.0.0.usr-is-merged by libtirpc3t64' Unpacking libtirpc3t64:armhf (1.3.4+ds-1.3) ... Selecting previously unselected package libnsl2:armhf. Preparing to unpack .../12-libnsl2_1.3.0-3+b2_armhf.deb ... Unpacking libnsl2:armhf (1.3.0-3+b2) ... Selecting previously unselected package readline-common. Preparing to unpack .../13-readline-common_8.2-5_all.deb ... Unpacking readline-common (8.2-5) ... Selecting previously unselected package libreadline8t64:armhf. Preparing to unpack .../14-libreadline8t64_8.2-5_armhf.deb ... Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8 to /lib/arm-linux-gnueabihf/libhistory.so.8.usr-is-merged by libreadline8t64' Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8.2 to /lib/arm-linux-gnueabihf/libhistory.so.8.2.usr-is-merged by libreadline8t64' Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8 to /lib/arm-linux-gnueabihf/libreadline.so.8.usr-is-merged by libreadline8t64' Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8.2 to /lib/arm-linux-gnueabihf/libreadline.so.8.2.usr-is-merged by libreadline8t64' Unpacking libreadline8t64:armhf (8.2-5) ... Selecting previously unselected package libpython3.12-stdlib:armhf. Preparing to unpack .../15-libpython3.12-stdlib_3.12.6-1_armhf.deb ... Unpacking libpython3.12-stdlib:armhf (3.12.6-1) ... Selecting previously unselected package python3.12. Preparing to unpack .../16-python3.12_3.12.6-1_armhf.deb ... Unpacking python3.12 (3.12.6-1) ... Selecting previously unselected package libpython3-stdlib:armhf. Preparing to unpack .../17-libpython3-stdlib_3.12.6-1_armhf.deb ... Unpacking libpython3-stdlib:armhf (3.12.6-1) ... Setting up python3-minimal (3.12.6-1) ... Selecting previously unselected package python3. (Reading database ... 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Selecting previously unselected package tk8.6-blt2.5. Preparing to unpack .../040-tk8.6-blt2.5_2.5.3+dfsg-7_armhf.deb ... Unpacking tk8.6-blt2.5 (2.5.3+dfsg-7) ... Selecting previously unselected package blt. Preparing to unpack .../041-blt_2.5.3+dfsg-7_armhf.deb ... Unpacking blt (2.5.3+dfsg-7) ... Selecting previously unselected package ca-certificates-java. Preparing to unpack .../042-ca-certificates-java_20240118_all.deb ... Unpacking ca-certificates-java (20240118) ... Selecting previously unselected package libdebhelper-perl. Preparing to unpack .../043-libdebhelper-perl_13.20_all.deb ... Unpacking libdebhelper-perl (13.20) ... Selecting previously unselected package libtool. Preparing to unpack .../044-libtool_2.4.7-7_all.deb ... Unpacking libtool (2.4.7-7) ... Selecting previously unselected package dh-autoreconf. Preparing to unpack .../045-dh-autoreconf_20_all.deb ... Unpacking dh-autoreconf (20) ... Selecting previously unselected package libarchive-zip-perl. 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Selecting previously unselected package liblcms2-2:armhf. Preparing to unpack .../058-liblcms2-2_2.14-2+b1_armhf.deb ... Unpacking liblcms2-2:armhf (2.14-2+b1) ... Selecting previously unselected package libjpeg62-turbo:armhf. Preparing to unpack .../059-libjpeg62-turbo_1%3a2.1.5-3_armhf.deb ... Unpacking libjpeg62-turbo:armhf (1:2.1.5-3) ... Selecting previously unselected package libnspr4:armhf. Preparing to unpack .../060-libnspr4_2%3a4.35-1.1+b1_armhf.deb ... Unpacking libnspr4:armhf (2:4.35-1.1+b1) ... Selecting previously unselected package libnss3:armhf. Preparing to unpack .../061-libnss3_2%3a3.105-2_armhf.deb ... Unpacking libnss3:armhf (2:3.105-2) ... Selecting previously unselected package libpcsclite1:armhf. Preparing to unpack .../062-libpcsclite1_2.3.0-1_armhf.deb ... Unpacking libpcsclite1:armhf (2.3.0-1) ... Selecting previously unselected package openjdk-21-jre-headless:armhf. Preparing to unpack .../063-openjdk-21-jre-headless_21.0.5~8ea-1_armhf.deb ... 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No schema files found: doing nothing. Setting up libblas3:armhf (3.12.0-3) ... update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so.3 to provide /usr/lib/arm-linux-gnueabihf/libblas.so.3 (libblas.so.3-arm-linux-gnueabihf) in auto mode Setting up libxcb-xinerama0:armhf (1.17.0-2) ... Setting up libgles2:armhf (1.7.0-1+b1) ... Setting up libjpeg62-turbo:armhf (1:2.1.5-3) ... Setting up libx11-data (2:1.8.7-1) ... Setting up libnspr4:armhf (2:4.35-1.1+b1) ... Setting up libxcb-sync1:armhf (1.17.0-2) ... Setting up librtmp1:armhf (2.4+20151223.gitfa8646d.1-2+b4) ... Setting up libxcb-cursor0:armhf (0.1.4-1+b1) ... Setting up libavahi-common-data:armhf (0.8-13+b2) ... Setting up libdbus-1-3:armhf (1.14.10-4+b1) ... Setting up libfribidi0:armhf (1.0.15-1) ... Setting up libimagequant0:armhf (2.18.0-1+b1) ... Setting up libproc2-0:armhf (2:4.0.4-6) ... Setting up fonts-dejavu-mono (2.37-8) ... Setting up libpng16-16t64:armhf (1.6.44-2) ... 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Setting up libxshmfence1:armhf (1.3-1+b1) ... Setting up libtiff6:armhf (4.5.1+git230720-5) ... Setting up libxcb-randr0:armhf (1.17.0-2) ... Setting up libuchardet0:armhf (0.0.8-1+b1) ... Setting up procps (2:4.0.4-6) ... Setting up libjson-perl (4.10000-1) ... Setting up libjargs-java (1.0.0-5) ... Setting up libnl-3-200:armhf (3.7.0-0.3) ... Setting up libmd4c0:armhf (0.5.2-2+b1) ... Setting up libopenjp2-7:armhf (2.5.0-2+b3) ... Setting up libx11-6:armhf (2:1.8.7-1+b1) ... Setting up libslurm41t64 (24.05.2-1) ... Setting up netbase (6.4) ... Setting up libngtcp2-16:armhf (1.6.0-1) ... Setting up sgml-base (1.31) ... Setting up libkrb5-3:armhf (1.21.3-3) ... Setting up libevent-core-2.1-7t64:armhf (2.1.12-stable-10) ... Setting up libxkbfile1:armhf (1:1.1.0-1+b1) ... Setting up libssh2-1t64:armhf (1.11.0-7) ... Setting up libjs-jquery (3.6.1+dfsg+~3.5.14-1) ... Setting up libjs-jquery-hotkeys (0~20130707+git2d51e3a9+dfsg-2.1) ... Setting up python-matplotlib-data (3.8.3-3) ... Setting up openssl (3.3.2-1) ... Setting up libwebpmux3:armhf (1.4.0-0.1) ... Setting up libdrm-common (2.4.123-1) ... Setting up libevdev2:armhf (1.13.3+dfsg-1) ... Setting up readline-common (8.2-5) ... Setting up libxml2:armhf (2.12.7+dfsg+really2.9.14-0.1) ... Setting up libxmuu1:armhf (2:1.1.3-3+b2) ... Setting up libgudev-1.0-0:armhf (238-5) ... Setting up libngtcp2-crypto-gnutls8:armhf (1.6.0-1) ... Setting up libsz2:armhf (1.1.3-1) ... Setting up libjs-underscore (1.13.4~dfsg+~1.11.4-3) ... Setting up libwacom-common (2.13.0-1) ... Setting up libxkbcommon0:armhf (1.6.0-1+b1) ... Setting up libwayland-client0:armhf (1.23.0-1) ... Setting up automake (1:1.16.5-1.3) ... update-alternatives: using /usr/bin/automake-1.16 to provide /usr/bin/automake (automake) in auto mode Setting up libfile-stripnondeterminism-perl (1.14.0-1) ... Setting up libxcb-dri3-0:armhf (1.17.0-2) ... Setting up libllvm19:armhf (1:19.1.1-1) ... Setting up libx11-xcb1:armhf (2:1.8.7-1+b1) ... Setting up libice6:armhf (2:1.0.10-1+b1) ... Setting up liblapack3:armhf (3.12.0-3) ... update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so.3 to provide /usr/lib/arm-linux-gnueabihf/liblapack.so.3 (liblapack.so.3-arm-linux-gnueabihf) in auto mode Setting up gettext (0.22.5-2) ... Setting up java-wrappers (0.5) ... Setting up libxpm4:armhf (1:3.5.17-1+b1) ... Setting up libxrender1:armhf (1:0.9.10-1.1+b1) ... Setting up libtool (2.4.7-7) ... Setting up libwacom9:armhf (2.13.0-1) ... Setting up libevent-pthreads-2.1-7t64:armhf (2.1.12-stable-10) ... Setting up fontconfig-config (2.15.0-1.1) ... Setting up libwebpdemux2:armhf (1.4.0-0.1) ... Setting up hwloc-nox (2.11.2-1) ... Setting up libavahi-common3:armhf (0.8-13+b2) ... Setting up libxext6:armhf (2:1.3.4-1+b1) ... Setting up libnss3:armhf (2:3.105-2) ... Setting up libldap-2.5-0:armhf (2.5.18+dfsg-3) ... Setting up libxxf86vm1:armhf (1:1.1.4-1+b2) ... Setting up libinput-bin (1.26.2-1) ... Setting up libtbbbind-2-5:armhf (2021.12.0-1) ... Setting up intltool-debian (0.35.0+20060710.6) ... Setting up libnl-route-3-200:armhf (3.7.0-0.3) ... Setting up libxnvctrl0:armhf (535.171.04-1) ... Setting up dh-autoreconf (20) ... Setting up ca-certificates (20240203) ... Updating certificates in /etc/ssl/certs... 146 added, 0 removed; done. Setting up libjs-jquery-ui (1.13.2+dfsg-1) ... Setting up libfreetype6:armhf (2.13.3+dfsg-1) ... Setting up libxfixes3:armhf (1:6.0.0-2+b1) ... Setting up libjs-jquery-metadata (12-4) ... Setting up shared-mime-info (2.4-5+b1) ... Setting up libxkbcommon-x11-0:armhf (1.6.0-1+b1) ... Setting up libjs-jquery-isonscreen (1.2.0-1.1) ... Setting up libgssapi-krb5-2:armhf (1.21.3-3) ... Setting up libxrandr2:armhf (2:1.5.4-1) ... Setting up libjs-sphinxdoc (7.4.7-3) ... Setting up libreadline8t64:armhf (8.2-5) ... Setting up dh-strip-nondeterminism (1.14.0-1) ... Setting up libdrm2:armhf (2.4.123-1) ... Setting up libjs-jquery-tablesorter (1:2.31.3+dfsg1-4) ... Setting up xauth (1:1.1.2-1) ... Setting up groff-base (1.23.0-5) ... Setting up xml-core (0.19) ... Setting up libxslt1.1:armhf (1.1.35-1.1) ... Setting up libharfbuzz0b:armhf (9.0.0-1) ... Setting up libxss1:armhf (1:1.2.3-1+b1) ... Setting up libfontconfig1:armhf (2.15.0-1.1) ... Setting up ca-certificates-java (20240118) ... No JRE found. Skipping Java certificates setup. Setting up libsm6:armhf (2:1.2.3-1+b1) ... Setting up libxfont2:armhf (1:2.0.6-1+b1) ... Setting up libavahi-client3:armhf (0.8-13+b2) ... Setting up liblbfgsb0:armhf (3.0+dfsg.4-1+b1) ... Setting up libdrm-amdgpu1:armhf (2.4.123-1) ... Setting up libinput10:armhf (1.26.2-1) ... Setting up libqt6core6t64:armhf (6.6.2+dfsg-12) ... Setting up libibverbs1:armhf (52.0-2) ... Setting up fontconfig (2.15.0-1.1) ... Regenerating fonts cache... done. Setting up libxft2:armhf (2.3.6-1+b1) ... Setting up libqt6test6:armhf (6.6.2+dfsg-12) ... Setting up openjdk-21-jre-headless:armhf (21.0.5~8ea-1) ... update-alternatives: using /usr/lib/jvm/java-21-openjdk-armhf/bin/java to provide /usr/bin/java (java) in auto mode update-alternatives: using /usr/lib/jvm/java-21-openjdk-armhf/bin/jpackage to provide /usr/bin/jpackage (jpackage) in auto mode update-alternatives: using /usr/lib/jvm/java-21-openjdk-armhf/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode update-alternatives: using /usr/lib/jvm/java-21-openjdk-armhf/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode update-alternatives: using /usr/lib/jvm/java-21-openjdk-armhf/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode Setting up libcurl4t64:armhf (8.10.1-1) ... Setting up libtirpc3t64:armhf (1.3.4+ds-1.3) ... Setting up libdrm-radeon1:armhf (2.4.123-1) ... Setting up mpich (4.2.0-14) ... update-alternatives: using /usr/bin/mpicc.mpich to provide /usr/bin/mpicc (mpi) in auto mode update-alternatives: using /usr/bin/mpirun.mpich to provide /usr/bin/mpirun (mpirun) in auto mode Setting up po-debconf (1.0.21+nmu1) ... Setting up libtk8.6:armhf (8.6.15-1) ... Setting up python3.12-tk (3.12.6-1) ... Setting up mpi-default-bin (1.17) ... Setting up libcurl3t64-gnutls:armhf (8.10.1-1) ... Setting up libtbb12:armhf (2021.12.0-1) ... Setting up libhwloc-plugins:armhf (2.11.2-1) ... Setting up man-db (2.13.0-1) ... Not building database; man-db/auto-update is not 'true'. Setting up libcairo2:armhf (1.18.2-2) ... Setting up python3.13-tk (3.13.0-1) ... Setting up libqt5core5t64:armhf (5.15.13+dfsg-4) ... Setting up libqt6xml6:armhf (6.6.2+dfsg-12) ... Setting up libqt6sql6:armhf (6.6.2+dfsg-12) ... Setting up libraqm0:armhf (0.10.1-1+b1) ... Setting up sphinx-common (7.4.7-3) ... Setting up libxt6t64:armhf (1:1.2.1-1.2) ... Setting up libtheora0:armhf (1.1.1+dfsg.1-17) ... Setting up libnsl2:armhf (1.3.0-3+b2) ... Setting up librdmacm1t64:armhf (52.0-2) ... Setting up libhdf5-103-1t64:armhf (1.10.10+repack-4) ... Setting up libcups2t64:armhf (2.4.10-2) ... Setting up libqt6dbus6:armhf (6.6.2+dfsg-12) ... Setting up libfabric1:armhf (1.17.0-3+b1) ... Setting up libhdf5-hl-100t64:armhf (1.10.10+repack-4) ... Setting up mesa-libgallium:armhf (24.2.4-1) ... Setting up tk8.6-blt2.5 (2.5.3+dfsg-7) ... Setting up libnetcdf19t64:armhf (1:4.9.2-7) ... Setting up libproxy1v5:armhf (0.5.8-1) ... Setting up libxmu6:armhf (2:1.1.3-3+b2) ... Setting up libqt5dbus5t64:armhf (5.15.13+dfsg-4) ... Setting up libpython3.12-stdlib:armhf (3.12.6-1) ... Setting up libproj25:armhf (9.5.0-1) ... Setting up libgbm1:armhf (24.2.4-1) ... Setting up blt (2.5.3+dfsg-7) ... Setting up python3.12 (3.12.6-1) ... Setting up libgl1-mesa-dri:armhf (24.2.4-1) ... Setting up libqt5network5t64:armhf (5.15.13+dfsg-4) ... Setting up debhelper (13.20) ... Setting up libxaw7:armhf (2:1.0.14-1+b2) ... Setting up libpython3.12t64:armhf (3.12.6-1) ... Setting up libegl-mesa0:armhf (24.2.4-1) ... Setting up libopenmpi3t64:armhf (4.1.6-13.3) ... Setting up libqt6network6:armhf (6.6.2+dfsg-12) ... Setting up libegl1:armhf (1.7.0-1+b1) ... Setting up libpython3-stdlib:armhf (3.12.6-1) ... Setting up libglx-mesa0:armhf (24.2.4-1) ... Setting up libglx0:armhf (1.7.0-1+b1) ... Setting up x11-xkb-utils (7.7+9) ... Setting up python3 (3.12.6-1) ... Setting up python3-zipp (3.20.2-1) ... Setting up python3-mpi4py (4.0.0-8) ... Setting up python3-autocommand (2.2.2-3) ... Setting up python3-markupsafe (2.1.5-1+b1) ... Setting up python3-wheel (0.44.0-2) ... Setting up python3-platformdirs (4.3.6-1) ... Setting up python3-tz (2024.1-2) ... Setting up python3-six (1.16.0-7) ... Setting up python3-pil:armhf (10.4.0-1) ... Setting up python3-roman (4.2-1) ... Setting up python3-decorator (5.1.1-5) ... Setting up python3-jinja2 (3.1.3-1) ... Setting up libqt5gui5t64:armhf (5.15.13+dfsg-4) ... Setting up python3-packaging (24.1-1) ... Setting up libgl1:armhf (1.7.0-1+b1) ... Setting up libqt6gui6:armhf (6.6.2+dfsg-12) ... Setting up python3-pyproject-hooks (1.1.0-2) ... Setting up python3-pyparsing (3.1.2-1) ... Setting up python3-certifi (2024.8.30-1) ... Setting up python3-snowballstemmer (2.2.0-4) ... Setting up python3-brotli (1.1.0-2+b4) ... Setting up python3-cycler (0.12.1-1) ... Setting up python3-kiwisolver (1.4.7-1) ... Setting up python3-idna (3.8-2) ... Setting up python3-typing-extensions (4.12.2-2) ... Setting up libglew2.2:armhf (2.2.0-4+b1) ... Setting up python3-toml (0.10.2-1) ... Setting up python3-installer (0.7.0+dfsg1-3) ... Setting up python3-urllib3 (2.0.7-2) ... Setting up python3-pluggy (1.5.0-1) ... Setting up python3-trove-classifiers (2024.9.12-1) ... Setting up python3-lxml:armhf (5.3.0-1) ... Setting up python3-dateutil (2.9.0-3) ... Setting up python3-lazy-loader (0.4-1) ... Setting up xserver-common (2:21.1.13-2) ... Setting up python3-mpmath (1.3.0-1) ... Setting up python3-pyqt6.sip (13.8.0-1) ... Setting up python3-build (1.2.2-1) ... Setting up python3-pathspec (0.12.1-1) ... Setting up python3-appdirs (1.4.4-4) ... Setting up python3-imagesize (1.4.1-1) ... Setting up python3-more-itertools (10.5.0-1) ... Setting up python3-iniconfig (1.1.1-2) ... Setting up python3-sympy (1.13.2-1) ... Setting up python3-attr (23.2.0-2) ... Setting up python3-jaraco.functools (4.1.0-1) ... Setting up python3-jaraco.context (6.0.0-1) ... Setting up libqt5widgets5t64:armhf (5.15.13+dfsg-4) ... Setting up python3-lz4 (4.0.2+dfsg-1+b4) ... Setting up python3-defusedxml (0.7.1-2) ... Setting up python3-charset-normalizer (3.3.2-4) ... Setting up python3-pytest (8.3.3-1) ... Setting up python3-alabaster (0.7.16-0.1) ... Setting up xvfb (2:21.1.13-2) ... Setting up python3-tqdm (4.66.5-1) ... Setting up python3-typeguard (4.3.0-1) ... Setting up python3-threadpoolctl (3.1.0-1) ... Setting up libqt6opengl6:armhf (6.6.2+dfsg-12) ... Setting up python3-all (3.12.6-1) ... Setting up python3-tk:armhf (3.12.6-1) ... Setting up libgl2ps1.4 (1.4.2+dfsg1-2) ... Setting up libqt6widgets6:armhf (6.6.2+dfsg-12) ... Setting up python3-inflect (7.3.1-2) ... Setting up python3-pil.imagetk:armhf (10.4.0-1) ... Setting up libvtk9.3:armhf (9.3.0+dfsg1-1+b2) ... Setting up python3-hatchling (1.25.0-1) ... Setting up libqt6openglwidgets6:armhf (6.6.2+dfsg-12) ... Setting up python3-pytestqt (4.3.1-1) ... Setting up python3-pkg-resources (74.1.2-2) ... Setting up libqt6printsupport6:armhf (6.6.2+dfsg-12) ... Setting up python3-setuptools (74.1.2-2) ... Setting up libvtk9.3-qt:armhf (9.3.0+dfsg1-1+b2) ... Setting up python3-pytest-timeout (2.3.1-1) ... Setting up python3-joblib (1.3.2-2) ... Setting up python3-pyqt6 (6.7.1-1) ... Setting up python3-babel (2.14.0-1) ... update-alternatives: using /usr/bin/pybabel-python3 to provide /usr/bin/pybabel (pybabel) in auto mode Setting up python3-coverage (7.6.0+dfsg1-1) ... Setting up python3-pytest-cov (5.0.0-1) ... Setting up python3-setuptools-scm (8.1.0-1) ... Setting up python3-fs (2.4.16-4) ... Setting up python3-pygments (2.18.0+dfsg-1) ... Setting up python3-chardet (5.2.0+dfsg-1) ... Setting up python3-requests (2.32.3+dfsg-1) ... Setting up python3-numpy (1:1.26.4+ds-11) ... Setting up python3-contourpy (1.3.0-2) ... Setting up python3-hatch-vcs (0.4.0-1) ... Setting up dh-python (6.20240824) ... Setting up python3-scipy (1.13.1-5) ... Setting up python3-vtk9 (9.3.0+dfsg1-1+b2) ... /usr/lib/python3/dist-packages/vtkmodules/util/vtkMethodParser.py:304: SyntaxWarning: invalid escape sequence '\S' patn = re.compile (" \S") Setting up pybuild-plugin-pyproject (6.20240824) ... Setting up python3-sklearn-lib:armhf (1.4.2+dfsg-6) ... Setting up python3-pooch (1.8.2-1) ... Setting up python3-nibabel (5.2.1-2) ... Setting up python3-sklearn (1.4.2+dfsg-6) ... Setting up python3-fonttools (4.46.0-1+b1) ... Setting up python3-ufolib2 (0.16.0+dfsg1-1) ... Setting up python3-matplotlib (3.8.3-3) ... Processing triggers for libc-bin (2.40-3) ... Processing triggers for ca-certificates-java (20240118) ... Adding debian:ACCVRAIZ1.pem Adding debian:AC_RAIZ_FNMT-RCM.pem Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem Adding debian:ANF_Secure_Server_Root_CA.pem Adding debian:Actalis_Authentication_Root_CA.pem Adding debian:AffirmTrust_Commercial.pem Adding debian:AffirmTrust_Networking.pem Adding debian:AffirmTrust_Premium.pem Adding debian:AffirmTrust_Premium_ECC.pem Adding debian:Amazon_Root_CA_1.pem Adding debian:Amazon_Root_CA_2.pem Adding debian:Amazon_Root_CA_3.pem Adding debian:Amazon_Root_CA_4.pem Adding debian:Atos_TrustedRoot_2011.pem Adding debian:Atos_TrustedRoot_Root_CA_ECC_TLS_2021.pem Adding debian:Atos_TrustedRoot_Root_CA_RSA_TLS_2021.pem Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem Adding debian:BJCA_Global_Root_CA1.pem Adding debian:BJCA_Global_Root_CA2.pem Adding debian:Baltimore_CyberTrust_Root.pem Adding debian:Buypass_Class_2_Root_CA.pem Adding debian:Buypass_Class_3_Root_CA.pem Adding debian:CA_Disig_Root_R2.pem Adding debian:CFCA_EV_ROOT.pem Adding debian:COMODO_Certification_Authority.pem Adding debian:COMODO_ECC_Certification_Authority.pem Adding debian:COMODO_RSA_Certification_Authority.pem Adding debian:Certainly_Root_E1.pem Adding debian:Certainly_Root_R1.pem Adding debian:Certigna.pem Adding debian:Certigna_Root_CA.pem Adding debian:Certum_EC-384_CA.pem Adding debian:Certum_Trusted_Network_CA.pem Adding debian:Certum_Trusted_Network_CA_2.pem Adding debian:Certum_Trusted_Root_CA.pem Adding debian:CommScope_Public_Trust_ECC_Root-01.pem Adding debian:CommScope_Public_Trust_ECC_Root-02.pem Adding debian:CommScope_Public_Trust_RSA_Root-01.pem Adding debian:CommScope_Public_Trust_RSA_Root-02.pem Adding debian:Comodo_AAA_Services_root.pem Adding debian:D-TRUST_BR_Root_CA_1_2020.pem Adding debian:D-TRUST_EV_Root_CA_1_2020.pem Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem Adding debian:DigiCert_Assured_ID_Root_CA.pem Adding debian:DigiCert_Assured_ID_Root_G2.pem Adding debian:DigiCert_Assured_ID_Root_G3.pem Adding debian:DigiCert_Global_Root_CA.pem Adding debian:DigiCert_Global_Root_G2.pem Adding debian:DigiCert_Global_Root_G3.pem Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem Adding debian:DigiCert_Trusted_Root_G4.pem Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem Adding debian:Entrust_Root_Certification_Authority.pem Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem Adding debian:Entrust_Root_Certification_Authority_-_G2.pem Adding debian:Entrust_Root_Certification_Authority_-_G4.pem Adding debian:GDCA_TrustAUTH_R5_ROOT.pem Adding debian:GLOBALTRUST_2020.pem Adding debian:GTS_Root_R1.pem Adding debian:GTS_Root_R2.pem Adding debian:GTS_Root_R3.pem Adding debian:GTS_Root_R4.pem Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem Adding debian:GlobalSign_Root_CA.pem Adding debian:GlobalSign_Root_CA_-_R3.pem Adding debian:GlobalSign_Root_CA_-_R6.pem Adding debian:GlobalSign_Root_E46.pem Adding debian:GlobalSign_Root_R46.pem Adding debian:Go_Daddy_Class_2_CA.pem Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem Adding debian:HiPKI_Root_CA_-_G1.pem Adding debian:Hongkong_Post_Root_CA_3.pem Adding debian:ISRG_Root_X1.pem Adding debian:ISRG_Root_X2.pem Adding debian:IdenTrust_Commercial_Root_CA_1.pem Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem Adding debian:Izenpe.com.pem Adding debian:Microsec_e-Szigno_Root_CA_2009.pem Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem Adding debian:NAVER_Global_Root_Certification_Authority.pem Adding debian:NetLock_Arany_=Class_Gold=_Főtanúsítvány.pem Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem Adding debian:QuoVadis_Root_CA_1_G3.pem Adding debian:QuoVadis_Root_CA_2.pem Adding debian:QuoVadis_Root_CA_2_G3.pem Adding debian:QuoVadis_Root_CA_3.pem Adding debian:QuoVadis_Root_CA_3_G3.pem Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem Adding debian:SSL.com_Root_Certification_Authority_ECC.pem Adding debian:SSL.com_Root_Certification_Authority_RSA.pem Adding debian:SSL.com_TLS_ECC_Root_CA_2022.pem Adding debian:SSL.com_TLS_RSA_Root_CA_2022.pem Adding debian:SZAFIR_ROOT_CA2.pem Adding debian:Sectigo_Public_Server_Authentication_Root_E46.pem Adding debian:Sectigo_Public_Server_Authentication_Root_R46.pem Adding debian:SecureSign_RootCA11.pem Adding debian:SecureTrust_CA.pem Adding debian:Secure_Global_CA.pem Adding debian:Security_Communication_ECC_RootCA1.pem Adding debian:Security_Communication_RootCA2.pem Adding debian:Security_Communication_RootCA3.pem Adding debian:Security_Communication_Root_CA.pem Adding debian:Starfield_Class_2_CA.pem Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem Adding debian:SwissSign_Gold_CA_-_G2.pem Adding debian:SwissSign_Silver_CA_-_G2.pem Adding debian:T-TeleSec_GlobalRoot_Class_2.pem Adding debian:T-TeleSec_GlobalRoot_Class_3.pem Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem Adding debian:TWCA_Global_Root_CA.pem Adding debian:TWCA_Root_Certification_Authority.pem Adding debian:TeliaSonera_Root_CA_v1.pem Adding debian:Telia_Root_CA_v2.pem Adding debian:TrustAsia_Global_Root_CA_G3.pem Adding debian:TrustAsia_Global_Root_CA_G4.pem Adding debian:Trustwave_Global_Certification_Authority.pem Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem Adding debian:TunTrust_Root_CA.pem Adding debian:UCA_Extended_Validation_Root.pem Adding debian:UCA_Global_G2_Root.pem Adding debian:USERTrust_ECC_Certification_Authority.pem Adding debian:USERTrust_RSA_Certification_Authority.pem Adding debian:XRamp_Global_CA_Root.pem Adding debian:certSIGN_ROOT_CA.pem Adding debian:certSIGN_Root_CA_G2.pem Adding debian:e-Szigno_Root_CA_2017.pem Adding debian:ePKI_Root_Certification_Authority.pem Adding debian:emSign_ECC_Root_CA_-_C3.pem Adding debian:emSign_ECC_Root_CA_-_G3.pem Adding debian:emSign_Root_CA_-_C1.pem Adding debian:emSign_Root_CA_-_G1.pem Adding debian:vTrus_ECC_Root_CA.pem Adding debian:vTrus_Root_CA.pem done. Setting up yui-compressor (2.4.8-3) ... Setting up default-jre-headless (2:1.21-76) ... Processing triggers for sgml-base (1.31) ... Setting up docutils-common (0.21.2+dfsg-2) ... Processing triggers for sgml-base (1.31) ... Setting up python3-docutils (0.21.2+dfsg-2) ... Setting up python3-sphinx (7.4.7-3) ... Processing triggers for ca-certificates (20240203) ... Updating certificates in /etc/ssl/certs... 0 added, 0 removed; done. Running hooks in /etc/ca-certificates/update.d... done. Processing triggers for ca-certificates-java (20240118) ... done. Reading package lists... Building dependency tree... Reading state information... Reading extended state information... Initializing package states... Writing extended state information... Building tag database... -> Finished parsing the build-deps I: Building the package I: Running cd /build/reproducible-path/python-mne-1.8.0/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-genchanges -S > ../python-mne_1.8.0-1_source.changes dpkg-buildpackage: info: source package python-mne dpkg-buildpackage: info: source version 1.8.0-1 dpkg-buildpackage: info: source distribution unstable dpkg-buildpackage: info: source changed by Étienne Mollier dpkg-source --before-build . dpkg-buildpackage: info: host architecture armhf debian/rules clean dh clean --buildsystem pybuild dh_auto_clean -O--buildsystem=pybuild dh_autoreconf_clean -O--buildsystem=pybuild debian/rules execute_before_dh_clean make[1]: Entering directory '/build/reproducible-path/python-mne-1.8.0' rm -rf *.egg-info make[1]: Leaving directory '/build/reproducible-path/python-mne-1.8.0' dh_clean -O--buildsystem=pybuild debian/rules binary dh binary --buildsystem pybuild dh_update_autotools_config -O--buildsystem=pybuild dh_autoreconf -O--buildsystem=pybuild dh_auto_configure -O--buildsystem=pybuild dh_auto_build -O--buildsystem=pybuild I: pybuild plugin_pyproject:129: Building wheel for python3.12 with "build" module I: pybuild base:311: python3.12 -m build --skip-dependency-check --no-isolation --wheel --outdir /build/reproducible-path/python-mne-1.8.0/.pybuild/cpython3_3.12_mne * Building wheel... Successfully built mne-1.8.0-py3-none-any.whl I: pybuild plugin_pyproject:144: Unpacking wheel built for python3.12 with "installer" module debian/rules override_dh_auto_test make[1]: Entering directory '/build/reproducible-path/python-mne-1.8.0' mkdir -p build xvfb-run \ --auto-servernum \ --server-num=20 \ -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" \ py.test -s -v mne Using default location ~/mne_data for testing... Creating /build/reproducible-path/python-mne-1.8.0/build/mne_data Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path ============================= test session starts ============================== platform linux -- Python 3.12.6, pytest-8.3.3, pluggy-1.5.0 -- /usr/bin/python3 cachedir: .pytest_cache PyQt6 6.7.1 -- Qt runtime 6.6.2 -- Qt compiled 6.6.2 MNE 0.0.0 -- /build/reproducible-path/python-mne-1.8.0/mne rootdir: /build/reproducible-path/python-mne-1.8.0 configfile: pyproject.toml plugins: cov-5.0.0, typeguard-4.3.0, timeout-2.3.1, qt-4.3.1 collecting ... Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Running subprocess: mri_watershed --version Command not found: mri_watershed Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for misc... Dataset misc version 0.0 out of date, latest version is 0.27 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for misc... Dataset misc version 0.0 out of date, latest version is 0.27 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for bst_raw... Using default location ~/mne_data for spm... Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for spm... Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for bst_raw... Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Running subprocess: mris_sphere --version Command not found: mris_sphere Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path collected 4441 items / 14 skipped mne/_fiff/meas_info.py::mne._fiff.meas_info.ContainsMixin.__contains__ SKIPPED mne/_fiff/pick.py::mne._fiff.pick.pick_channels_regexp PASSED mne/_fiff/tests/test_compensator.py::test_compensation_identity Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 PASSED mne/_fiff/tests/test_compensator.py::test_compensation_apply[False-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 3 -> 2 Applying compensator to loaded data Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_False_0/ctf-raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_False_0/ctf-raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_False_0/ctf-raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 2 Compensator constructed to change 2 -> 3 PASSED mne/_fiff/tests/test_compensator.py::test_compensation_apply[False-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Compensator constructed to change 3 -> 2 Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_False_1/ctf-raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_False_1/ctf-raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_False_1/ctf-raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 2 Compensator constructed to change 2 -> 3 PASSED mne/_fiff/tests/test_compensator.py::test_compensation_apply[True-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 3 -> 2 Applying compensator to loaded data Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_True_T0/ctf-raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_True_T0/ctf-raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_True_T0/ctf-raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 2 Compensator constructed to change 2 -> 3 PASSED mne/_fiff/tests/test_compensator.py::test_compensation_apply[True-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Compensator constructed to change 3 -> 2 Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_True_F0/ctf-raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_True_F0/ctf-raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_apply_True_F0/ctf-raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 2 Compensator constructed to change 2 -> 3 PASSED mne/_fiff/tests/test_compensator.py::test_compensation_mne SKIPPED (...) mne/_fiff/tests/test_constants.py::test_constants SKIPPED (MNE_SKIP_...) mne/_fiff/tests/test_constants.py::test_dict_completion[dict_0-FIFFV_POINT_-extras0] PASSED mne/_fiff/tests/test_constants.py::test_dict_completion[dict_1-^FIFFV_.*_CH$-extras1] PASSED mne/_fiff/tests/test_constants.py::test_dict_completion[dict_2-FIFFV_COORD_-extras2] PASSED mne/_fiff/tests/test_constants.py::test_dict_completion[dict_3-FIFF_UNIT_-extras3] PASSED mne/_fiff/tests/test_constants.py::test_dict_completion[dict_4-FIFF_UNITM_-extras4] PASSED mne/_fiff/tests/test_constants.py::test_dict_completion[dict_5-FIFFV_COIL_-extras5] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs0-want0] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs1-want1] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs2-want2] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs3-want3] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs4-want4] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs5-want5] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs6-want6] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs7-want7] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs8-want8] PASSED mne/_fiff/tests/test_meas_info.py::test_create_info_grad[kwargs9-want9] PASSED mne/_fiff/tests/test_meas_info.py::test_get_valid_units PASSED mne/_fiff/tests/test_meas_info.py::test_coil_trans PASSED mne/_fiff/tests/test_meas_info.py::test_make_info PASSED mne/_fiff/tests/test_meas_info.py::test_duplicate_name_correction PASSED mne/_fiff/tests/test_meas_info.py::test_fiducials_io Overwriting existing file. PASSED mne/_fiff/tests/test_meas_info.py::test_info Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/_fiff/tests/test_meas_info.py::test_read_write_info Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/_fiff/tests/test_meas_info.py::test_dir_warning SKIPPED (Require...) mne/_fiff/tests/test_meas_info.py::test_io_dig_points PASSED mne/_fiff/tests/test_meas_info.py::test_io_coord_frame Isotrak not found Isotrak not found Isotrak not found Isotrak not found Isotrak not found Isotrak not found PASSED mne/_fiff/tests/test_meas_info.py::test_make_dig_points PASSED mne/_fiff/tests/test_meas_info.py::test_redundant PASSED mne/_fiff/tests/test_meas_info.py::test_merge_info Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/_fiff/tests/test_meas_info.py::test_check_consistency PASSED mne/_fiff/tests/test_meas_info.py::test_meas_date_convert[stamp0-dt0] PASSED mne/_fiff/tests/test_meas_info.py::test_meas_date_convert[stamp1-dt1] PASSED mne/_fiff/tests/test_meas_info.py::test_meas_date_convert[stamp2-dt2] PASSED mne/_fiff/tests/test_meas_info.py::test_anonymize Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not fully anonymizing info - keeping his_id, sex, and hand info Not fully anonymizing info - keeping his_id, sex, and hand info Not fully anonymizing info - keeping his_id, sex, and hand info Writing /tmp/pytest-of-pbuilder1/pytest-0/test_anonymize0/tmp_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_anonymize0/tmp_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_anonymize0/tmp_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Loading data for 1 events and 61 original time points ... 0 bad epochs dropped Loading data for 1 events and 61 original time points ... Loading data for 1 events and 61 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_anonymize0/tmp_epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = 0.00 ... 99.90 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/_fiff/tests/test_meas_info.py::test_anonymize_with_io Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_anonymize_with_io0/tmp_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_anonymize_with_io0/tmp_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_anonymize_with_io0/tmp_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_meas_info.py::test_csr_csc SKIPPED (Requires te...) mne/_fiff/tests/test_meas_info.py::test_check_compensation_consistency SKIPPED mne/_fiff/tests/test_meas_info.py::test_field_round_trip Isotrak not found PASSED mne/_fiff/tests/test_meas_info.py::test_equalize_channels Identifying common channels ... Dropped the following channels: ['CH3', 'CH4'] PASSED mne/_fiff/tests/test_meas_info.py::test_repr PASSED mne/_fiff/tests/test_meas_info.py::test_repr_html Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/_fiff/tests/test_meas_info.py::test_invalid_subject_birthday SKIPPED mne/_fiff/tests/test_meas_info.py::test_channel_name_limit[fname0] SKIPPED mne/_fiff/tests/test_meas_info.py::test_channel_name_limit[fname1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 1 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 1) 1 projection items activated Loading data for 2 events and 421 original time points ... 1 bad epochs dropped Loading data for 1 events and 421 original time points ... Writing channel names to FIF truncated to 15 characters with remapping Loading data for 1 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_channel_name_limit_fname10/test-epo.fif ... Reading extended channel information Read a total of 1 projection items: test (1 x 2) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) 1 projection items activated 3 x 3 full covariance (kind = 1) found. Read a total of 1 projection items: test (1 x 2) active Writing channel names to FIF truncated to 15 characters with remapping Reading /tmp/pytest-of-pbuilder1/pytest-0/test_channel_name_limit_fname10/test-ave.fif ... Reading extended channel information Read a total of 1 projection items: test (1 x 2) active Found the data of interest: t = -199.80 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [-0.199795, 0] s) Fitted sphere radius: 91.0 mm Origin head coordinates: -4.1 16.0 51.7 mm Origin device coordinates: 1.4 17.8 -10.3 mm Equiv. model fitting -> RV = 0.00497445 %% mu1 = 0.943836 lambda1 = 0.139689 mu2 = 0.662383 lambda2 = 0.698159 mu3 = 0.308906 lambda3 = -0.0274779 Set up EEG sphere model with scalp radius 91.0 mm Sphere : origin at (0.0 0.0 0.0) mm radius : 95.0 mm Source location file : dict() Assuming input in millimeters Assuming input in MRI coordinates Positions (in meters) and orientations 1 sources Source space : ] MRI (surface RAS) coords, ~1 kB> MRI -> head transform : identity Measurement data : instance of Info Sphere model : origin at [-0.00413909 0.01597995 0.05174252] mm Standard field computations Do computations in head coordinates Free source orientations Read 1 source spaces a total of 1 active source locations Coordinate transformation: MRI (surface RAS) -> head 1.000000 0.000000 0.000000 0.00 mm 0.000000 1.000000 0.000000 0.00 mm 0.000000 0.000000 1.000000 0.00 mm 0.000000 0.000000 0.000000 1.00 Read 3 MEG channels from info 105 coil definitions read Coordinate transformation: MEG device -> head 0.991420 -0.039936 -0.124467 -6.13 mm 0.060661 0.984012 0.167456 0.06 mm 0.115790 -0.173570 0.977991 64.74 mm 0.000000 0.000000 0.000000 1.00 MEG coil definitions created in head coordinates. Source spaces are now in head coordinates. Using the sphere model. Computing MEG at 1 source location (free orientations)... Finished. Writing channel names to FIF truncated to 15 characters with remapping Write a source space... [done] 1 source spaces written Reading forward solution from /tmp/pytest-of-pbuilder1/pytest-0/test_channel_name_limit_fname10/temp-fwd.fif... Reading a source space... [done] 1 source spaces read Desired named matrix (kind = 3523 (FIFF_MNE_FORWARD_SOLUTION_GRAD)) not available Read MEG forward solution (1 sources, 3 channels, free orientations) Reading extended channel information Source spaces transformed to the forward solution coordinate frame info["bads"] and noise_cov["bads"] do not match, excluding bad channels from both Computing inverse operator with 2 channels. 2 out of 3 channels remain after picking Selected 2 channels Creating the depth weighting matrix... 2 planar channels limit = 2/1 = 1.000000 scale = 2.57437e-10 exp = 0.8 Whitening the forward solution. Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 9.8e-17 (2.2e-16 eps * 2 dim * 0.22 max singular value) Estimated rank (grad): 1 GRAD: rank 1 computed from 2 data channels with 1 projector Setting small GRAD eigenvalues to zero (without PCA) Creating the source covariance matrix Adjusting source covariance matrix. Computing SVD of whitened and weighted lead field matrix. largest singular value = 1 scaling factor to adjust the trace = 1.14159e+13 (nchan = 2 nzero = 1) Write inverse operator decomposition in /tmp/pytest-of-pbuilder1/pytest-0/test_channel_name_limit_fname10/test-inv.fif... Writing channel names to FIF truncated to 15 characters with remapping Write a source space... [done] 1 source spaces written Writing inverse operator info... Writing noise covariance matrix. Writing source covariance matrix. Writing orientation priors. [done] Reading inverse operator decomposition from /tmp/pytest-of-pbuilder1/pytest-0/test_channel_name_limit_fname10/test-inv.fif... Reading inverse operator info... [done] Reading inverse operator decomposition... [done] 2 x 2 full covariance (kind = 1) found. Read a total of 1 projection items: test (1 x 2) active Noise covariance matrix read. 3 x 3 diagonal covariance (kind = 2) found. Source covariance matrix read. 3 x 3 diagonal covariance (kind = 6) found. Orientation priors read. 3 x 3 diagonal covariance (kind = 5) found. Depth priors read. Did not find the desired covariance matrix (kind = 3) Reading a source space... [done] 1 source spaces read Reading extended channel information Read a total of 1 projection items: test (1 x 2) active Source spaces transformed to the inverse solution coordinate frame Preparing the inverse operator for use... Scaled noise and source covariance from nave = 1 to nave = 1 Created the regularized inverter Created an SSP operator (subspace dimension = 1) Created the whitener using a noise covariance matrix with rank 1 (1 small eigenvalues omitted) Computing noise-normalization factors (dSPM)... [done] Applying inverse operator to "1"... Picked 2 channels from the data Computing inverse... Eigenleads need to be weighted ... Computing residual... Explained 99.0% variance Combining the current components... dSPM... [done] PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[True-fname_info0-highest] Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[True-fname_info0-default] Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[True-create_info-highest] PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[True-create_info-default] PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[False-fname_info0-highest] Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[False-fname_info0-default] Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[False-create_info-highest] PASSED mne/_fiff/tests/test_meas_info.py::test_pickle[False-create_info-default] PASSED mne/_fiff/tests/test_meas_info.py::test_info_bad PASSED mne/_fiff/tests/test_meas_info.py::test_get_montage Creating RawArray with float64 data, n_channels=94, n_times=1024 Range : 0 ... 1023 = 0.000 ... 1.998 secs Ready. Creating RawArray with float64 data, n_channels=94, n_times=1024 Range : 0 ... 1023 = 0.000 ... 1.998 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_pick_refs Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Compensator constructed to change 3 -> 2 PASSED mne/_fiff/tests/test_pick.py::test_pick_channels_regexp PASSED mne/_fiff/tests/test_pick.py::test_pick_seeg_ecog Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 0 ... 9 = 0.000 ... 0.009 secs Ready. Not setting metadata 2 matching events found Applying baseline correction (mode: mean) 0 projection items activated Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_pick_dbs Creating RawArray with float64 data, n_channels=7, n_times=7 Range : 0 ... 6 = 0.000 ... 0.006 secs Ready. Not setting metadata 2 matching events found Applying baseline correction (mode: mean) 0 projection items activated Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_pick_chpi PASSED mne/_fiff/tests/test_pick.py::test_pick_csd PASSED mne/_fiff/tests/test_pick.py::test_pick_bio PASSED mne/_fiff/tests/test_pick.py::test_pick_fnirs PASSED mne/_fiff/tests/test_pick.py::test_pick_ref Read 5 compensation matrices PASSED mne/_fiff/tests/test_pick.py::test_pick_forward_seeg_ecog SKIPPED (R...) mne/_fiff/tests/test_pick.py::test_picks_by_channels Creating RawArray with float64 data, n_channels=4, n_times=2000 Range : 0 ... 1999 = 0.000 ... 7.996 secs Ready. Creating RawArray with float64 data, n_channels=4, n_times=2000 Range : 0 ... 1999 = 0.000 ... 7.996 secs Ready. NOTE: pick_types() is a legacy function. New code should use inst.pick(...). NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). PASSED mne/_fiff/tests/test_pick.py::test_clean_info_bads Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated PASSED mne/_fiff/tests/test_pick.py::test_picks_to_idx SKIPPED (Requires te...) mne/_fiff/tests/test_pick.py::test_pick_channels_cov PASSED mne/_fiff/tests/test_pick.py::test_pick_types_meg PASSED mne/_fiff/tests/test_pick.py::test_pick_types_csd Creating RawArray with float64 data, n_channels=8, n_times=512 Range : 0 ... 511 = 0.000 ... 1.996 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-True-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-True-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-True-grad] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-True-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-False-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-False-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-False-grad] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[True-False-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-True-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-True-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-True-grad] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-True-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-False-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-False-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-False-grad] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_pick.py::test_get_channel_types_equiv[False-False-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/_fiff/tests/test_proc_history.py::test_maxfilter_io PASSED mne/_fiff/tests/test_reference.py::test_apply_reference SKIPPED (Req...) mne/_fiff/tests/test_reference.py::test_set_eeg_reference SKIPPED (R...) mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[False-auto-msg0] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Applying average reference. EEG data marked as already having the desired reference. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[False-ecog-msg1] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Applying average reference. EEG data marked as already having the desired reference. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[False-dbs-msg2] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Applying average reference. EEG data marked as already having the desired reference. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[False-ch_type3-msg3] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Applying average reference. EEG data marked as already having the desired reference. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[True-auto-msg0] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Adding average EEG reference projection. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[True-ecog-msg1] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Adding average EEG reference projection. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[True-dbs-msg2] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Adding average EEG reference projection. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_ch_type[True-ch_type3-msg3] Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Adding average EEG reference projection. Creating RawArray with float64 data, n_channels=5, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/_fiff/tests/test_reference.py::test_set_eeg_reference_rest SKIPPED mne/_fiff/tests/test_reference.py::test_set_bipolar_reference[raw] SKIPPED mne/_fiff/tests/test_reference.py::test_set_bipolar_reference[epochs] SKIPPED mne/_fiff/tests/test_reference.py::test_set_bipolar_reference[evoked] SKIPPED mne/_fiff/tests/test_reference.py::test_add_reference SKIPPED (Requi...) mne/_fiff/tests/test_reference.py::test_add_reorder[1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 60 = 0.000 ... 0.100 secs... PASSED mne/_fiff/tests/test_reference.py::test_add_reorder[2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 60 = 0.000 ... 0.100 secs... PASSED mne/_fiff/tests/test_reference.py::test_bipolar_combinations Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Added the following bipolar channels: CH2-CH1 EEG channel type selected for re-referencing Creating RawArray with float64 data, n_channels=45, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Added the following bipolar channels: CH1-CH2, CH1-CH3, CH1-CH4, CH1-CH5, CH1-CH6, CH1-CH7, CH1-CH8, CH1-CH9, CH1-CH10, CH2-CH3, CH2-CH4, CH2-CH5, CH2-CH6, CH2-CH7, CH2-CH8, CH2-CH9, CH2-CH10, CH3-CH4, CH3-CH5, CH3-CH6, CH3-CH7, CH3-CH8, CH3-CH9, CH3-CH10, CH4-CH5, CH4-CH6, CH4-CH7, CH4-CH8, CH4-CH9, CH4-CH10, CH5-CH6, CH5-CH7, CH5-CH8, CH5-CH9, CH5-CH10, CH6-CH7, CH6-CH8, CH6-CH9, CH6-CH10, CH7-CH8, CH7-CH9, CH7-CH10, CH8-CH9, CH8-CH10, CH9-CH10 EEG channel type selected for re-referencing Creating RawArray with float64 data, n_channels=45, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Added the following bipolar channels: CH1-CH2, CH1-CH3, CH1-CH4, CH1-CH5, CH1-CH6, CH1-CH7, CH1-CH8, CH1-CH9, CH1-CH10, CH2-CH3, CH2-CH4, CH2-CH5, CH2-CH6, CH2-CH7, CH2-CH8, CH2-CH9, CH2-CH10, CH3-CH4, CH3-CH5, CH3-CH6, CH3-CH7, CH3-CH8, CH3-CH9, CH3-CH10, CH4-CH5, CH4-CH6, CH4-CH7, CH4-CH8, CH4-CH9, CH4-CH10, CH5-CH6, CH5-CH7, CH5-CH8, CH5-CH9, CH5-CH10, CH6-CH7, CH6-CH8, CH6-CH9, CH6-CH10, CH7-CH8, CH7-CH9, CH7-CH10, CH8-CH9, CH8-CH10, CH9-CH10 EEG channel type selected for re-referencing Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Added the following bipolar channels: CH2-CH1, CH1-CH2 EEG channel type selected for re-referencing Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Added the following bipolar channels: bad_bipolar EEG channel type selected for re-referencing Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Added the following bipolar channels: bad_bipolar PASSED mne/_fiff/tests/test_show_fiff.py::test_show_fiff PASSED mne/_fiff/tests/test_utils.py::test_check_orig_units PASSED mne/_fiff/tests/test_what.py::test_what SKIPPED (Requires testing da...) mne/_fiff/tests/test_write.py::test_write_int PASSED mne/_fiff/utils.py::mne._fiff.utils._blk_read_lims PASSED mne/annotations.py::mne.annotations.Annotations SKIPPED (all tests s...) mne/annotations.py::mne.annotations.count_annotations PASSED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-False-0] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-False-100] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-False-200] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-False-233] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-True-0] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-True-100] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-True-200] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics[testing_data-True-233] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-single-None-0] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-single-None-100] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-single-None-200] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-single-None-233] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-matrix-unit-noise-gain-0] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-matrix-unit-noise-gain-100] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-matrix-unit-noise-gain-200] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_csd[testing_data-matrix-unit-noise-gain-233] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-single-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-single-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-single-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-single-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-matrix-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-matrix-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-matrix-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-0-matrix-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-single-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-single-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-single-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-single-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-matrix-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-matrix-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-matrix-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-100-matrix-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-single-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-single-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-single-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-single-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-matrix-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-matrix-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-matrix-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-200-matrix-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-single-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-single-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-single-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-single-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-matrix-None] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-matrix-normal] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-matrix-max-power] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_ori_inv[testing_data-233-matrix-vector] SKIPPED mne/beamformer/tests/test_dics.py::test_real[testing_data-0] SKIPPED mne/beamformer/tests/test_dics.py::test_real[testing_data-100] SKIPPED mne/beamformer/tests/test_dics.py::test_real[testing_data-200] SKIPPED mne/beamformer/tests/test_dics.py::test_real[testing_data-233] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_timeseries[testing_data-0] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_timeseries[testing_data-100] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_timeseries[testing_data-200] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_timeseries[testing_data-233] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_tfr[True] SKIPPED mne/beamformer/tests/test_dics.py::test_apply_dics_tfr[False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-unit-noise-gain-invariant-False-None-26-28-True] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-unit-noise-gain-False-None-13-15-True] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-nai-False-None-13-15-True] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-unit-noise-gain-invariant-False-None-26-28-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-unit-noise-gain-invariant-True-None-40-42-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-unit-noise-gain-invariant-True-None-40-42-True] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-unit-noise-gain-False-None-13-14-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-unit-noise-gain-True-None-35-37-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-nai-True-None-35-37-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-None-True-None-12-14-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-None-True-0.8-39-43-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-unit-noise-gain-invariant-False-None-17-20-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-unit-noise-gain-False-None-17-20-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-unit-noise-gain-False-None-17-20-True] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-nai-True-None-21-24-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-None-True-None-7-10-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-None-True-0.8-15-18-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-None-None-True-None-21-32-False] SKIPPED mne/beamformer/tests/test_dics.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-max-power-None-True-None-13-19-False] SKIPPED mne/beamformer/tests/test_dics.py::test_orientation_max_power[testing_data-testing_data-testing_data-unit-noise-gain-invariant-57-58-0.6-0.61-False] SKIPPED mne/beamformer/tests/test_dics.py::test_orientation_max_power[testing_data-testing_data-testing_data-unit-noise-gain-57-58-0.6-0.61-False] SKIPPED mne/beamformer/tests/test_dics.py::test_orientation_max_power[testing_data-testing_data-testing_data-unit-noise-gain-57-58-0.6-0.61-True] SKIPPED mne/beamformer/tests/test_dics.py::test_orientation_max_power[testing_data-testing_data-testing_data-None-27-28-0.56-0.57-False] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.05-unit-noise-gain-True-None-97-98] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.05-nai-True-None-96-98] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.05-nai-True-0.8-96-98] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.05-None-True-None-74-76] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.05-None-True-0.8-90-93] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.05-unit-noise-gain-False-None-83-86] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.05-unit-noise-gain-False-0.8-83-86] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-0.0-unit-noise-gain-True-None-35-99] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-unit-noise-gain-invariant-False-None-26-28-0.82-0.84] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-unit-noise-gain-invariant-True-None-40-42-0.96-0.98] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-unit-noise-gain-False-None-13-14-0.79-0.81] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-unit-noise-gain-True-None-35-37-0.98-0.99] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-nai-True-None-35-37-0.98-0.99] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-None-True-None-12-14-0.97-0.98] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-vector-None-True-0.8-39-43-0.97-0.98] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-unit-noise-gain-invariant-False-None-17-20-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-unit-noise-gain-False-None-17-20-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-nai-True-None-21-24-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-None-True-None-7-10-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-max-power-None-True-0.8-15-18-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.05-None-None-True-0.8-40-42-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-vector-None-True-None-23-24-0.96-0.97] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-vector-unit-noise-gain-invariant-True-None-52-54-0.95-0.96] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-vector-unit-noise-gain-True-None-44-48-0.97-0.99] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-vector-nai-True-None-44-48-0.97-0.99] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-max-power-None-True-None-14-15-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-max-power-unit-noise-gain-invariant-True-None-35-37-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-max-power-unit-noise-gain-True-None-35-37-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_localization_bias_free[testing_data-testing_data-testing_data-0.0-max-power-nai-True-None-35-37-0-0] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.05-unit-noise-gain-invariant-False-None-38-40-0.54-0.55] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.05-unit-noise-gain-False-None-38-40-0.54-0.55] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.05-nai-True-None-56-57-0.59-0.61] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.05-None-True-None-27-28-0.56-0.57] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.05-None-True-0.8-42-43-0.56-0.57] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.0-None-True-None-50-51-0.58-0.59] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.0-unit-noise-gain-invariant-True-None-73-75-0.59-0.61] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.0-unit-noise-gain-True-None-73-75-0.59-0.61] SKIPPED mne/beamformer/tests/test_lcmv.py::test_orientation_max_power[testing_data-testing_data-testing_data-0.0-nai-True-None-73-75-0.59-0.61] SKIPPED mne/beamformer/tests/test_lcmv.py::test_depth_does_not_matter[testing_data-testing_data-testing_data-nai-max-power] SKIPPED mne/beamformer/tests/test_lcmv.py::test_depth_does_not_matter[testing_data-testing_data-testing_data-unit-noise-gain-vector] SKIPPED mne/beamformer/tests/test_lcmv.py::test_depth_does_not_matter[testing_data-testing_data-testing_data-unit-noise-gain-max-power] SKIPPED mne/beamformer/tests/test_lcmv.py::test_depth_does_not_matter[testing_data-testing_data-testing_data-unit-noise-gain-None] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_warn_inverse_operator[testing_data-testing_data-testing_data] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_make_inverse_operator_loose[testing_data-testing_data] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_inverse_operator_channel_ordering[testing_data-testing_data-testing_data] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-MNE-54-57-depth0] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-MNE-75-80-depth1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-MNE-83-87-0.8] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-MNE-89-92-depth3] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-dSPM-96-98-0.8] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-sLORETA-100-100-0.8] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-eLORETA-100-100-None] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_fixed[testing_data-testing_data-testing_data-eLORETA-100-100-0.8] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-MNE-32-37-depth0-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-MNE-78-81-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-MNE-89-92-depth2-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-dSPM-85-87-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-sLORETA-100-100-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-eLORETA-99-100-None-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-eLORETA-99-100-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-None-eLORETA-99-100-0.8-0.001] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-MNE-32-37-depth0-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-MNE-78-81-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-MNE-89-92-depth2-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-dSPM-85-87-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-sLORETA-100-100-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-eLORETA-99-100-None-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-eLORETA-99-100-0.8-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_loose[testing_data-testing_data-testing_data-vector-eLORETA-99-100-0.8-0.001] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-MNE-21-24-0.73-0.75-kwargs0-depth0-1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-MNE-35-40-0.93-0.94-kwargs1-depth1-1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-MNE-45-55-0.94-0.95-kwargs2-0.8-1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-MNE-65-70-0.945-0.955-kwargs3-depth3-1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-dSPM-40-45-0.96-0.97-kwargs4-0.8-1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-sLORETA-93-95-0.95-0.96-kwargs5-0.8-1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-eLORETA-93-100-0.95-0.96-kwargs6-None-1] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-eLORETA-100-100-0.98-0.99-kwargs7-None-1.0] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-eLORETA-100-100-0.98-0.99-kwargs8-0.8-1.0] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_localization_bias_free[testing_data-testing_data-testing_data-eLORETA-100-100-0.98-0.99-kwargs9-0.8-0.999] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_sphere[testing_data-testing_data] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_eLORETA_MNE_equiv[testing_data-testing_data-testing_data-0.1111111111111111-0.0] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_eLORETA_MNE_equiv[testing_data-testing_data-testing_data-0.1111111111111111-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_eLORETA_MNE_equiv[testing_data-testing_data-testing_data-0.1111111111111111-1.0] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_eLORETA_MNE_equiv[testing_data-testing_data-testing_data-0.0-0.0] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_eLORETA_MNE_equiv[testing_data-testing_data-testing_data-0.0-0.2] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_eLORETA_MNE_equiv[testing_data-testing_data-testing_data-0.0-1.0] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_operator[testing_data-testing_data-inv0-0-1.3e-08] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_operator[testing_data-testing_data-inv1--2.5e-08-2.5e-08] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses0-0.87-0.94-0.9-1.1-MNE] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses0-0.87-0.94-0.9-1.1-dSPM] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses0-0.87-0.94-0.9-1.1-sLORETA] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses0-0.87-0.94-0.9-1.1-eLORETA] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses1-0.3-0.6-2-4-MNE] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses1-0.3-0.6-2-4-dSPM] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses1-0.3-0.6-2-4-sLORETA] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_orientation_prior[testing_data-testing_data-testing_data-looses1-0.3-0.6-2-4-eLORETA] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_inverse_residual[testing_data-testing_data-None-MNE] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_inverse_residual[testing_data-testing_data-None-dSPM] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_inverse_residual[testing_data-testing_data-None-sLORETA] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_inverse_residual[testing_data-testing_data-None-eLORETA] SKIPPED 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mne/viz/tests/test_topomap.py::test_plot_evoked_topomap_extrapolation[testing_data-testing_data-local] SKIPPED mne/viz/tests/test_topomap.py::test_plot_evoked_topomap_extrapolation[testing_data-testing_data-head] SKIPPED mne/viz/tests/test_topomap.py::test_plot_arrowmap[testing_data-testing_data] SKIPPED mne/time_frequency/tests/test_tfr.py::test_average_tfr_init[testing_data] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-raw_tfr-mag-dict,mean] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-raw_tfr-mag-list,rms] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-raw_tfr-mag-none,lambda] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-raw_tfr-grad-dict,mean] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-raw_tfr-grad-list,rms] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-raw_tfr-grad-none,lambda] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-epochs_tfr-mag-dict,mean] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-epochs_tfr-mag-list,rms] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-epochs_tfr-mag-none,lambda] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-average_tfr-mag-dict,mean] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-average_tfr-mag-list,rms] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-average_tfr-mag-none,lambda] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-average_tfr-grad-dict,mean] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-average_tfr-grad-list,rms] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint[testing_data-average_tfr-grad-none,lambda] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint_errors[testing_data-Requested time point \\(-88.000 s\\) exceeds the range of-timefreqs0-None] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint_errors[testing_data-Requested frequency \\(99.0 Hz\\) exceeds the range of-timefreqs1-None] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint_errors[testing_data-list of tuple pairs, or a dict of such tuple pairs, not 0-timefreqs2-None] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint_errors[testing_data-does not match the channel type present in-None-topomap_args3] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_joint_doesnt_modify[testing_data] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs0-power-morlet] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs0-power-multitaper] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs0-phase-morlet] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs0-phase-multitaper] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs0-complex-morlet] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs0-complex-multitaper] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs1-power-morlet] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs1-power-multitaper] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs1-phase-morlet] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs1-phase-multitaper] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs1-complex-morlet] SKIPPED mne/time_frequency/tests/test_tfr.py::test_evoked_compute_tfr[testing_data-freqs1-complex-multitaper] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topomap[testing_data-raw_tfr-mag] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topomap[testing_data-raw_tfr-grad] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topomap[testing_data-epochs_tfr-mag] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topomap[testing_data-average_tfr-mag] SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topomap[testing_data-average_tfr-grad] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-False-0] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-False-100] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-False-200] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-False-233] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-True-0] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-True-100] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-True-200] SKIPPED mne/beamformer/tests/test_dics.py::test_make_dics_rank[testing_data-True-233] SKIPPED mne/beamformer/tests/test_external.py::test_lcmv_fieldtrip[testing_data-ug_scal-None-max-power-False] SKIPPED mne/beamformer/tests/test_external.py::test_lcmv_fieldtrip[testing_data-ung-unit-noise-gain-max-power-False] SKIPPED mne/beamformer/tests/test_external.py::test_lcmv_fieldtrip[testing_data-ung_pow-unit-noise-gain-max-power-True] SKIPPED mne/beamformer/tests/test_external.py::test_lcmv_fieldtrip[testing_data-ug_vec-None-vector-False] SKIPPED mne/beamformer/tests/test_external.py::test_lcmv_fieldtrip[testing_data-ung_vec-unit-noise-gain-vector-False] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_vector SKIPPED (Require...) mne/beamformer/tests/test_lcmv.py::test_make_lcmv_bem[0.01-True-volume] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_bem[0.0-False-volume] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_bem[0.01-False-surface] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_bem[0.0-True-surface] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_sphere[unit-noise-gain-max-power] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_sphere[unit-noise-gain-vector] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_sphere[unit-noise-gain-None] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_sphere[nai-vector] SKIPPED mne/beamformer/tests/test_lcmv.py::test_make_lcmv_sphere[None-max-power] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_cov[max-power-None] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_cov[max-power-unit-noise-gain] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_cov[normal-None] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_cov[normal-unit-noise-gain] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_ctf_comp SKIPPED (Requi...) mne/beamformer/tests/test_lcmv.py::test_lcmv_reg_proj[True-unit-noise-gain] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_reg_proj[False-unit-noise-gain] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_reg_proj[True-None] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_reg_proj[True-nai] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_maxfiltered[info] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_maxfiltered[computed] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_maxfiltered[full] SKIPPED mne/beamformer/tests/test_lcmv.py::test_lcmv_maxfiltered[None] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[vector-unit-noise-gain-invariant-0.05-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[vector-unit-noise-gain-invariant-0.05-single] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[vector-unit-noise-gain-0.05-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[vector-unit-noise-gain-0.05-single] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[vector-unit-noise-gain-0.0-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[vector-unit-noise-gain-0.0-single] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[vector-nai-0.05-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[max-power-unit-noise-gain-0.05-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[max-power-unit-noise-gain-0.0-single] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[max-power-unit-noise-gain-0.05-single] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[max-power-unit-noise-gain-invariant-0.05-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[normal-unit-noise-gain-0.05-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_unit_noise_gain_formula[normal-nai-0.0-matrix] SKIPPED mne/beamformer/tests/test_lcmv.py::test_api PASSED mne/beamformer/tests/test_rap_music.py::test_rap_music_simulated SKIPPED mne/beamformer/tests/test_rap_music.py::test_rap_music_sphere SKIPPED mne/beamformer/tests/test_rap_music.py::test_rap_music_picks SKIPPED mne/beamformer/tests/test_rap_music.py::test_trap_music SKIPPED (Req...) mne/beamformer/tests/test_resolution_matrix.py::test_resolution_matrix_lcmv SKIPPED mne/channels/tests/test_channels.py::test_reorder_channels[True-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 60 = 0.000 ... 0.100 secs... PASSED mne/channels/tests/test_channels.py::test_reorder_channels[True-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_channels.py::test_reorder_channels[False-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 60 = 0.000 ... 0.100 secs... PASSED mne/channels/tests/test_channels.py::test_reorder_channels[False-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_channels.py::test_rename_channels Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_channels.py::test_set_channel_types Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated PASSED mne/channels/tests/test_channels.py::test_get_builtin_ch_adjacencies PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-biosemi16] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-biosemi32] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-biosemi64] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-bti148] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-bti248] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-bti248grad] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-ctf151] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-ctf275] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-ctf64] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-easycap128ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-easycap32ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-easycap64ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-easycapM1] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-easycapM11] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-easycapM14] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-easycapM15] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-ecog256] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-ecog256bipolar] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-eeg1010_neighb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-elec1005] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-elec1010] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-elec1020] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-itab153] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-itab28] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-KIT-157] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-KIT-208] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-KIT-NYU-2019] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-KIT-UMD-1] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-KIT-UMD-2] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-KIT-UMD-3] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-KIT-UMD-4] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-language29ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-mpi_59_channels] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-neuromag122cmb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-neuromag306cmb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-neuromag306mag] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-neuromag306planar] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-yokogawa160] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-slice-yokogawa440] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-biosemi16] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-biosemi32] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-biosemi64] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-bti148] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-bti248] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-bti248grad] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-ctf151] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-ctf275] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-ctf64] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-easycap128ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-easycap32ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-easycap64ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-easycapM1] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-easycapM11] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-easycapM14] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-easycapM15] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-ecog256] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-ecog256bipolar] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-eeg1010_neighb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-elec1005] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-elec1010] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-elec1020] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-itab153] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-itab28] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-KIT-157] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-KIT-208] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-KIT-NYU-2019] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-KIT-UMD-1] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-KIT-UMD-2] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-KIT-UMD-3] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-KIT-UMD-4] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-language29ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-mpi_59_channels] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-neuromag122cmb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-neuromag306cmb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-neuromag306mag] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-neuromag306planar] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-yokogawa160] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-arange-yokogawa440] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-biosemi16] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-biosemi32] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-biosemi64] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-bti148] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-bti248] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-bti248grad] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-ctf151] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-ctf275] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-ctf64] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-easycap128ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-easycap32ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-easycap64ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-easycapM1] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-easycapM11] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-easycapM14] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-easycapM15] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-ecog256] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-ecog256bipolar] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-eeg1010_neighb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-elec1005] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-elec1010] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-elec1020] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-itab153] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-itab28] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-KIT-157] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-KIT-208] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-KIT-NYU-2019] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-KIT-UMD-1] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-KIT-UMD-2] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-KIT-UMD-3] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-KIT-UMD-4] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-language29ch-avg] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-mpi_59_channels] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-neuromag122cmb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-neuromag306cmb] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-neuromag306mag] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-neuromag306planar] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-yokogawa160] PASSED mne/channels/tests/test_channels.py::test_read_builtin_ch_adjacency_picks[pick-names-yokogawa440] PASSED mne/channels/tests/test_channels.py::test_read_ch_adjacency PASSED mne/channels/tests/test_channels.py::test_adjacency_matches_ft SKIPPED mne/channels/tests/test_channels.py::test_get_set_sensor_positions Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_channels.py::test_1020_selection SKIPPED (Re...) mne/channels/tests/test_channels.py::test_find_ch_adjacency SKIPPED mne/channels/tests/test_channels.py::test_neuromag122_adjacency SKIPPED mne/channels/tests/test_channels.py::test_drop_channels Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_channels.py::test_pick_channels Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_channels.py::test_add_reference_channels Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 31 events and 421 original time points ... 1 bad epochs dropped PASSED mne/channels/tests/test_channels.py::test_equalize_channels Creating RawArray with float64 data, n_channels=4, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready. Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Identifying common channels ... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Dropped the following channels: ['CH3', 'CH5', 'CH4', 'CH8'] Identifying common channels ... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Dropped the following channels: ['CH3', 'CH5', 'CH4'] PASSED mne/channels/tests/test_channels.py::test_combine_channels Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Creating RawArray with float64 data, n_channels=2, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Using data from preloaded Raw for 31 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 30 matching events found Applying baseline correction (mode: mean) 0 projection items activated Applying baseline correction (mode: mean) Creating RawArray with float64 data, n_channels=2, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Dropped the following channels in group foo: [0, 1] Creating RawArray with float64 data, n_channels=2, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=11, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Creating RawArray with float64 data, n_channels=2, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Dropped the following channels in group foo: [0] Creating RawArray with float64 data, n_channels=2, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_channels.py::test_combine_channels_metadata SKIPPED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[None-3e-06-True-ctol0-0.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'spline'}. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 13 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[None-3e-06-True-ctol0-0.1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'spline'}. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 13 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[None-3e-06-False-ctol1-0.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'spline'}. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 13 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[None-3e-06-False-ctol1-0.1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'spline'}. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 13 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing interpolation matrix from 14 sensor positions Interpolating 1 sensors Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[method1-4e-06-True-ctol0-0.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'MNE'}. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 13 EEG channels... Computing cross products for 13 → 14 EEG channels... Preparing the mapping matrix... Truncating at 12/13 components and regularizing with α=1.0e-01 The map has an average electrode reference (14 channels) Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 13/14 components and regularizing with α=1.0e-01 The map has an average electrode reference (15 channels) Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 13/14 components and regularizing with α=1.0e-01 The map has an average electrode reference (15 channels) Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[method1-4e-06-True-ctol0-0.1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'MNE'}. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 13 EEG channels... Computing cross products for 13 → 14 EEG channels... Preparing the mapping matrix... Truncating at 12/13 components and regularizing with α=1.0e-01 The map has an average electrode reference (14 channels) Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 13/14 components and regularizing with α=1.0e-01 The map has an average electrode reference (15 channels) Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 13/14 components and regularizing with α=1.0e-01 The map has an average electrode reference (15 channels) Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[method1-4e-06-False-ctol1-0.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'MNE'}. Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 13 EEG channels... Computing cross products for 13 → 14 EEG channels... Preparing the mapping matrix... Truncating at 13/13 components and regularizing with α=1.0e-01 Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_eeg[method1-4e-06-False-ctol1-0.1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting channel interpolation method to {'eeg': 'MNE'}. Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 13 EEG channels... Computing cross products for 13 → 14 EEG channels... Preparing the mapping matrix... Truncating at 13/13 components and regularizing with α=1.0e-01 Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Creating RawArray with float64 data, n_channels=15, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'eeg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 14 EEG channels... Computing cross products for 14 → 15 EEG channels... Preparing the mapping matrix... Truncating at 14/14 components and regularizing with α=1.0e-01 Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 96.4 mm Computing dot products for 1 MEG channel... Computing cross products for 1 → 1 MEG channel... Preparing the mapping matrix... Truncating at 1/1 components to omit less than 0.0001 (0) PASSED mne/channels/tests/test_interpolation.py::test_interpolation_meg Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 60 = 0.000 ... 0.100 secs... Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 98 MEG channels... Computing cross products for 98 → 1 MEG channel... Preparing the mapping matrix... Truncating at 73/98 components to omit less than 0.0001 (9.5e-05) Creating RawArray with float64 data, n_channels=100, n_times=421 Range : 0 ... 420 = 0.000 ... 0.699 secs Ready. Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 99 MEG channels... Computing cross products for 99 → 1 MEG channel... Preparing the mapping matrix... Truncating at 74/99 components to omit less than 0.0001 (9.8e-05) Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 99 MEG channels... Computing cross products for 99 → 1 MEG channel... Preparing the mapping matrix... Truncating at 74/99 components to omit less than 0.0001 (9.8e-05) Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 99 MEG channels... Computing cross products for 99 → 1 MEG channel... Preparing the mapping matrix... Truncating at 60/99 components to omit less than 0.0001 (9.8e-05) Setting channel interpolation method to {'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing dot products for 98 MEG channels... Computing cross products for 98 → 1 MEG channel... Preparing the mapping matrix... Truncating at 60/98 components to omit less than 0.0001 (9.4e-05) PASSED mne/channels/tests/test_interpolation.py::test_interpolate_meg_ctf Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 2400 = 0.000 ... 1.000 secs... Compensator constructed to change 0 -> 3 Applying compensator to loaded data Removing 5 compensators from info because not all compensation channels were picked. Computing dot products for 303 MEG channels... Computing cross products for 303 → 1 MEG channel... Preparing the mapping matrix... Truncating at 92/303 components to omit less than 0.0001 (9.9e-05) Removing 5 compensators from info because not all compensation channels were picked. Removing 5 compensators from info because not all compensation channels were picked. Computing dot products for 274 MEG channels... Computing cross products for 274 → 1 MEG channel... Preparing the mapping matrix... Truncating at 92/274 components to omit less than 0.0001 (9.9e-05) Corrcoef of interpolated with original channel: {'no_refmeg': 0.8500877951708318, 'with_refmeg': 0.7714578504412622} PASSED mne/channels/tests/test_interpolation.py::test_interpolation_ctf_comp SKIPPED mne/channels/tests/test_interpolation.py::test_interpolation_nirs SKIPPED mne/channels/tests/test_interpolation.py::test_interpolation_ecog SKIPPED mne/channels/tests/test_interpolation.py::test_interpolation_seeg SKIPPED mne/channels/tests/test_interpolation.py::test_nan_interpolation Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'meg': 'nan'}. Interpolating bad channels. Setting channel interpolation method to {'meg': 'nan'}. Interpolating bad channels. PASSED mne/channels/tests/test_interpolation.py::test_method_str SKIPPED (R...) mne/channels/tests/test_layout.py::test_io_layout_lout PASSED mne/channels/tests/test_layout.py::test_io_layout_lay PASSED mne/channels/tests/test_layout.py::test_find_topomap_coords Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/channels/tests/test_layout.py::test_make_eeg_layout Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/channels/tests/test_layout.py::test_make_grid_layout PASSED mne/channels/tests/test_layout.py::test_find_layout Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Read 5 compensation matrices Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test_umd-raw.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. PASSED mne/channels/tests/test_layout.py::test_box_size PASSED mne/channels/tests/test_layout.py::test_generate_2d_layout PASSED mne/channels/tests/test_layout.py::test_layout_copy PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks0-exclude0] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks1-exclude1] PASSED mne/channels/tests/test_layout.py::test_layout_pick[None-exclude2] PASSED mne/channels/tests/test_layout.py::test_layout_pick[None-2_0] PASSED mne/channels/tests/test_layout.py::test_layout_pick[None-exclude4] PASSED mne/channels/tests/test_layout.py::test_layout_pick[None-2_1] PASSED mne/channels/tests/test_layout.py::test_layout_pick[all-2_0] PASSED mne/channels/tests/test_layout.py::test_layout_pick[all-2_1] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks8-exclude8] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks9-exclude9] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks10-exclude10] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks11-exclude11] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks12-exclude12] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks13-exclude13] PASSED mne/channels/tests/test_layout.py::test_layout_pick[None-exclude14] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks15-exclude15] PASSED mne/channels/tests/test_layout.py::test_layout_pick[None-exclude16] PASSED mne/channels/tests/test_layout.py::test_layout_pick[picks17-exclude17] PASSED mne/channels/tests/test_layout.py::test_layout_pick_more PASSED mne/channels/tests/test_layout.py::test_layout_pick_errors PASSED mne/channels/tests/test_montage.py::test_dig_montage_trans PASSED mne/channels/tests/test_montage.py::test_fiducials PASSED mne/channels/tests/test_montage.py::test_documented PASSED mne/channels/tests/test_montage.py::test_montage_readers[sfp_duplicate] PASSED mne/channels/tests/test_montage.py::test_montage_readers[sfp_headshape] PASSED mne/channels/tests/test_montage.py::test_montage_readers[EEGLAB] PASSED mne/channels/tests/test_montage.py::test_montage_readers[matlab] PASSED mne/channels/tests/test_montage.py::test_montage_readers[ASA electrode] PASSED mne/channels/tests/test_montage.py::test_montage_readers[generic theta-phi (txt)] PASSED mne/channels/tests/test_montage.py::test_montage_readers[BESA spherical model] PASSED mne/channels/tests/test_montage.py::test_montage_readers[legacy mne-c] PASSED mne/channels/tests/test_montage.py::test_montage_readers[CSV file] PASSED mne/channels/tests/test_montage.py::test_montage_readers[XYZ file] PASSED mne/channels/tests/test_montage.py::test_montage_readers[TSV file] PASSED mne/channels/tests/test_montage.py::test_montage_readers[brainvision] PASSED mne/channels/tests/test_montage.py::test_read_locs SKIPPED (Requires...) mne/channels/tests/test_montage.py::test_read_dig_dat PASSED mne/channels/tests/test_montage.py::test_read_dig_montage_using_polhemus_fastscan PASSED mne/channels/tests/test_montage.py::test_read_dig_montage_using_polhemus_fastscan_error_handling PASSED mne/channels/tests/test_montage.py::test_read_dig_polhemus_isotrak_hsp PASSED mne/channels/tests/test_montage.py::test_read_dig_polhemus_isotrak_elp PASSED mne/channels/tests/test_montage.py::test_read_dig_polhemus_isotrak_eeg PASSED mne/channels/tests/test_montage.py::test_read_dig_polhemus_isotrak_error_handling PASSED mne/channels/tests/test_montage.py::test_combining_digmontage_objects PASSED mne/channels/tests/test_montage.py::test_combining_digmontage_forbiden_behaviors PASSED mne/channels/tests/test_montage.py::test_set_dig_montage PASSED mne/channels/tests/test_montage.py::test_set_dig_montage_with_nan_positions PASSED mne/channels/tests/test_montage.py::test_fif_dig_montage SKIPPED (Re...) mne/channels/tests/test_montage.py::test_egi_dig_montage SKIPPED (Re...) mne/channels/tests/test_montage.py::test_read_dig_captrak SKIPPED (R...) mne/channels/tests/test_montage.py::test_set_montage_mgh[raw] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Normal return from subroutine COBYLA NFVALS = 125 F = 1.804228E-03 MAXCV = 0.000000E+00 X =-1.021112E-03 1.455380E-02 4.140431E-02 9.708486E-02 PASSED mne/channels/tests/test_montage.py::test_set_montage_mgh[montage] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Normal return from subroutine COBYLA NFVALS = 125 F = 1.804228E-03 MAXCV = 0.000000E+00 X =-1.021112E-03 1.455380E-02 4.140431E-02 9.708486E-02 PASSED mne/channels/tests/test_montage.py::test_set_montage_mgh[custom] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Normal return from subroutine COBYLA NFVALS = 125 F = 1.804228E-03 MAXCV = 0.000000E+00 X =-1.021112E-03 1.455380E-02 4.140431E-02 9.708486E-02 PASSED mne/channels/tests/test_montage.py::test_montage_positions_similar[fname0-mgh60-60-59-bads0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 199 samples (0.331 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Fitted sphere radius: 91.0 mm Origin head coordinates: -4.1 16.0 51.7 mm Origin device coordinates: 1.4 17.8 -10.3 mm Fitted sphere radius: 96.3 mm Origin head coordinates: -0.9 14.6 43.0 mm Origin device coordinates: 3.6 17.8 -19.5 mm Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 53 sensor positions Interpolating 6 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 96.3 mm Computing interpolation matrix from 53 sensor positions Interpolating 6 sensors PASSED mne/channels/tests/test_montage.py::test_montage_positions_similar[fname1-mgh70-70-64-None] SKIPPED mne/channels/tests/test_montage.py::test_digmontage_constructor_errors PASSED mne/channels/tests/test_montage.py::test_transform_to_head_and_compute_dev_head_t PASSED mne/channels/tests/test_montage.py::test_set_montage_with_mismatching_ch_names Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/channels/tests/test_montage.py::test_set_montage_with_sub_super_set_of_ch_names PASSED mne/channels/tests/test_montage.py::test_set_montage_with_known_aliases PASSED mne/channels/tests/test_montage.py::test_heterogeneous_ch_type Creating RawArray with float64 data, n_channels=4, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready. PASSED mne/channels/tests/test_montage.py::test_set_montage_coord_frame_in_head_vs_unknown Creating RawArray with float64 data, n_channels=3, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready. PASSED mne/channels/tests/test_montage.py::test_montage_head_frame[eeg] SKIPPED mne/channels/tests/test_montage.py::test_montage_head_frame[ecog] SKIPPED mne/channels/tests/test_montage.py::test_montage_head_frame[seeg] SKIPPED mne/channels/tests/test_montage.py::test_montage_head_frame[dbs] SKIPPED mne/channels/tests/test_montage.py::test_set_montage_with_missing_coordinates Creating RawArray with float64 data, n_channels=3, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready. PASSED mne/channels/tests/test_montage.py::test_get_montage SKIPPED (Requir...) mne/channels/tests/test_montage.py::test_read_dig_hpts PASSED mne/channels/tests/test_montage.py::test_get_builtin_montages PASSED mne/channels/tests/test_montage.py::test_plot_montage SKIPPED (Requi...) mne/channels/tests/test_montage.py::test_montage_equality PASSED mne/channels/tests/test_montage.py::test_montage_add_fiducials SKIPPED mne/channels/tests/test_montage.py::test_read_dig_localite PASSED mne/channels/tests/test_montage.py::test_make_wrong_dig_montage PASSED mne/channels/tests/test_montage.py::test_fnirs_montage SKIPPED (Requ...) mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[standard_1005] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[standard_1020] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[standard_alphabetic] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[standard_postfixed] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[standard_prefixed] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[standard_primed] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[biosemi16] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[biosemi32] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[biosemi64] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[biosemi128] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[biosemi160] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[biosemi256] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[easycap-M1] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[easycap-M10] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[easycap-M43] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[EGI_256] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[GSN-HydroCel-32] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[GSN-HydroCel-64_1.0] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[GSN-HydroCel-65_1.0] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[GSN-HydroCel-128] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[GSN-HydroCel-129] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[GSN-HydroCel-256] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[GSN-HydroCel-257] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[mgh60] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[mgh70] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[artinis-octamon] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[artinis-brite23] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_have_fids[brainproducts-RNP-BA-128] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montage_errors PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[EGI_256-1e-05-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[EGI_256-1e-05-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[easycap-M1-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[easycap-M1-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[easycap-M10-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[easycap-M10-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi128-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi128-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi16-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi16-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi160-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi160-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi256-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi256-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi32-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi32-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi64-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[biosemi64-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[brainproducts-RNP-BA-128-1e-08-0.095] PASSED mne/channels/tests/test_standard_montage.py::test_standard_montages_on_sphere[brainproducts-RNP-BA-128-1e-08-0.05] PASSED mne/channels/tests/test_standard_montage.py::test_standard_superset PASSED mne/channels/tests/test_standard_montage.py::test_set_montage_artinis_fsaverage[octamon] PASSED mne/channels/tests/test_standard_montage.py::test_set_montage_artinis_fsaverage[brite23] PASSED mne/channels/tests/test_standard_montage.py::test_set_montage_artinis_basic Creating RawArray with float64 data, n_channels=16, n_times=100 Range : 0 ... 99 = 0.000 ... 9.900 secs Ready. Creating RawArray with float64 data, n_channels=46, n_times=100 Range : 0 ... 99 = 0.000 ... 9.900 secs Ready. Creating RawArray with float64 data, n_channels=46, n_times=100 Range : 0 ... 99 = 0.000 ... 9.900 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=100 Range : 0 ... 99 = 0.000 ... 9.900 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=100 Range : 0 ... 99 = 0.000 ... 9.900 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=100 Range : 0 ... 99 = 0.000 ... 9.900 secs Ready. PASSED mne/channels/tests/test_unify_bads.py::test_error_raising Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped PASSED mne/channels/tests/test_unify_bads.py::test_bads_compilation Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/commands/tests/test_commands.py::test_browse_raw PASSED mne/commands/tests/test_commands.py::test_what PASSED mne/commands/tests/test_commands.py::test_bti2fiff PASSED mne/commands/tests/test_commands.py::test_compare_fiff PASSED mne/commands/tests/test_commands.py::test_show_fiff PASSED mne/commands/tests/test_commands.py::test_clean_eog_ecg SKIPPED (Req...) mne/commands/tests/test_commands.py::test_compute_proj_exg[mne.commands.mne_compute_proj_ecg] PASSED mne/commands/tests/test_commands.py::test_compute_proj_exg[mne.commands.mne_compute_proj_eog] PASSED mne/commands/tests/test_commands.py::test_coreg PASSED mne/commands/tests/test_commands.py::test_kit2fiff PASSED mne/commands/tests/test_commands.py::test_make_scalp_surfaces SKIPPED mne/commands/tests/test_commands.py::test_report SKIPPED (Requires t...) mne/commands/tests/test_commands.py::test_surf2bem PASSED mne/commands/tests/test_commands.py::test_watershed_bem SKIPPED (Req...) mne/commands/tests/test_commands.py::test_flash_bem SKIPPED (Require...) mne/commands/tests/test_commands.py::test_setup_source_space SKIPPED mne/commands/tests/test_commands.py::test_setup_forward_model SKIPPED mne/commands/tests/test_commands.py::test_mne_prepare_bem_model SKIPPED mne/commands/tests/test_commands.py::test_show_info PASSED mne/commands/tests/test_commands.py::test_sys_info PASSED mne/commands/tests/test_commands.py::test_anonymize Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/datasets/eegbci/eegbci.py::mne.datasets.eegbci.eegbci.data_path PASSED mne/datasets/eegbci/eegbci.py::mne.datasets.eegbci.eegbci.load_data PASSED mne/datasets/eegbci/tests/test_eegbci.py::test_eegbci_download Downloading EEGBCI data Download complete in 00s (0.0 MB) Downloading EEGBCI data Download complete in 00s (0.0 MB) Downloading EEGBCI data Download complete in 00s (0.0 MB) Downloading EEGBCI data Download complete in 00s (0.0 MB) PASSED mne/datasets/limo/limo.py::mne.datasets.limo.limo.data_path PASSED mne/datasets/sleep_physionet/age.py::mne.datasets.sleep_physionet.age.fetch_data PASSED mne/datasets/sleep_physionet/temazepam.py::mne.datasets.sleep_physionet.temazepam.fetch_data PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_run_update_age_records SKIPPED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_subjects[39] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_subjects[68] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_subjects[69] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_subjects[78] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_subjects[79] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_subjects[83] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_recordings[13-2] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_recordings[36-1] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age_missing_recordings[52-1] PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_age Download complete in 00s (0.0 MB) Download complete in 00s (0.0 MB) Download complete in 00s (0.0 MB) PASSED mne/datasets/sleep_physionet/tests/test_physionet.py::test_run_update_temazepam_records SKIPPED mne/datasets/sleep_physionet/tests/test_physionet.py::test_sleep_physionet_temazepam Download complete in 00s (0.0 MB) PASSED mne/datasets/tests/test_datasets.py::test_datasets_basic Using default location ~/mne_data for sample... Creating /tmp/tmp_mne_tempdir_dpl0brwg/mne_data Using default location ~/mne_data for sample... Using default location ~/mne_data for sample... Using default location ~/mne_data for sample... sample: False Using default location ~/mne_data for somato... Using default location ~/mne_data for somato... Using default location ~/mne_data for somato... Using default location ~/mne_data for somato... somato: False Using default location ~/mne_data for spm... Using default location ~/mne_data for spm... Using default location ~/mne_data for spm... Using default location ~/mne_data for spm... spm_face: False Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path Using default location ~/mne_data for testing... Dataset testing version 0.0 out of date, latest version is 0.152 Dataset out of date but force_update=False and download=False, returning empty data_path testing: False Using default location ~/mne_data for opm... Using default location ~/mne_data for opm... Using default location ~/mne_data for opm... Using default location ~/mne_data for opm... opm: False Using default location ~/mne_data for bst_raw... Using default location ~/mne_data for bst_raw... Using default location ~/mne_data for bst_raw... Using default location ~/mne_data for bst_raw... bst_raw: False Using default location ~/mne_data for bst_auditory... Using default location ~/mne_data for bst_auditory... Using default location ~/mne_data for bst_auditory... Using default location ~/mne_data for bst_auditory... bst_auditory: False Using default location ~/mne_data for bst_resting... Using default location ~/mne_data for bst_resting... Using default location ~/mne_data for bst_resting... Using default location ~/mne_data for bst_resting... bst_resting: False Using default location ~/mne_data for multimodal... Using default location ~/mne_data for multimodal... Using default location ~/mne_data for multimodal... Using default location ~/mne_data for multimodal... multimodal: False Using default location ~/mne_data for bst_phantom_ctf... Using default location ~/mne_data for bst_phantom_ctf... Using default location ~/mne_data for bst_phantom_ctf... Using default location ~/mne_data for bst_phantom_ctf... bst_phantom_ctf: False Using default location ~/mne_data for bst_phantom_elekta... Using default location ~/mne_data for bst_phantom_elekta... Using default location ~/mne_data for bst_phantom_elekta... Using default location ~/mne_data for bst_phantom_elekta... bst_phantom_elekta: False Using default location ~/mne_data for kiloword... Using default location ~/mne_data for kiloword... Using default location ~/mne_data for kiloword... Using default location ~/mne_data for kiloword... kiloword: False Using default location ~/mne_data for mtrf... Using default location ~/mne_data for mtrf... Using default location ~/mne_data for mtrf... Using default location ~/mne_data for mtrf... mtrf: False Using default location ~/mne_data for phantom_4dbti... Using default location ~/mne_data for phantom_4dbti... Using default location ~/mne_data for phantom_4dbti... Using default location ~/mne_data for phantom_4dbti... phantom_4dbti: False Using default location ~/mne_data for visual_92_categories... Using default location ~/mne_data for visual_92_categories... Using default location ~/mne_data for visual_92_categories... Using default location ~/mne_data for visual_92_categories... visual_92_categories: False Using default location ~/mne_data for fieldtrip_cmc... Using default location ~/mne_data for fieldtrip_cmc... Using default location ~/mne_data for fieldtrip_cmc... Using default location ~/mne_data for fieldtrip_cmc... fieldtrip_cmc: False Using default location ~/mne_data for bar... Creating /tmp/pytest-of-pbuilder1/pytest-0/test_datasets_basic0/mne_data Using default location ~/mne_data for montage coregistration... Attempting to create new mne-python configuration file: /tmp/pytest-of-pbuilder1/pytest-0/test_datasets_basic0/.mne/mne-python.json PASSED mne/datasets/tests/test_datasets.py::test_downloads SKIPPED (MNE_SKI...) mne/datasets/tests/test_datasets.py::test_fetch_parcellations SKIPPED mne/datasets/tests/test_datasets.py::test_manifest_check_download[0] PASSED mne/datasets/tests/test_datasets.py::test_manifest_check_download[1] PASSED mne/datasets/tests/test_datasets.py::test_manifest_check_download[2] PASSED mne/datasets/tests/test_datasets.py::test_infant PASSED mne/datasets/tests/test_datasets.py::test_phantom PASSED mne/datasets/tests/test_datasets.py::test_fetch_uncompressed_file SKIPPED mne/decoding/receptive_field.py::mne.decoding.receptive_field._delay_time_series PASSED mne/decoding/tests/test_base.py::test_get_coef PASSED mne/decoding/tests/test_base.py::test_get_coef_inverse_transform[Scaler-kwargs0-True] 0%| | Fitting SlidingEstimator : 0/2 [00:00 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 2.8e-14 (2.2e-16 eps * 4 dim * 31 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 2.8e-14 (2.2e-16 eps * 4 dim * 31 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Loading data for 29 events and 181 original time points ... 0 bad epochs dropped Computing rank from data with rank=None Using tolerance 4.9e-14 (2.2e-16 eps * 4 dim * 55 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.9e-14 (2.2e-16 eps * 4 dim * 55 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.9e-14 (2.2e-16 eps * 4 dim * 55 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.9e-14 (2.2e-16 eps * 4 dim * 55 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.9e-14 (2.2e-16 eps * 4 dim * 55 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.9e-14 (2.2e-16 eps * 4 dim * 55 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=2 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Estimating class=4 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 1.1 (2.2e-16 eps * 10 dim * 5.1e+14 max singular value) Estimated rank (data): 10 data: rank 10 computed from 10 data channels with 0 projectors Reducing data rank from 10 -> 10 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 1.1 (2.2e-16 eps * 10 dim * 5.1e+14 max singular value) Estimated rank (data): 10 data: rank 10 computed from 10 data channels with 0 projectors Reducing data rank from 10 -> 10 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. Estimating class=100 covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-None-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 3.1e+02 (2.2e-16 eps * 51 dim * 2.8e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 2.7e+02 (2.2e-16 eps * 51 dim * 2.4e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 2.6e+02 (2.2e-16 eps * 51 dim * 2.3e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 2.9e+02 (2.2e-16 eps * 51 dim * 2.6e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 2.9e+02 (2.2e-16 eps * 51 dim * 2.6e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 2.8e+02 (2.2e-16 eps * 51 dim * 2.5e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 3.1e+02 (2.2e-16 eps * 51 dim * 2.8e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-None-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 8.2 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 7.7 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 3.8 (2.2e-16 eps * 30 dim * 5.7e+14 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 8.2 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-None-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 5e+02 (2.2e-16 eps * 81 dim * 2.8e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.4e+02 (2.2e-16 eps * 81 dim * 2.4e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.1e+02 (2.2e-16 eps * 81 dim * 2.3e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.6e+02 (2.2e-16 eps * 81 dim * 2.6e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.6e+02 (2.2e-16 eps * 81 dim * 2.6e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 4.5e+02 (2.2e-16 eps * 81 dim * 2.5e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 5e+02 (2.2e-16 eps * 81 dim * 2.8e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-full-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-full-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-full-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-correct-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-correct-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[None-correct-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using EMPIRICAL Done. Estimating class=3 covariance using EMPIRICAL Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-None-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 3.1e+02 (2.2e-16 eps * 51 dim * 2.8e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 2.7e+02 (2.2e-16 eps * 51 dim * 2.4e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 2.6e+02 (2.2e-16 eps * 51 dim * 2.3e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 2.9e+02 (2.2e-16 eps * 51 dim * 2.6e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 2.9e+02 (2.2e-16 eps * 51 dim * 2.6e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 2.8e+02 (2.2e-16 eps * 51 dim * 2.5e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 3.1e+02 (2.2e-16 eps * 51 dim * 2.8e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-None-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 8.2 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 7.7 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 3.8 (2.2e-16 eps * 30 dim * 5.7e+14 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 8.2 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-None-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 5e+02 (2.2e-16 eps * 81 dim * 2.8e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 4.4e+02 (2.2e-16 eps * 81 dim * 2.4e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 4.1e+02 (2.2e-16 eps * 81 dim * 2.3e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 4.6e+02 (2.2e-16 eps * 81 dim * 2.6e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 4.6e+02 (2.2e-16 eps * 81 dim * 2.6e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 4.5e+02 (2.2e-16 eps * 81 dim * 2.5e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank=None Using tolerance 5e+02 (2.2e-16 eps * 81 dim * 2.8e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-full-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-full-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-full-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-correct-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-correct-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[0.001-correct-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using SHRINKAGE Done. Estimating class=3 covariance using SHRINKAGE Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-None-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 3.1e+02 (2.2e-16 eps * 51 dim * 2.8e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 2.7e+02 (2.2e-16 eps * 51 dim * 2.4e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 2.6e+02 (2.2e-16 eps * 51 dim * 2.3e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 2.9e+02 (2.2e-16 eps * 51 dim * 2.6e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 2.9e+02 (2.2e-16 eps * 51 dim * 2.6e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 2.8e+02 (2.2e-16 eps * 51 dim * 2.5e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 3.1e+02 (2.2e-16 eps * 51 dim * 2.8e+16 max singular value) Estimated rank (data): 48 data: rank 48 computed from 51 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-None-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 8.2 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 7.7 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 3.8 (2.2e-16 eps * 30 dim * 5.7e+14 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 8.1 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 8.2 (2.2e-16 eps * 30 dim * 1.2e+15 max singular value) Estimated rank (data): 28 data: rank 28 computed from 30 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-None-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank=None Using tolerance 5e+02 (2.2e-16 eps * 81 dim * 2.8e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 4.4e+02 (2.2e-16 eps * 81 dim * 2.4e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 4.1e+02 (2.2e-16 eps * 81 dim * 2.3e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 4.6e+02 (2.2e-16 eps * 81 dim * 2.6e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 4.6e+02 (2.2e-16 eps * 81 dim * 2.6e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 4.5e+02 (2.2e-16 eps * 81 dim * 2.5e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank=None Using tolerance 5e+02 (2.2e-16 eps * 81 dim * 2.8e+16 max singular value) Estimated rank (data): 76 data: rank 76 computed from 81 data channels with 0 projectors Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-full-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 51 from info Reducing data rank from 51 -> 51 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-full-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 30 from info Reducing data rank from 30 -> 30 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-full-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.3s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Computing rank from data with rank='full' data: rank 81 from info Reducing data rank from 81 -> 81 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-correct-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 51 -> 48 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 51 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-correct-eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/23 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 2) 2 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 30 -> 28 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 30 proj_id: 1 item (ndarray) proj_name: test projs: Average EEG reference: on, eeg-Raw-0.000-23.975-PCA-01: on sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_regularized_csp[oas-correct-ch_type2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 4) Dropped 0/23 epochs No channels 'grad' found. Skipping. Adding projection: eeg-Raw-0.000-23.975-PCA-01 (exp var=97.3%) 1 projection items deactivated Filtering raw data in 1 contiguous segment Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 993 samples (1.653 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Created an SSP operator (subspace dimension = 5) 5 projection items activated SSP projectors applied... Not setting metadata 29 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 5) 5 projection items activated Using data from preloaded Raw for 29 events and 181 original time points (prior to decimation) ... 0 bad epochs dropped Dropped 1 epoch: 10 Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Setting small data eigenvalues to zero (without PCA) Reducing data rank from 81 -> 76 Estimating class=1 covariance using OAS Done. Estimating class=3 covariance using OAS Done. Getting coefficients from estimator: Pipeline Last estimator is an estimator: True Removing inverse transformation from inverse list. Applying inverse transformation: . Applying inverse transformation: head transform dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra) events: 1 item (list) experimenter: MEG file_id: 4 items (dict) highpass: 2.0 Hz hpi_meas: 1 item (list) hpi_results: 1 item (list) lowpass: 40.0 Hz meas_date: 2002-12-03 19:01:10 UTC meas_id: 4 items (dict) nchan: 81 proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on, Average EEG reference: on, ... sfreq: 120.1 Hz >, 'scalings': None, 'with_mean': True, 'with_std': True})>. Squeezing first dimension of coefficients. PASSED mne/decoding/tests/test_csp.py::test_csp_pipeline PASSED mne/decoding/tests/test_csp.py::test_ajd PASSED mne/decoding/tests/test_csp.py::test_spoc Reducing data rank from 10 -> 10 Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Reducing data rank from 10 -> 10 Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Reducing data rank from 10 -> 10 Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_csp.py::test_csp_twoclass_symmetry Computing rank from data with rank=None Using tolerance 0.031 (2.2e-16 eps * 4 dim * 3.5e+13 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=class_a covariance using EMPIRICAL Done. Estimating class=class_b covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 0.031 (2.2e-16 eps * 4 dim * 3.5e+13 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=class_a covariance using EMPIRICAL Done. Estimating class=class_b covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_csp.py::test_csp_component_ordering Computing rank from data with rank=None Using tolerance 0.031 (2.2e-16 eps * 4 dim * 3.5e+13 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=class_a covariance using EMPIRICAL Done. Estimating class=class_b covariance using EMPIRICAL Done. Computing rank from data with rank=None Using tolerance 0.031 (2.2e-16 eps * 4 dim * 3.5e+13 max singular value) Estimated rank (data): 4 data: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 4 -> 4 Estimating class=class_a covariance using EMPIRICAL Done. Estimating class=class_b covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_ems.py::test_ems Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 16 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 16 events and 421 original time points ... 1 bad epochs dropped ...computing surrogate time series. This can take some time Dropped 1 epoch: 14 ...computing surrogate time series. This can take some time ...computing surrogate time series. This can take some time Not setting metadata 23 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 23 events and 421 original time points ... 1 bad epochs dropped Dropped 1 epoch: 21 ...computing surrogate time series. This can take some time ...computing surrogate time series. This can take some time Dropped 0 epochs: ...computing surrogate time series. This can take some time ...computing surrogate time series. This can take some time ...computing surrogate time series. This can take some time ...computing surrogate time series. This can take some time PASSED mne/decoding/tests/test_receptive_field.py::test_compute_reg_neighbors PASSED mne/decoding/tests/test_receptive_field.py::test_rank_deficiency Fitting 1 epochs, 1 channels 0%| | Sample : 0/2 [00:00 5 Estimating class=0 covariance using EMPIRICAL Done. Estimating class=1 covariance using EMPIRICAL Done. PASSED mne/decoding/tests/test_ssd.py::test_sorting Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 12.50 Hz) - Filter length: 825 samples (3.300 s) Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 7.00 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 14.00 Hz) - Filter length: 207 samples (0.828 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.5s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.7s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.9s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 1.2s [Parallel(n_jobs=1)]: Done 1457 tasks | elapsed: 1.5s Setting up band-pass filter from 8 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 6.00 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 15.00 Hz) - Filter length: 207 samples (0.828 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.4s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.6s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.9s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 1.2s [Parallel(n_jobs=1)]: Done 1457 tasks | elapsed: 1.5s Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Computing rank from covariance with rank=None Using tolerance 2.7e-12 (2.2e-16 eps * 20 dim * 6.1e+02 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.1e-13 (2.2e-16 eps * 20 dim * 70 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Preserving covariance rank (20) Done. Effective window size : 1.000 (s) Effective window size : 1.000 (s) Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 7.00 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 14.00 Hz) - Filter length: 207 samples (0.828 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.4s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.6s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.9s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 1.2s [Parallel(n_jobs=1)]: Done 1457 tasks | elapsed: 1.5s Setting up band-pass filter from 8 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 6.00 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 15.00 Hz) - Filter length: 207 samples (0.828 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.5s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.7s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.9s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 1.2s [Parallel(n_jobs=1)]: Done 1457 tasks | elapsed: 1.5s Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Computing rank from covariance with rank=None Using tolerance 2.7e-12 (2.2e-16 eps * 20 dim * 6.1e+02 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.1e-13 (2.2e-16 eps * 20 dim * 70 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Preserving covariance rank (20) Effective window size : 1.000 (s) Done. Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 7.00 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 14.00 Hz) - Filter length: 207 samples (0.828 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.4s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.6s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.9s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 1.2s [Parallel(n_jobs=1)]: Done 1457 tasks | elapsed: 1.5s Setting up band-pass filter from 8 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 6.00 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 15.00 Hz) - Filter length: 207 samples (0.828 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.4s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.7s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.9s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 1.2s [Parallel(n_jobs=1)]: Done 1457 tasks | elapsed: 1.5s Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Computing rank from covariance with rank=None Using tolerance 2.7e-12 (2.2e-16 eps * 20 dim * 6.1e+02 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.1e-13 (2.2e-16 eps * 20 dim * 70 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Preserving covariance rank (20) Done. Effective window size : 1.000 (s) PASSED mne/decoding/tests/test_ssd.py::test_return_filtered Setting up band-pass filter from 4 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 13.50 Hz) - Filter length: 825 samples (3.300 s) Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 12.50 Hz) - Filter length: 825 samples (3.300 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Setting up band-pass filter from 8 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 7.50 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 13.50 Hz) - Filter length: 825 samples (3.300 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Computing rank from covariance with rank=None Using tolerance 5.9e-11 (2.2e-16 eps * 20 dim * 1.3e+04 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 2.4e-11 (2.2e-16 eps * 20 dim * 5.4e+03 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Preserving covariance rank (20) Done. Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 12.50 Hz) - Filter length: 825 samples (3.300 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Effective window size : 1.000 (s) Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 12.50 Hz) - Filter length: 825 samples (3.300 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Setting up band-pass filter from 8 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 7.50 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 13.50 Hz) - Filter length: 825 samples (3.300 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Computing rank from covariance with rank=None Using tolerance 5.9e-11 (2.2e-16 eps * 20 dim * 1.3e+04 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 2.4e-11 (2.2e-16 eps * 20 dim * 5.4e+03 max singular value) Estimated rank (eeg): 20 EEG: rank 20 computed from 20 data channels with 0 projectors Preserving covariance rank (20) Done. Effective window size : 1.000 (s) PASSED mne/decoding/tests/test_ssd.py::test_non_full_rank_data Setting up band-pass filter from 4 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 13.50 Hz) - Filter length: 825 samples (3.300 s) Setting up band-pass filter from 9 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 9.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 12.50 Hz) - Filter length: 825 samples (3.300 s) Setting up band-pass filter from 8 - 13 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 7.50 Hz) - Upper passband edge: 13.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 13.50 Hz) - Filter length: 825 samples (3.300 s) Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Computing rank from covariance with rank=None Using tolerance 5.8e-11 (2.2e-16 eps * 10 dim * 2.6e+04 max singular value) Estimated rank (eeg): 5 EEG: rank 5 computed from 10 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 2.3e-11 (2.2e-16 eps * 10 dim * 1.1e+04 max singular value) Estimated rank (eeg): 5 EEG: rank 5 computed from 10 data channels with 0 projectors Projecting covariance of 10 channels to 5 rank subspace Effective window size : 1.000 (s) Done. PASSED mne/decoding/tests/test_time_frequency.py::test_timefrequency PASSED mne/decoding/tests/test_transformer.py::test_scaler[True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 15 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 15 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 15 events and 7 original time points ... 0 bad epochs dropped PASSED mne/decoding/tests/test_transformer.py::test_scaler[True-method1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 15 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 15 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 15 events and 7 original time points ... 0 bad epochs dropped PASSED mne/decoding/tests/test_transformer.py::test_scaler[False-mean] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 15 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 15 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 15 events and 7 original time points ... 0 bad epochs dropped PASSED mne/decoding/tests/test_transformer.py::test_scaler[False-median] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 15 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 15 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 15 events and 7 original time points ... 0 bad epochs dropped PASSED mne/decoding/tests/test_transformer.py::test_filterestimator Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 15 events and 421 original time points ... 1 bad epochs dropped PASSED mne/decoding/tests/test_transformer.py::test_psdestimator Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 15 events and 421 original time points ... 1 bad epochs dropped Using multitaper spectrum estimation with 7 DPSS windows Using multitaper spectrum estimation with 7 DPSS windows PASSED mne/decoding/tests/test_transformer.py::test_vectorizer PASSED mne/decoding/tests/test_transformer.py::test_unsupervised_spatial_filter Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/decoding/tests/test_transformer.py::test_temporal_filter FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal allpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation Setting up band-pass filter from 5 - 15 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 5.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 4.00 Hz) - Upper passband edge: 15.00 Hz - Upper transition bandwidth: 3.75 Hz (-6 dB cutoff frequency: 16.88 Hz) - Filter length: 165 samples (1.650 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Setting up band-pass filter from 5 - 15 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 5.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 4.00 Hz) - Upper passband edge: 15.00 Hz - Upper transition bandwidth: 3.75 Hz (-6 dB cutoff frequency: 16.88 Hz) - Filter length: 165 samples (1.650 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Setting up low-pass filter at 15 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 15.00 Hz - Upper transition bandwidth: 3.75 Hz (-6 dB cutoff frequency: 16.88 Hz) - Filter length: 89 samples (0.890 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Setting up low-pass filter at 15 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 15.00 Hz - Upper transition bandwidth: 3.75 Hz (-6 dB cutoff frequency: 16.88 Hz) - Filter length: 89 samples (0.890 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Setting up high-pass filter at 5 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 5.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 4.00 Hz) - Filter length: 165 samples (1.650 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Setting up high-pass filter at 5 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 5.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 4.00 Hz) - Filter length: 165 samples (1.650 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/decoding/tests/test_transformer.py::test_bad_triage Setting up band-pass filter from 8 - 60 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 7.00 Hz) - Upper passband edge: 60.00 Hz - Upper transition bandwidth: 15.00 Hz (-6 dB cutoff frequency: 67.50 Hz) - Filter length: 265 samples (1.656 s) PASSED mne/dipole.py::mne.dipole.Dipole.__len__ SKIPPED (all tests skipped ...) mne/epochs.py::mne.epochs.BaseEpochs.average SKIPPED (all tests skip...) mne/epochs.py::mne.epochs.equalize_epoch_counts SKIPPED (all tests s...) mne/event.py::mne.event.count_events PASSED mne/event.py::mne.event.find_events SKIPPED (all tests skipped by +S...) mne/event.py::mne.event.match_event_names PASSED mne/event.py::mne.event.merge_events PASSED mne/export/tests/test_export.py::test_export_raw_pybv[None-None-.vhdr] SKIPPED mne/export/tests/test_export.py::test_export_raw_pybv[meas_date1-None-.eeg] SKIPPED mne/export/tests/test_export.py::test_export_raw_eeglab SKIPPED (cou...) mne/export/tests/test_export.py::test_double_export_edf SKIPPED (uns...) mne/export/tests/test_export.py::test_edf_physical_range SKIPPED (un...) mne/export/tests/test_export.py::test_edf_padding[1] SKIPPED (unsafe...) mne/export/tests/test_export.py::test_edf_padding[10] SKIPPED (unsaf...) mne/export/tests/test_export.py::test_edf_padding[100] SKIPPED (unsa...) mne/export/tests/test_export.py::test_edf_padding[500] SKIPPED (unsa...) mne/export/tests/test_export.py::test_edf_padding[999] SKIPPED (unsa...) mne/export/tests/test_export.py::test_export_edf_annotations SKIPPED mne/export/tests/test_export.py::test_rawarray_edf SKIPPED (unsafe u...) mne/export/tests/test_export.py::test_edf_export_non_voltage_channels SKIPPED mne/export/tests/test_export.py::test_channel_label_too_long_for_edf_raises_error SKIPPED mne/export/tests/test_export.py::test_measurement_date_outside_range_valid_for_edf SKIPPED mne/export/tests/test_export.py::test_export_edf_signal_clipping[physical_range0-maximum] SKIPPED mne/export/tests/test_export.py::test_export_edf_signal_clipping[physical_range1-minimum] SKIPPED mne/export/tests/test_export.py::test_export_raw_edf[input_path0-Data has a non-integer] SKIPPED mne/export/tests/test_export.py::test_export_raw_edf[input_path1-EDF format requires] SKIPPED mne/export/tests/test_export.py::test_export_raw_edf_does_not_fail_on_empty_header_fields SKIPPED mne/export/tests/test_export.py::test_export_epochs_eeglab[True] SKIPPED mne/export/tests/test_export.py::test_export_epochs_eeglab[False] SKIPPED mne/export/tests/test_export.py::test_export_evokeds_to_mff[True-auto] SKIPPED mne/export/tests/test_export.py::test_export_evokeds_to_mff[True-mff] SKIPPED mne/export/tests/test_export.py::test_export_evokeds_to_mff[False-auto] SKIPPED mne/export/tests/test_export.py::test_export_evokeds_to_mff[False-mff] SKIPPED mne/export/tests/test_export.py::test_export_to_mff_no_device SKIPPED mne/export/tests/test_export.py::test_export_to_mff_incompatible_sfreq SKIPPED mne/export/tests/test_export.py::test_export_evokeds_unsupported_format[EEGLAB-set] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/export/tests/test_export.py::test_export_evokeds_unsupported_format[EDF-edf] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/export/tests/test_export.py::test_export_evokeds_unsupported_format[BrainVision-vhdr] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/export/tests/test_export.py::test_export_evokeds_unsupported_format[auto-vhdr] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/filter.py::mne.filter.FilterMixin.savgol_filter PASSED mne/filter.py::mne.filter.construct_iir_filter SKIPPED (all tests sk...) mne/filter.py::mne.filter.detrend PASSED mne/forward/_make_forward.py::mne.forward._make_forward.use_coil_def SKIPPED mne/forward/tests/test_field_interpolation.py::test_field_map_ctf SKIPPED mne/forward/tests/test_field_interpolation.py::test_legendre_val Generating Legendre table... Generating Legendre table... Generating Legendre derivative table... Generating Legendre derivative table... PASSED mne/forward/tests/test_field_interpolation.py::test_legendre_table Generating Legendre table... Generating Legendre table... Generating Legendre derivative table... Generating Legendre derivative table... PASSED mne/forward/tests/test_field_interpolation.py::test_make_field_map_eeg SKIPPED mne/forward/tests/test_field_interpolation.py::test_make_field_map_meg SKIPPED mne/forward/tests/test_field_interpolation.py::test_make_field_map_meeg SKIPPED mne/forward/tests/test_field_interpolation.py::test_as_meg_type_evoked SKIPPED mne/forward/tests/test_forward.py::test_convert_forward SKIPPED (Req...) mne/forward/tests/test_forward.py::test_io_forward SKIPPED (Requires...) mne/forward/tests/test_forward.py::test_apply_forward SKIPPED (Requi...) mne/forward/tests/test_forward.py::test_restrict_forward_to_stc SKIPPED mne/forward/tests/test_forward.py::test_restrict_forward_to_label SKIPPED mne/forward/tests/test_forward.py::test_restrict_forward_to_label_cps[True] SKIPPED mne/forward/tests/test_forward.py::test_restrict_forward_to_label_cps[False] SKIPPED mne/forward/tests/test_forward.py::test_average_forward_solution SKIPPED mne/forward/tests/test_forward.py::test_priors SKIPPED (Requires tes...) mne/forward/tests/test_forward.py::test_equalize_channels SKIPPED (R...) mne/forward/tests/test_make_forward.py::test_magnetic_dipole Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Coil too close (dist = 0 mm) PASSED mne/forward/tests/test_make_forward.py::test_make_forward_solution_kit[testing_data] SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_bti[testing_data] SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_ctf[testing_data-MNE-C] SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_ctf[testing_data-openmeeg] SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_discrete[testing_data] SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_sphere[testing_data] SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_basic SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_openmeeg[3] SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_solution_openmeeg[1] SKIPPED mne/forward/tests/test_make_forward.py::test_forward_mixed_source_space SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_dipole SKIPPED mne/forward/tests/test_make_forward.py::test_make_forward_no_meg SKIPPED mne/forward/tests/test_make_forward.py::test_use_coil_def Equiv. model fitting -> RV = 0.00366187 %% mu1 = 0.944081 lambda1 = 0.138749 mu2 = 0.665871 lambda2 = 0.684553 mu3 = -0.130647 lambda3 = -0.0129324 Set up EEG sphere model with scalp radius 10.0 mm Sphere : origin at (0.0 0.0 0.0) mm radius : 9.0 mm grid : 5.0 mm mindist : 5.0 mm Setting up the sphere... Surface CM = ( 0.0 0.0 0.0) mm Surface fits inside a sphere with radius 9.0 mm Surface extent: x = -9.0 ... 9.0 mm y = -9.0 ... 9.0 mm z = -9.0 ... 9.0 mm Grid extent: x = -10.0 ... 10.0 mm y = -10.0 ... 10.0 mm z = -10.0 ... 10.0 mm 125 sources before omitting any. 27 sources after omitting infeasible sources not within 0.0 - 9.0 mm. 1 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside. Adjusting the neighborhood info. Source space : MRI voxel -> MRI (surface RAS) 0.005000 0.000000 0.000000 -10.00 mm 0.000000 0.005000 0.000000 -10.00 mm 0.000000 0.000000 0.005000 -10.00 mm 0.000000 0.000000 0.000000 1.00 Source space : ] MRI (surface RAS) coords, ~21 kB> MRI -> head transform : instance of Transform Measurement data : instance of Info Sphere model : origin at [0. 0. 0.] mm Standard field computations Do computations in head coordinates Free source orientations Read 1 source spaces a total of 1 active source locations Coordinate transformation: MRI (surface RAS) -> head 1.000000 0.000000 0.000000 0.00 mm 0.000000 1.000000 0.000000 0.00 mm 0.000000 0.000000 1.000000 0.00 mm 0.000000 0.000000 0.000000 1.00 Read 1 MEG channels from info 105 coil definitions read Coordinate transformation: MEG device -> head 1.000000 0.000000 0.000000 0.00 mm 0.000000 1.000000 0.000000 0.00 mm 0.000000 0.000000 1.000000 0.00 mm 0.000000 0.000000 0.000000 1.00 Source space : ] MRI (surface RAS) coords, ~21 kB> MRI -> head transform : instance of Transform Measurement data : instance of Info Sphere model : origin at [0. 0. 0.] mm Standard field computations Do computations in head coordinates Free source orientations Read 1 source spaces a total of 1 active source locations Coordinate transformation: MRI (surface RAS) -> head 1.000000 0.000000 0.000000 0.00 mm 0.000000 1.000000 0.000000 0.00 mm 0.000000 0.000000 1.000000 0.00 mm 0.000000 0.000000 0.000000 1.00 Read 1 MEG channels from info Source space : ] MRI (surface RAS) coords, ~21 kB> MRI -> head transform : instance of Transform Measurement data : instance of Info Sphere model : origin at [0. 0. 0.] mm Standard field computations Do computations in head coordinates Free source orientations Read 1 source spaces a total of 1 active source locations Coordinate transformation: MRI (surface RAS) -> head 1.000000 0.000000 0.000000 0.00 mm 0.000000 1.000000 0.000000 0.00 mm 0.000000 0.000000 1.000000 0.00 mm 0.000000 0.000000 0.000000 1.00 Read 1 MEG channels from info 1 coil definition read 105 coil definitions read Coordinate transformation: MEG device -> head 1.000000 0.000000 0.000000 0.00 mm 0.000000 1.000000 0.000000 0.00 mm 0.000000 0.000000 1.000000 0.00 mm 0.000000 0.000000 0.000000 1.00 MEG coil definitions created in head coordinates. Source spaces are now in head coordinates. Using the sphere model. Computing MEG at 1 source location (free orientations)... Finished. PASSED mne/forward/tests/test_make_forward.py::test_sensors_inside_bem SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_pyvista_file_support[pyvistaqt-inst_path0] SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_pyvista_file_support[pyvistaqt-gen_montage] SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_pyvista_file_support[pyvistaqt-inst_path2] SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_pyvista_file_support[pyvistaqt-inst_path3] SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_pyvista_file_support[pyvistaqt-inst_path4] SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_pyvista_file_support[pyvistaqt-inst_path5] SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_pyvista_basic[pyvistaqt] SKIPPED mne/gui/tests/test_coreg.py::test_fullscreen[pyvistaqt] SKIPPED (Req...) mne/gui/tests/test_coreg.py::test_coreg_gui_scraper[pyvistaqt] SKIPPED mne/gui/tests/test_coreg.py::test_coreg_gui_notebook[notebook] SKIPPED mne/gui/tests/test_coreg.py::test_no_sparse_head[testing_data-pyvistaqt] SKIPPED mne/gui/tests/test_coreg.py::test_splash_closed[pyvistaqt] SKIPPED (...) mne/gui/tests/test_gui_api.py::test_gui_api[notebook] SKIPPED (Skipp...) mne/gui/tests/test_gui_api.py::test_gui_api_qt[pyvistaqt] SKIPPED (T...) mne/inverse_sparse/tests/test_gamma_map.py::test_gamma_map_standard SKIPPED mne/inverse_sparse/tests/test_gamma_map.py::test_gamma_map_vol_sphere SKIPPED mne/inverse_sparse/tests/test_mxne_debiasing.py::test_compute_debiasing Debiasing converged after 82 iterations max(|D - D0| = 4.381032e-08 < 1.000000e-07) Debiasing converged after 35 iterations max(|D - D0| = 3.372059e-06 < 1.000000e-05) PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_standard[_pytest_param] SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_meg[_pytest_param-idx0-weights0] SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_meg[_pytest_param-idx1-weights1] SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_meg[_pytest_param-idx2-weights2] SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_meg[_pytest_param-idx3-weights3] SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_meg[_pytest_param-idx4-weights4] SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_meg[_pytest_param-idx5-weights5] SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_vol_sphere SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_basic[None] PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_basic[mult] PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_basic[augment] PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_basic[sign] PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_basic[zero] PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_split_gof_basic[less] PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[1-0.75-2-10-15-7] Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[1-0.75-2-20-60-20] Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 3 reweightings! Convergence reached after 2 reweightings! Convergence reached after 3 reweightings! Convergence reached after 2 reweightings! PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[1-0.75-4-10-15-7] PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[1-0.75-4-20-60-20] Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[3-0.75-2-10-15-7] Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[3-0.75-2-20-60-20] Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[3-0.75-4-10-15-7] Convergence reached after 3 reweightings! PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure_synthetic[3-0.75-4-20-60-20] Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 3 reweightings! Convergence reached after 2 reweightings! Convergence reached after 3 reweightings! Convergence reached after 2 reweightings! Convergence reached after 3 reweightings! Convergence reached after 2 reweightings! Convergence reached after 2 reweightings! PASSED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_sure SKIPPED mne/inverse_sparse/tests/test_mxne_inverse.py::test_mxne_inverse_empty SKIPPED mne/inverse_sparse/tests/test_mxne_optim.py::test_l21_mxne -- ALPHA MAX : 306.20426383292977 Using coordinate descent Debiasing converged after 26 iterations max(|D - D0| = 1.941368e-07 < 1.000000e-06) Final active set size: 2 -- ALPHA MAX : 306.20426383292977 Using block coordinate descent Debiasing converged after 26 iterations max(|D - D0| = 1.941368e-07 < 1.000000e-06) Final active set size: 2 -- ALPHA MAX : 306.20426383292977 Using coordinate descent Iteration 1 :: p_obj 946.847964 :: dgap 937.860894 :: n_active_start 2 :: n_active_end 2 Iteration 2 :: p_obj 22.308752 :: dgap 0.000000 :: n_active_start 4 :: n_active_end 2 Convergence reached ! (gap: 2.4659740915922157e-10 < 1e-08) Debiasing converged after 26 iterations max(|D - D0| = 1.941368e-07 < 1.000000e-06) Final active set size: 2 -- ALPHA MAX : 306.20426383292977 Using block coordinate descent Iteration 1 :: p_obj 946.847964 :: dgap 937.860894 :: n_active_start 2 :: n_active_end 2 Iteration 2 :: p_obj 22.308752 :: dgap 0.000000 :: n_active_start 4 :: n_active_end 2 Convergence reached ! (gap: 3.11928260998684e-11 < 1e-08) Debiasing converged after 26 iterations max(|D - D0| = 1.941368e-07 < 1.000000e-06) Final active set size: 2 -- ALPHA MAX : 311.2841294694218 Using block coordinate descent Iteration 1 :: p_obj 898.879595 :: dgap 889.891637 :: n_active_start 2 :: n_active_end 2 Iteration 2 :: p_obj 22.305850 :: dgap 0.000000 :: n_active_start 4 :: n_active_end 2 Convergence reached ! (gap: 1.7474732771916024e-10 < 1e-08) Debiasing converged after 51 iterations max(|D - D0| = 1.727571e-07 < 1.000000e-06) Final active set size: 2 -- ALPHA MAX : 311.2841294694218 Using block coordinate descent Iteration 1 :: p_obj 898.879595 :: dgap 889.891637 :: n_active_start 2 :: n_active_end 2 Iteration 2 :: p_obj 22.305850 :: dgap 0.000000 :: n_active_start 4 :: n_active_end 2 Convergence reached ! (gap: 1.7474732771916024e-10 < 1e-08) Debiasing converged after 51 iterations max(|D - D0| = 1.727571e-07 < 1.000000e-06) Final active set size: 2 -- ALPHA MAX : 337.6083385527738 Using block coordinate descent Iteration 1 :: p_obj 16.097554 :: dgap 0.000000 :: n_active_start 2 :: n_active_end 1 Convergence reached ! (gap: 3.930662018092335e-09 < 1e-08) Debiasing converged after 39 iterations max(|D - D0| = 3.195122e-07 < 1.000000e-06) Final active set size: 1 -- ALPHA MAX : 337.6083385527738 Using block coordinate descent Iteration 1 :: p_obj 16.097554 :: dgap 0.000000 :: n_active_start 2 :: n_active_end 1 Convergence reached ! (gap: 3.930662018092335e-09 < 1e-08) Debiasing converged after 39 iterations max(|D - D0| = 3.195122e-07 < 1.000000e-06) Final active set size: 1 PASSED mne/inverse_sparse/tests/test_mxne_optim.py::test_non_convergence PASSED mne/inverse_sparse/tests/test_mxne_optim.py::test_tf_mxne Using block coordinate descent with active set approach Iteration 10 :: n_active 2 dgap 2.21e+01 :: p_obj 1300.3021128819519 :: d_obj 1278.1957154502215 Iteration 20 :: n_active 2 dgap 7.18e+00 :: p_obj 1295.703730365869 :: d_obj 1288.5237907272822 Iteration 30 :: n_active 2 dgap 4.12e+00 :: p_obj 1294.6258319605363 :: d_obj 1290.5057416705872 Iteration 40 :: n_active 2 dgap 2.76e+00 :: p_obj 1294.2702550519177 :: d_obj 1291.5061935779358 Iteration 50 :: n_active 2 dgap 1.83e+00 :: p_obj 1294.1241700125952 :: d_obj 1292.29594861999 Iteration 60 :: n_active 2 dgap 1.17e+00 :: p_obj 1294.0602329923618 :: d_obj 1292.8933906627453 Iteration 70 :: n_active 2 dgap 7.91e-01 :: p_obj 1294.0322376193178 :: d_obj 1293.2413942435123 Iteration 80 :: n_active 2 dgap 5.44e-01 :: p_obj 1294.0193357080702 :: d_obj 1293.4751903548631 Iteration 90 :: n_active 2 dgap 3.49e-01 :: p_obj 1294.0133412155874 :: d_obj 1293.6647938405604 Iteration 100 :: n_active 2 dgap 2.32e-01 :: p_obj 1294.0108069896642 :: d_obj 1293.779167548047 Iteration 110 :: n_active 2 dgap 1.57e-01 :: p_obj 1294.0096495932967 :: d_obj 1293.8527045663502 Iteration 120 :: n_active 2 dgap 1.07e-01 :: p_obj 1294.0091139035358 :: d_obj 1293.9021150818435 Iteration 130 :: n_active 2 dgap 7.32e-02 :: p_obj 1294.0088645038202 :: d_obj 1293.9356740288067 Iteration 140 :: n_active 2 dgap 5.02e-02 :: p_obj 1294.0087480198376 :: d_obj 1293.9585937653985 Iteration 150 :: n_active 2 dgap 3.44e-02 :: p_obj 1294.0086935111988 :: d_obj 1293.9742939560508 Iteration 160 :: n_active 2 dgap 2.36e-02 :: p_obj 1294.008667973004 :: d_obj 1293.9850648464956 Iteration 170 :: n_active 2 dgap 1.62e-02 :: p_obj 1294.0086559984259 :: d_obj 1293.9924589499153 Iteration 180 :: n_active 2 dgap 1.11e-02 :: p_obj 1294.0086503806901 :: d_obj 1293.9975359592495 Iteration 190 :: n_active 2 dgap 7.63e-03 :: p_obj 1294.0086477442524 :: d_obj 1294.0010218668203 Iteration 200 :: n_active 2 dgap 4.43e-03 :: p_obj 1294.0086466606217 :: d_obj 1294.0042184039098 dgap 4.43e-03 :: p_obj 1294.008647 :: d_obj 1294.004218 :: n_active 2 Iteration 10 :: n_active 2 dgap 2.68e-03 :: p_obj 1294.008646236419 :: d_obj 1294.0059679064589 Iteration 20 :: n_active 2 dgap 1.71e-03 :: p_obj 1294.0086460828315 :: d_obj 1294.0069381106623 Iteration 30 :: n_active 2 dgap 1.10e-03 :: p_obj 1294.00864601774 :: d_obj 1294.0075491400032 Iteration 40 :: n_active 2 dgap 7.08e-04 :: p_obj 1294.0086459899967 :: d_obj 1294.0079378538462 Iteration 50 :: n_active 2 dgap 4.59e-04 :: p_obj 1294.0086459781387 :: d_obj 1294.008187057986 Iteration 60 :: n_active 2 dgap 2.98e-04 :: p_obj 1294.008645973062 :: d_obj 1294.0083477266887 Iteration 70 :: n_active 2 dgap 1.94e-04 :: p_obj 1294.0086459708846 :: d_obj 1294.008451743377 Iteration 80 :: n_active 2 dgap 1.27e-04 :: p_obj 1294.0086459699492 :: d_obj 1294.0085192892127 Iteration 90 :: n_active 2 dgap 8.27e-05 :: p_obj 1294.00864596955 :: d_obj 1294.0085632508512 Iteration 100 :: n_active 2 dgap 5.41e-05 :: p_obj 1294.008645969378 :: d_obj 1294.0085919108485 Iteration 110 :: n_active 2 dgap 3.54e-05 :: p_obj 1294.008645969303 :: d_obj 1294.008610618503 Iteration 120 :: n_active 2 dgap 2.31e-05 :: p_obj 1294.0086459692723 :: d_obj 1294.008622841172 Iteration 130 :: n_active 2 dgap 1.51e-05 :: p_obj 1294.0086459692586 :: d_obj 1294.0086308324178 Iteration 140 :: n_active 2 dgap 9.91e-06 :: p_obj 1294.0086459692536 :: d_obj 1294.0086360598607 Iteration 150 :: n_active 2 dgap 6.49e-06 :: p_obj 1294.0086459692513 :: d_obj 1294.0086394807129 Iteration 160 :: n_active 2 dgap 4.25e-06 :: p_obj 1294.0086459692493 :: d_obj 1294.0086417199884 Iteration 170 :: n_active 2 dgap 2.78e-06 :: p_obj 1294.008645969249 :: d_obj 1294.0086431861378 Iteration 180 :: n_active 2 dgap 1.82e-06 :: p_obj 1294.0086459692504 :: d_obj 1294.0086441462518 Iteration 190 :: n_active 2 dgap 1.19e-06 :: p_obj 1294.00864596925 :: d_obj 1294.0086447750662 Iteration 200 :: n_active 2 dgap 7.82e-07 :: p_obj 1294.0086459692493 :: d_obj 1294.0086451869397 dgap 7.82e-07 :: p_obj 1294.008646 :: d_obj 1294.008645 :: n_active 2 Iteration 10 :: n_active 2 dgap 4.91e-07 :: p_obj 1294.0086459692477 :: d_obj 1294.0086454779598 Iteration 20 :: n_active 2 dgap 3.22e-07 :: p_obj 1294.0086459692484 :: d_obj 1294.0086456473828 Iteration 30 :: n_active 2 dgap 2.11e-07 :: p_obj 1294.0086459692473 :: d_obj 1294.0086457583727 Iteration 40 :: n_active 2 dgap 1.38e-07 :: p_obj 1294.0086459692486 :: d_obj 1294.0086458310896 Iteration 50 :: n_active 2 dgap 9.05e-08 :: p_obj 1294.0086459692502 :: d_obj 1294.0086458787312 Iteration 60 :: n_active 2 dgap 5.93e-08 :: p_obj 1294.0086459692473 :: d_obj 1294.0086459099405 Iteration 70 :: n_active 2 dgap 3.89e-08 :: p_obj 1294.0086459692488 :: d_obj 1294.0086459303911 Iteration 80 :: n_active 2 dgap 2.55e-08 :: p_obj 1294.0086459692507 :: d_obj 1294.0086459437907 Iteration 90 :: n_active 2 dgap 1.67e-08 :: p_obj 1294.0086459692484 :: d_obj 1294.0086459525671 Iteration 100 :: n_active 2 dgap 1.09e-08 :: p_obj 1294.0086459692475 :: d_obj 1294.0086459583176 Iteration 110 :: n_active 2 dgap 7.16e-09 :: p_obj 1294.008645969249 :: d_obj 1294.0086459620877 dgap 7.16e-09 :: p_obj 1294.008646 :: d_obj 1294.008646 :: n_active 2 Convergence reached! Debiasing converged after 19 iterations max(|D - D0| = 1.219391e-07 < 1.000000e-06) PASSED mne/inverse_sparse/tests/test_mxne_optim.py::test_norm_epsilon PASSED mne/inverse_sparse/tests/test_mxne_optim.py::test_dgapl21l1 Using block coordinate descent with active set approach Iteration 10 :: n_active 1 dgap 0.00e+00 :: p_obj 1863.5322414490474 :: d_obj 1863.5322414490474 dgap 0.00e+00 :: p_obj 1863.532241 :: d_obj 1863.532241 :: n_active 1 Convergence reached! Using block coordinate descent with active set approach Iteration 10 :: n_active 12 dgap 2.12e+00 :: p_obj 1241.2076905555732 :: d_obj 1239.084027940947 Iteration 20 :: n_active 12 dgap 4.07e-01 :: p_obj 1240.708277520702 :: d_obj 1240.30123462035 Iteration 30 :: n_active 12 dgap 1.25e-01 :: p_obj 1240.6709231278921 :: d_obj 1240.5455544780903 Iteration 40 :: n_active 12 dgap 4.08e-02 :: p_obj 1240.6677723047515 :: d_obj 1240.6269766563419 Iteration 50 :: n_active 12 dgap 1.34e-02 :: p_obj 1240.6674932913477 :: d_obj 1240.6541013525266 Iteration 60 :: n_active 12 dgap 4.38e-03 :: p_obj 1240.6674681506422 :: d_obj 1240.663083584775 Iteration 70 :: n_active 12 dgap 1.43e-03 :: p_obj 1240.6674658599245 :: d_obj 1240.6660388218934 Iteration 80 :: n_active 12 dgap 4.62e-04 :: p_obj 1240.6674656494938 :: d_obj 1240.6670039278733 Iteration 90 :: n_active 12 dgap 1.49e-04 :: p_obj 1240.6674656300358 :: d_obj 1240.6673170261856 Iteration 100 :: n_active 12 dgap 4.76e-05 :: p_obj 1240.6674656282262 :: d_obj 1240.667418020731 Iteration 110 :: n_active 12 dgap 1.52e-05 :: p_obj 1240.667465628057 :: d_obj 1240.667450437334 Iteration 120 :: n_active 12 dgap 4.83e-06 :: p_obj 1240.6674656280406 :: d_obj 1240.6674607978302 Iteration 130 :: n_active 12 dgap 1.53e-06 :: p_obj 1240.6674656280397 :: d_obj 1240.6674640968388 Iteration 140 :: n_active 12 dgap 4.84e-07 :: p_obj 1240.6674656280409 :: d_obj 1240.667465143934 Iteration 150 :: n_active 12 dgap 1.53e-07 :: p_obj 1240.6674656280397 :: d_obj 1240.6674654753413 Iteration 160 :: n_active 12 dgap 4.81e-08 :: p_obj 1240.6674656280402 :: d_obj 1240.6674655799752 Iteration 170 :: n_active 12 dgap 1.51e-08 :: p_obj 1240.6674656280404 :: d_obj 1240.6674656129376 Iteration 180 :: n_active 12 dgap 4.74e-09 :: p_obj 1240.6674656280386 :: d_obj 1240.6674656233017 dgap 4.74e-09 :: p_obj 1240.667466 :: d_obj 1240.667466 :: n_active 12 Convergence reached! Using block coordinate descent with active set approach Iteration 10 :: n_active 1 dgap 0.00e+00 :: p_obj 1863.5343530440375 :: d_obj 1863.5343530440375 dgap 0.00e+00 :: p_obj 1863.534353 :: d_obj 1863.534353 :: n_active 1 Convergence reached! Using block coordinate descent with active set approach dgap 1.62e+01 :: p_obj 1261.423751 :: d_obj 1245.248164 :: n_active 2 Iteration 10 :: n_active 2 dgap 2.60e+00 :: p_obj 1253.7707106120656 :: d_obj 1251.171541253669 Iteration 20 :: n_active 2 dgap 8.84e-01 :: p_obj 1253.2117304072494 :: d_obj 1252.327850237813 Iteration 30 :: n_active 2 dgap 3.83e-01 :: p_obj 1253.1299222659031 :: d_obj 1252.7464618631266 Iteration 40 :: n_active 2 dgap 1.80e-01 :: p_obj 1253.1148404655914 :: d_obj 1252.9350039400629 Iteration 50 :: n_active 2 dgap 8.53e-02 :: p_obj 1253.1119028113535 :: d_obj 1253.0266049764573 Iteration 60 :: n_active 2 dgap 4.05e-02 :: p_obj 1253.1113166622913 :: d_obj 1253.0708627473164 Iteration 70 :: n_active 2 dgap 1.91e-02 :: p_obj 1253.1111979608036 :: d_obj 1253.0920728223482 Iteration 80 :: n_active 2 dgap 9.02e-03 :: p_obj 1253.1111736925245 :: d_obj 1253.1021512254897 Iteration 90 :: n_active 2 dgap 4.24e-03 :: p_obj 1253.1111686955237 :: d_obj 1253.1069292759066 Iteration 100 :: n_active 2 dgap 1.99e-03 :: p_obj 1253.1111676612015 :: d_obj 1253.109182047464 Iteration 110 :: n_active 2 dgap 9.27e-04 :: p_obj 1253.1111674462738 :: d_obj 1253.110239965517 Iteration 120 :: n_active 2 dgap 4.32e-04 :: p_obj 1253.1111674014796 :: d_obj 1253.1107351812902 Iteration 130 :: n_active 2 dgap 2.01e-04 :: p_obj 1253.111167392124 :: d_obj 1253.1109663722884 Iteration 140 :: n_active 2 dgap 9.33e-05 :: p_obj 1253.1111673901655 :: d_obj 1253.1110740586823 Iteration 150 :: n_active 2 dgap 4.33e-05 :: p_obj 1253.1111673897533 :: d_obj 1253.1111241208794 Iteration 160 :: n_active 2 dgap 2.00e-05 :: p_obj 1253.1111673896692 :: d_obj 1253.1111473556393 Iteration 170 :: n_active 2 dgap 9.27e-06 :: p_obj 1253.1111673896507 :: d_obj 1253.1111581239054 Iteration 180 :: n_active 2 dgap 4.28e-06 :: p_obj 1253.1111673896455 :: d_obj 1253.111163108351 Iteration 190 :: n_active 2 dgap 1.98e-06 :: p_obj 1253.111167389648 :: d_obj 1253.111165413104 Iteration 200 :: n_active 2 dgap 9.12e-07 :: p_obj 1253.1111673896462 :: d_obj 1253.1111664777998 dgap 9.12e-07 :: p_obj 1253.111167 :: d_obj 1253.111166 :: n_active 2 Iteration 10 :: n_active 2 dgap 3.89e-07 :: p_obj 1253.1111673896473 :: d_obj 1253.1111670005832 Iteration 20 :: n_active 2 dgap 1.79e-07 :: p_obj 1253.1111673896469 :: d_obj 1253.1111672103816 Iteration 30 :: n_active 2 dgap 8.26e-08 :: p_obj 1253.111167389645 :: d_obj 1253.1111673070864 Iteration 40 :: n_active 2 dgap 3.80e-08 :: p_obj 1253.1111673896457 :: d_obj 1253.1111673516398 Iteration 50 :: n_active 2 dgap 1.75e-08 :: p_obj 1253.1111673896455 :: d_obj 1253.111167372156 Iteration 60 :: n_active 2 dgap 8.05e-09 :: p_obj 1253.1111673896453 :: d_obj 1253.1111673816 dgap 8.05e-09 :: p_obj 1253.111167 :: d_obj 1253.111167 :: n_active 2 Convergence reached! PASSED mne/inverse_sparse/tests/test_mxne_optim.py::test_tf_mxne_vs_mxne Using block coordinate descent with active set approach Iteration 10 :: n_active 2 dgap 1.08e-09 :: p_obj 1434.1496281344603 :: d_obj 1434.1496281333798 dgap 1.08e-09 :: p_obj 1434.149628 :: d_obj 1434.149628 :: n_active 2 Convergence reached! PASSED mne/inverse_sparse/tests/test_mxne_optim.py::test_iterative_reweighted_mxne -- ALPHA MAX : 306.20426383292977 Using block coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 306.20426383292977 Using block coordinate descent Iteration 1 :: p_obj 946.847964 :: dgap 937.860894 :: n_active_start 2 :: n_active_end 2 Iteration 2 :: p_obj 22.308752 :: dgap 0.000000 :: n_active_start 4 :: n_active_end 2 Convergence reached ! (gap: 3.11928260998684e-11 < 1e-08) Final active set size: 2 active set size 2 -- ALPHA MAX : 2238.8076005320822 Using block coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2242.5258009696636 Using block coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2242.526995953283 Using block coordinate descent Final active set size: 2 active set size 2 Convergence reached after 3 reweightings! Debiasing converged after 18 iterations max(|D - D0| = 7.543753e-07 < 1.000000e-06) -- ALPHA MAX : 306.20426383292977 Using coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2238.807600532037 Using coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2242.525800969573 Using coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2242.526995953193 Using coordinate descent Final active set size: 2 active set size 2 Convergence reached after 3 reweightings! Debiasing converged after 18 iterations max(|D - D0| = 7.543753e-07 < 1.000000e-06) -- ALPHA MAX : 311.2841294694218 Using block coordinate descent Iteration 1 :: p_obj 898.879595 :: dgap 889.891637 :: n_active_start 2 :: n_active_end 2 Iteration 2 :: p_obj 22.305850 :: dgap 0.000000 :: n_active_start 4 :: n_active_end 2 Convergence reached ! (gap: 1.7474732771916024e-10 < 1e-08) Final active set size: 2 active set size 2 -- ALPHA MAX : 2275.782555461625 Using block coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2279.7028068671802 Using block coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2279.704144745551 Using block coordinate descent Final active set size: 2 active set size 2 Convergence reached after 3 reweightings! Debiasing converged after 28 iterations max(|D - D0| = 1.678225e-07 < 1.000000e-06) -- ALPHA MAX : 311.2841294694218 Using block coordinate descent Iteration 1 :: p_obj 898.879595 :: dgap 889.891637 :: n_active_start 2 :: n_active_end 2 Iteration 2 :: p_obj 22.305850 :: dgap 0.000000 :: n_active_start 4 :: n_active_end 2 Convergence reached ! (gap: 1.7474732771916024e-10 < 1e-08) Final active set size: 2 active set size 2 -- ALPHA MAX : 2275.782555461625 Using block coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2279.7028068671802 Using block coordinate descent Final active set size: 2 active set size 2 -- ALPHA MAX : 2279.704144745551 Using block coordinate descent Final active set size: 2 active set size 2 Convergence reached after 3 reweightings! Debiasing converged after 28 iterations max(|D - D0| = 1.678225e-07 < 1.000000e-06) -- ALPHA MAX : 337.6083385527738 Using block coordinate descent Iteration 1 :: p_obj 16.097554 :: dgap 0.000000 :: n_active_start 2 :: n_active_end 1 Convergence reached ! (gap: 3.930662018092335e-09 < 1e-08) Final active set size: 1 active set size 1 -- ALPHA MAX : 2706.8187039334352 Using block coordinate descent Final active set size: 1 active set size 1 -- ALPHA MAX : 2710.790078099374 Using block coordinate descent Final active set size: 1 active set size 1 -- ALPHA MAX : 2710.7909068050803 Using block coordinate descent Final active set size: 1 active set size 1 Convergence reached after 3 reweightings! Debiasing converged after 16 iterations max(|D - D0| = 2.470113e-07 < 1.000000e-06) -- ALPHA MAX : 337.6083385527738 Using block coordinate descent Iteration 1 :: p_obj 16.097554 :: dgap 0.000000 :: n_active_start 2 :: n_active_end 1 Convergence reached ! (gap: 3.930662018092335e-09 < 1e-08) Final active set size: 1 active set size 1 -- ALPHA MAX : 2706.8187039334352 Using block coordinate descent Final active set size: 1 active set size 1 -- ALPHA MAX : 2710.790078099374 Using block coordinate descent Final active set size: 1 active set size 1 -- ALPHA MAX : 2710.7909068050803 Using block coordinate descent Final active set size: 1 active set size 1 Convergence reached after 3 reweightings! Debiasing converged after 16 iterations max(|D - D0| = 2.470113e-07 < 1.000000e-06) PASSED mne/inverse_sparse/tests/test_mxne_optim.py::test_iterative_reweighted_tfmxne dgap 2.71e+00 :: p_obj 1329.510006 :: d_obj 1326.802870 :: n_active 2 Iteration 10 :: n_active 2 dgap 1.86e-02 :: p_obj 1327.8836339528912 :: d_obj 1327.8649926193027 Iteration 20 :: n_active 2 dgap 8.57e-04 :: p_obj 1327.8806916432666 :: d_obj 1327.8798350896523 Iteration 30 :: n_active 2 dgap 3.68e-05 :: p_obj 1327.8806747190836 :: d_obj 1327.8806379469502 dgap 3.68e-05 :: p_obj 1327.880675 :: d_obj 1327.880638 :: n_active 2 Convergence reached! Iteration 1: active set size=2, E=1278.345971 Iteration 10 :: n_active 8 dgap 1.52e+00 :: p_obj 1200.3954943454091 :: d_obj 1198.8771399196503 Iteration 20 :: n_active 8 dgap 9.98e-02 :: p_obj 1200.3368396865135 :: d_obj 1200.237024819873 Iteration 30 :: n_active 8 dgap 1.52e-02 :: p_obj 1200.3351597517317 :: d_obj 1200.3199851311153 Iteration 40 :: n_active 8 dgap 2.75e-03 :: p_obj 1200.3351003999603 :: d_obj 1200.3323522335882 Iteration 50 :: n_active 8 dgap 5.14e-04 :: p_obj 1200.335098242779 :: d_obj 1200.3345838893797 Iteration 60 :: n_active 8 dgap 9.73e-05 :: p_obj 1200.3350981633469 :: d_obj 1200.335000882771 dgap 9.73e-05 :: p_obj 1200.335098 :: d_obj 1200.335001 :: n_active 8 Convergence reached! Iteration 1: active set size=8, E=1657.523763 dgap 7.21e+01 :: p_obj 966.072951 :: d_obj 893.968300 :: n_active 2 Iteration 10 :: n_active 2 dgap 7.01e+00 :: p_obj 957.9896563473658 :: d_obj 950.9829246196167 Iteration 20 :: n_active 2 dgap 2.95e+00 :: p_obj 956.3218322677437 :: d_obj 953.3698705085851 Iteration 30 :: n_active 2 dgap 1.47e+00 :: p_obj 955.629224870742 :: d_obj 954.1570009401697 Iteration 40 :: n_active 2 dgap 8.25e-01 :: p_obj 955.2917071099894 :: d_obj 954.4670287513736 Iteration 50 :: n_active 2 dgap 4.67e-01 :: p_obj 955.1111928515686 :: d_obj 954.6443572627247 Iteration 60 :: n_active 2 dgap 2.74e-01 :: p_obj 955.011274421362 :: d_obj 954.7376775356189 Iteration 70 :: n_active 2 dgap 1.67e-01 :: p_obj 954.9558341420536 :: d_obj 954.7892933970197 Iteration 80 :: n_active 2 dgap 1.03e-01 :: p_obj 954.9237852762491 :: d_obj 954.8208850509699 Iteration 90 :: n_active 2 dgap 6.49e-02 :: p_obj 954.9050975200582 :: d_obj 954.8401548378179 Iteration 100 :: n_active 2 dgap 4.12e-02 :: p_obj 954.8943646603842 :: d_obj 954.8531779300026 Iteration 110 :: n_active 2 dgap 2.74e-02 :: p_obj 954.8880702846959 :: d_obj 954.8606658590271 Iteration 120 :: n_active 2 dgap 1.87e-02 :: p_obj 954.8842594159976 :: d_obj 954.8655525744263 Iteration 130 :: n_active 2 dgap 1.30e-02 :: p_obj 954.8819403820094 :: d_obj 954.8689380083467 Iteration 140 :: n_active 2 dgap 9.19e-03 :: p_obj 954.8805240017739 :: d_obj 954.8713367418939 Iteration 150 :: n_active 2 dgap 6.56e-03 :: p_obj 954.8796568087173 :: d_obj 954.8730931735989 Iteration 160 :: n_active 2 dgap 4.77e-03 :: p_obj 954.879126745268 :: d_obj 954.8743598630263 Iteration 170 :: n_active 2 dgap 3.51e-03 :: p_obj 954.8788005600302 :: d_obj 954.8752884041745 Iteration 180 :: n_active 2 dgap 2.62e-03 :: p_obj 954.8785994806518 :: d_obj 954.8759834057478 Iteration 190 :: n_active 2 dgap 1.97e-03 :: p_obj 954.8784753469274 :: d_obj 954.8765084552892 Iteration 200 :: n_active 2 dgap 1.49e-03 :: p_obj 954.8783986223834 :: d_obj 954.8769080201847 Iteration 210 :: n_active 2 dgap 1.14e-03 :: p_obj 954.8783511517719 :: d_obj 954.8772139128785 Iteration 220 :: n_active 2 dgap 8.73e-04 :: p_obj 954.8783217550198 :: d_obj 954.877449242874 Iteration 230 :: n_active 2 dgap 6.73e-04 :: p_obj 954.8783035367512 :: d_obj 954.877631014083 Iteration 240 :: n_active 2 dgap 5.20e-04 :: p_obj 954.8782922386383 :: d_obj 954.8777718763238 Iteration 250 :: n_active 2 dgap 4.04e-04 :: p_obj 954.8782852279534 :: d_obj 954.8778813289642 Iteration 260 :: n_active 2 dgap 3.14e-04 :: p_obj 954.8782808754316 :: d_obj 954.8779665622898 Iteration 270 :: n_active 2 dgap 2.45e-04 :: p_obj 954.8782781719585 :: d_obj 954.8780330549862 Iteration 280 :: n_active 2 dgap 1.91e-04 :: p_obj 954.8782764920645 :: d_obj 954.8780850046287 Iteration 290 :: n_active 2 dgap 1.50e-04 :: p_obj 954.878275447817 :: d_obj 954.8781256419232 Iteration 300 :: n_active 2 dgap 1.17e-04 :: p_obj 954.8782747984823 :: d_obj 954.8781574627678 Iteration 310 :: n_active 2 dgap 9.20e-05 :: p_obj 954.8782743945901 :: d_obj 954.8781824013433 dgap 9.20e-05 :: p_obj 954.878274 :: d_obj 954.878182 :: n_active 2 Convergence reached! Iteration 2: active set size=2, E=1083.725460 Iteration 10 :: n_active 1 dgap 3.33e+00 :: p_obj 920.6535140242241 :: d_obj 917.3264197170581 Iteration 20 :: n_active 1 dgap 1.30e+00 :: p_obj 920.0750307817809 :: d_obj 918.7773623631372 Iteration 30 :: n_active 1 dgap 6.13e-01 :: p_obj 919.8354408526238 :: d_obj 919.2219869318044 Iteration 40 :: n_active 1 dgap 3.36e-01 :: p_obj 919.7154952846369 :: d_obj 919.379292257597 Iteration 50 :: n_active 1 dgap 1.98e-01 :: p_obj 919.6439450367393 :: d_obj 919.4458753957033 Iteration 60 :: n_active 1 dgap 1.12e-01 :: p_obj 919.6051626909392 :: d_obj 919.4936206419396 Iteration 70 :: n_active 1 dgap 7.19e-02 :: p_obj 919.5821168476482 :: d_obj 919.5102021842554 Iteration 80 :: n_active 1 dgap 4.75e-02 :: p_obj 919.5672161275418 :: d_obj 919.5196841737878 Iteration 90 :: n_active 1 dgap 3.18e-02 :: p_obj 919.5571740569827 :: d_obj 919.5253972559167 Iteration 100 :: n_active 1 dgap 2.13e-02 :: p_obj 919.5505344080324 :: d_obj 919.5292617567313 Iteration 110 :: n_active 1 dgap 1.41e-02 :: p_obj 919.5461566552103 :: d_obj 919.5320172320454 Iteration 120 :: n_active 1 dgap 9.50e-03 :: p_obj 919.5432540627219 :: d_obj 919.5337522385105 Iteration 130 :: n_active 1 dgap 6.51e-03 :: p_obj 919.5413310714968 :: d_obj 919.534816460374 Iteration 140 :: n_active 1 dgap 4.50e-03 :: p_obj 919.5400084131205 :: d_obj 919.5355105851673 Iteration 150 :: n_active 1 dgap 3.11e-03 :: p_obj 919.5390959725626 :: d_obj 919.5359837606561 Iteration 160 :: n_active 1 dgap 2.16e-03 :: p_obj 919.5384653424956 :: d_obj 919.5363089363074 Iteration 170 :: n_active 1 dgap 1.50e-03 :: p_obj 919.538028852923 :: d_obj 919.5365331780878 Iteration 180 :: n_active 1 dgap 1.04e-03 :: p_obj 919.5377263790969 :: d_obj 919.5366881240496 Iteration 190 :: n_active 1 dgap 7.21e-04 :: p_obj 919.5375165648961 :: d_obj 919.5367953326197 Iteration 200 :: n_active 1 dgap 5.01e-04 :: p_obj 919.5373709006053 :: d_obj 919.5368695863734 Iteration 210 :: n_active 1 dgap 3.49e-04 :: p_obj 919.5372696975735 :: d_obj 919.5369210572037 Iteration 220 :: n_active 1 dgap 2.43e-04 :: p_obj 919.5371993390686 :: d_obj 919.5369567600058 Iteration 230 :: n_active 1 dgap 1.69e-04 :: p_obj 919.5371503961645 :: d_obj 919.5369815401182 Iteration 240 :: n_active 1 dgap 1.18e-04 :: p_obj 919.5371163329398 :: d_obj 919.5369987483199 Iteration 250 :: n_active 1 dgap 8.19e-05 :: p_obj 919.5370926147331 :: d_obj 919.537010704065 dgap 8.19e-05 :: p_obj 919.537093 :: d_obj 919.537011 :: n_active 1 Convergence reached! Iteration 3: active set size=1, E=1045.634170 Iteration 10 :: n_active 13 dgap 1.67e-01 :: p_obj 1310.8151115272399 :: d_obj 1310.6485319404956 Iteration 20 :: n_active 13 dgap 1.39e-02 :: p_obj 1310.8044847536669 :: d_obj 1310.790564770694 Iteration 30 :: n_active 13 dgap 1.26e-03 :: p_obj 1310.8044197297327 :: d_obj 1310.8031569049285 Iteration 40 :: n_active 13 dgap 1.14e-04 :: p_obj 1310.80441930013 :: d_obj 1310.80430505851 Iteration 50 :: n_active 13 dgap 1.03e-05 :: p_obj 1310.8044192971993 :: d_obj 1310.80440903915 dgap 1.03e-05 :: p_obj 1310.804419 :: d_obj 1310.804409 :: n_active 13 Convergence reached! Iteration 1: active set size=13, E=1740.302203 Iteration 10 :: n_active 2 dgap 5.68e+00 :: p_obj 1031.8597774556492 :: d_obj 1026.1761566634389 Iteration 20 :: n_active 2 dgap 1.63e+00 :: p_obj 1030.0558076420284 :: d_obj 1028.4301368361787 Iteration 30 :: n_active 2 dgap 7.35e-01 :: p_obj 1029.5903696236924 :: d_obj 1028.8552882189153 Iteration 40 :: n_active 2 dgap 3.92e-01 :: p_obj 1029.4405559014137 :: d_obj 1029.048798051539 Iteration 50 :: n_active 2 dgap 2.19e-01 :: p_obj 1029.3896319109663 :: d_obj 1029.1710740609174 Iteration 60 :: n_active 2 dgap 1.23e-01 :: p_obj 1029.3698712029636 :: d_obj 1029.2468879531896 Iteration 70 :: n_active 2 dgap 7.52e-02 :: p_obj 1029.3622909923497 :: d_obj 1029.2870482590224 Iteration 80 :: n_active 2 dgap 4.87e-02 :: p_obj 1029.359274132525 :: d_obj 1029.3105571939086 Iteration 90 :: n_active 2 dgap 3.19e-02 :: p_obj 1029.3580640616885 :: d_obj 1029.3261860333648 Iteration 100 :: n_active 2 dgap 2.10e-02 :: p_obj 1029.3575763053564 :: d_obj 1029.3365705085594 Iteration 110 :: n_active 2 dgap 1.32e-02 :: p_obj 1029.3573819135506 :: d_obj 1029.3442183576685 Iteration 120 :: n_active 2 dgap 8.71e-03 :: p_obj 1029.3573032355155 :: d_obj 1029.3485928977027 Iteration 130 :: n_active 2 dgap 5.78e-03 :: p_obj 1029.3572712832781 :: d_obj 1029.3514957695427 Iteration 140 :: n_active 2 dgap 3.83e-03 :: p_obj 1029.357258281791 :: d_obj 1029.3534276435132 Iteration 150 :: n_active 2 dgap 2.54e-03 :: p_obj 1029.3572529830885 :: d_obj 1029.3547134711332 Iteration 160 :: n_active 2 dgap 1.68e-03 :: p_obj 1029.357250820725 :: d_obj 1029.3555686865482 Iteration 170 :: n_active 2 dgap 1.11e-03 :: p_obj 1029.3572499372378 :: d_obj 1029.356136874787 Iteration 180 :: n_active 2 dgap 7.36e-04 :: p_obj 1029.3572495758829 :: d_obj 1029.3565138907395 Iteration 190 :: n_active 2 dgap 4.86e-04 :: p_obj 1029.3572494279422 :: d_obj 1029.356763725421 Iteration 200 :: n_active 2 dgap 3.20e-04 :: p_obj 1029.3572493673212 :: d_obj 1029.3569290632072 Iteration 210 :: n_active 2 dgap 2.11e-04 :: p_obj 1029.3572493424608 :: d_obj 1029.3570383412962 Iteration 220 :: n_active 2 dgap 1.39e-04 :: p_obj 1029.3572493322586 :: d_obj 1029.3571104783457 Iteration 230 :: n_active 2 dgap 9.13e-05 :: p_obj 1029.3572493280667 :: d_obj 1029.3571580418982 dgap 9.13e-05 :: p_obj 1029.357249 :: d_obj 1029.357158 :: n_active 2 Convergence reached! Iteration 2: active set size=2, E=1155.493365 PASSED mne/io/array/tests/test_array.py::test_long_names Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/io/array/tests/test_array.py::test_array_copy Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/io/array/tests/test_array.py::test_array_raw Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=28, n_times=1803 Range : 3606 ... 5408 = 6.004 ... 9.004 secs Ready. Creating RawArray with float64 data, n_channels=28, n_times=1803 Range : 3606 ... 5408 = 6.004 ... 9.004 secs Ready. Writing /tmp/tmp_mne_tempdir_wb3n0081/test_raw.fif Closing /tmp/tmp_mne_tempdir_wb3n0081/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_wb3n0081/test_raw.fif... Isotrak not found Range : 3606 ... 5408 = 6.004 ... 9.004 secs Ready. Creating RawArray with float64 data, n_channels=28, n_times=1803 Range : 3606 ... 5408 = 6.004 ... 9.004 secs Ready. Creating RawArray with float64 data, n_channels=28, n_times=1803 Range : 3606 ... 5408 = 6.004 ... 9.004 secs Ready. Creating RawArray with float64 data, n_channels=28, n_times=1803 Range : 3606 ... 5408 = 6.004 ... 9.004 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 4 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 4.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 6.00 Hz) - Filter length: 497 samples (0.827 s) Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up high-pass filter at 16 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 16.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 14.00 Hz) - Filter length: 497 samples (0.827 s) Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up band-pass filter from 8 - 12 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 8.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 6.00 Hz) - Upper passband edge: 12.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 14.00 Hz) - Filter length: 497 samples (0.827 s) Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up band-stop filter from 4 - 16 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 2.00 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 4.00 Hz (-6 dB cutoff frequency: 18.00 Hz) - Filter length: 497 samples (0.827 s) Effective window size : 1.705 (s) Plotting power spectral density (dB=True). 3 events found on stim channel STI 014 Event IDs: [1 2 3] Not setting metadata 3 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 361 original time points ... 1 bad epochs dropped Creating RawArray with complex128 data, n_channels=1, n_times=100 Range : 0 ... 99 = 0.000 ... 0.099 secs Ready. Creating RawArray with float64 data, n_channels=10, n_times=10000 Range : 0 ... 9999 = 0.000 ... 19.529 secs Ready. Effective window size : 4.000 (s) Plotting power spectral density (dB=True). PASSED mne/io/artemis123/tests/test_artemis123.py::test_artemis_reader SKIPPED mne/io/artemis123/tests/test_artemis123.py::test_dev_head_t SKIPPED mne/io/artemis123/tests/test_artemis123.py::test_utils Converting Tristan coil file to mne loc file... Loading mne loc file /build/reproducible-path/python-mne-1.8.0/mne/io/artemis123/resources/Artemis123_mneLoc.csv Loading mne loc file /tmp/pytest-of-pbuilder1/pytest-0/test_utils0/test_gen_mne_locs.csv PASSED mne/io/base.py::mne.io.base.BaseRaw.__getitem__ SKIPPED (all tests s...) mne/io/base.py::mne.io.base.BaseRaw.__len__ SKIPPED (all tests skipp...) mne/io/besa/tests/test_besa.py::test_read_evoked_besa[fname0] Reading electrode names and types from /build/reproducible-path/python-mne-1.8.0/mne/io/besa/tests/data/simulation.elp PASSED mne/io/besa/tests/test_besa.py::test_read_evoked_besa[fname1] Reading electrode names and types from /build/reproducible-path/python-mne-1.8.0/mne/io/besa/tests/data/simulation_oldstyle.elp PASSED mne/io/besa/tests/test_besa.py::test_read_evoked_besa[fname2] Reading electrode names and types from /build/reproducible-path/python-mne-1.8.0/mne/io/besa/tests/data/simulation.elp PASSED mne/io/besa/tests/test_besa.py::test_read_evoked_besa_avr_incomplete No /tmp/pytest-of-pbuilder1/pytest-0/test_read_evoked_besa_avr_inco0/missing.elp file present containing electrode information. No .elp file found and no channel names present in the .avr file. Falling back to generic names. Marking all channels as EEG. No /tmp/pytest-of-pbuilder1/pytest-0/test_read_evoked_besa_avr_inco0/missing.elp file present containing electrode information. No .elp file found and no channel names present in the .avr file. Falling back to generic names. Marking all channels as EEG. No /tmp/pytest-of-pbuilder1/pytest-0/test_read_evoked_besa_avr_inco0/missing.elp file present containing electrode information. No .elp file found and no channel names present in the .avr file. Falling back to generic names. Marking all channels as EEG. PASSED mne/io/besa/tests/test_besa.py::test_read_evoked_besa_mul_incomplete No /tmp/pytest-of-pbuilder1/pytest-0/test_read_evoked_besa_mul_inco0/missing.elp file present containing electrode information. Marking all channels as EEG. No /tmp/pytest-of-pbuilder1/pytest-0/test_read_evoked_besa_mul_inco0/missing.elp file present containing electrode information. Marking all channels as EEG. PASSED mne/io/boxy/tests/test_boxy.py::test_boxy_load SKIPPED (Requires tes...) mne/io/boxy/tests/test_boxy.py::test_boxy_filetypes[fname0] SKIPPED mne/io/boxy/tests/test_boxy.py::test_boxy_filetypes[fname1] SKIPPED mne/io/boxy/tests/test_boxy.py::test_boxy_digaux[fname0] SKIPPED (Re...) mne/io/boxy/tests/test_boxy.py::test_boxy_digaux[fname1] SKIPPED (Re...) mne/io/boxy/tests/test_boxy.py::test_raw_properties[fname0] SKIPPED mne/io/boxy/tests/test_boxy.py::test_raw_properties[fname1] SKIPPED mne/io/boxy/tests/test_boxy.py::test_raw_properties[fname2] SKIPPED mne/io/brainvision/tests/test_brainvision.py::test_orig_units Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_meas_date[mocked_meas_date_file0] Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/brainvision_mocked_meas_date0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_meas_date[mocked_meas_date_file1] Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/brainvision_mocked_meas_date0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_meas_date[mocked_meas_date_file2] Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/brainvision_mocked_meas_date0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_meas_date[mocked_meas_date_file3] Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/brainvision_mocked_meas_date0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_meas_date[mocked_meas_date_file4] Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/brainvision_mocked_meas_date0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_meas_date[mocked_meas_date_file5] Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/brainvision_mocked_meas_date0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_meas_date[mocked_meas_date_file6] Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/brainvision_mocked_meas_date0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_vhdr_codepage_ansi Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_vhdr_codepage_ansi0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_vhdr_versions[BrainVision Data Exchange %s File Version 1.0\n] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_vhdr_versions_BrainVision0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_vhdr_versions[Brain Vision Core Data Exchange %s File, Version 2.0\n] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_vhdr_versions_Brain_Visio0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_vhdr_versions[Brain Vision Core Data Exchange %s File, Version 3.0\n] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_vhdr_versions_Brain_Visio1/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_vhdr_versions[\n] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_vhdr_versions__n_0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_ascii[ ] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_ascii___0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_ascii[,] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_ascii___1/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_ascii[+] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_ascii___2/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_ch_names_comma Extracting parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_ch_names_comma0/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_data_highpass_filters Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_g5ep44qj/test_raw.fif Closing /tmp/tmp_mne_tempdir_g5ep44qj/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_g5ep44qj/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_nkv9a4se/test_raw.fif Closing /tmp/tmp_mne_tempdir_nkv9a4se/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_nkv9a4se/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Extracting parameters from test_mixed_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_jibjaq7l/test_raw.fif Closing /tmp/tmp_mne_tempdir_jibjaq7l/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_jibjaq7l/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from test_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_dix2dtu4/test_raw.fif Closing /tmp/tmp_mne_tempdir_dix2dtu4/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_dix2dtu4/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from test_mixed_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_highpass_hz.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... PASSED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_data_lowpass_filters Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_gd_e80m4/test_raw.fif Closing /tmp/tmp_mne_tempdir_gd_e80m4/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_gd_e80m4/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_highpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_k_mpf878/test_raw.fif Closing /tmp/tmp_mne_tempdir_k_mpf878/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_k_mpf878/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Extracting parameters from test_mixed_lowpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_vl8iqfar/test_raw.fif Closing /tmp/tmp_mne_tempdir_vl8iqfar/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_vl8iqfar/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from test_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_z5fn36p6/test_raw.fif Closing /tmp/tmp_mne_tempdir_z5fn36p6/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_z5fn36p6/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from test_mixed_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass_s.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... PASSED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_data_partially_disabled_hw_filters Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_uxe6_dra/test_raw.fif Closing /tmp/tmp_mne_tempdir_uxe6_dra/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_uxe6_dra/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Extracting parameters from test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_partially_disabled_hw_filter.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... PASSED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_data_software_filters_latin1_global_units Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 250 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 250 = 0.000 ... 1.000 secs... Reading 0 ... 250 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 250 = 0.000 ... 1.000 secs... Reading 0 ... 250 = 0.000 ... 1.000 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Writing /tmp/tmp_mne_tempdir_zkod27ih/test_raw.fif Closing /tmp/tmp_mne_tempdir_zkod27ih/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_zkod27ih/test_raw.fif... Isotrak not found Range : 0 ... 250 = 0.000 ... 1.000 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Extracting parameters from test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 250 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 250 = 0.000 ... 1.000 secs... Reading 0 ... 250 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 250 = 0.000 ... 1.000 secs... Reading 0 ... 250 = 0.000 ... 1.000 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Writing /tmp/tmp_mne_tempdir_bwkppb5o/test_raw.fif Closing /tmp/tmp_mne_tempdir_bwkppb5o/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_bwkppb5o/test_raw.fif... Isotrak not found Range : 0 ... 250 = 0.000 ... 1.000 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Extracting parameters from test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter_longname.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... PASSED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_data Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vmrk... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_vf7oky7s/test_raw.fif Closing /tmp/tmp_mne_tempdir_vf7oky7s/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_vf7oky7s/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_bin_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Reading 0 ... 7899 = 0.000 ... 7.899 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Writing /tmp/tmp_mne_tempdir_fwuvmtft/test_raw.fif Closing /tmp/tmp_mne_tempdir_fwuvmtft/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_fwuvmtft/test_raw.fif... Isotrak not found Range : 0 ... 7899 = 0.000 ... 7.899 secs Ready. Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Extracting parameters from test_units.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_units.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... PASSED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_vectorized_data Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... PASSED mne/io/brainvision/tests/test_brainvision.py::test_coodinates_extraction Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/testv2.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_neuroone_export SKIPPED mne/io/brainvision/tests/test_brainvision.py::test_read_vmrk_annotations SKIPPED mne/io/brainvision/tests/test_brainvision.py::test_ignore_marker_types Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_read_vhdr_annotations_and_events SKIPPED mne/io/brainvision/tests/test_brainvision.py::test_automatic_vmrk_sfreq_recovery SKIPPED mne/io/brainvision/tests/test_brainvision.py::test_event_id_stability_when_save_and_fif_reload SKIPPED mne/io/brainvision/tests/test_brainvision.py::test_parse_impedance Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test_mixed_lowpass.vhdr... Setting channel info structure... PASSED mne/io/brainvision/tests/test_brainvision.py::test_ahdr_format SKIPPED mne/io/bti/tests/test_bti.py::test_read_2500 SKIPPED (Requires testi...) mne/io/bti/tests/test_bti.py::test_no_loc_none Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. PASSED mne/io/bti/tests/test_bti.py::test_read_config PASSED mne/io/bti/tests/test_bti.py::test_crop_append Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. 1 projection items deactivated Reading 0 ... 304 = 0.000 ... 0.299 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 0 ... 304 = 0.000 ... 0.299 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... Writing /tmp/tmp_mne_tempdir_9tmb9vtn/test_raw.fif Closing /tmp/tmp_mne_tempdir_9tmb9vtn/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_9tmb9vtn/test_raw.fif... Range : 0 ... 304 = 0.000 ... 0.299 secs Ready. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... PASSED mne/io/bti/tests/test_bti.py::test_transforms[pdf0-config0-hs0-exported0] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. PASSED mne/io/bti/tests/test_bti.py::test_transforms[pdf1-config1-hs1-exported1] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_solaris Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. PASSED mne/io/bti/tests/test_bti.py::test_raw[pdf0-config0-hs0-exported0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/exported4D_linux_raw.fif... Range : 0 ... 304 = 0.000 ... 0.299 secs Ready. Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_raw_pdf0_config0_hs0_expo0/tmp_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_raw_pdf0_config0_hs0_expo0/tmp_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_raw_pdf0_config0_hs0_expo0/tmp_raw.fif... Range : 0 ... 304 = 0.000 ... 0.299 secs Ready. PASSED mne/io/bti/tests/test_bti.py::test_raw[pdf1-config1-hs1-exported1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/exported4D_solaris_raw.fif... Range : 0 ... 677 = 0.000 ... 0.998 secs Ready. Reading 0 ... 677 = 0.000 ... 0.998 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_solaris Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_raw_pdf1_config1_hs1_expo0/tmp_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_raw_pdf1_config1_hs1_expo0/tmp_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_raw_pdf1_config1_hs1_expo0/tmp_raw.fif... Range : 0 ... 677 = 0.000 ... 0.998 secs Ready. PASSED mne/io/bti/tests/test_bti.py::test_info_no_rename_no_reorder_no_pdf[pdf0-config0-hs0-exported0] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. No pdf_fname passed, trying to construct partial info from config Reading 4D PDF file None... Creating Neuromag info structure ... ... Setting channel info structure. ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... no headshape file supplied, doing nothing. Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... no headshape file supplied, doing nothing. Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 304 = 0.000 ... 0.299 secs... PASSED mne/io/bti/tests/test_bti.py::test_info_no_rename_no_reorder_no_pdf[pdf1-config1-hs1-exported1] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_solaris Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. No pdf_fname passed, trying to construct partial info from config Reading 4D PDF file None... Creating Neuromag info structure ... ... Setting channel info structure. ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_solaris Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... no headshape file supplied, doing nothing. Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 677 = 0.000 ... 0.998 secs... Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... no headshape file supplied, doing nothing. Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 0 ... 677 = 0.000 ... 0.998 secs... PASSED mne/io/bti/tests/test_bti.py::test_no_conversion[pdf0-config0-hs0-exported0] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. PASSED mne/io/bti/tests/test_bti.py::test_no_conversion[pdf1-config1-hs1-exported1] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_solaris Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_solaris Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. PASSED mne/io/bti/tests/test_bti.py::test_bytes_io[pdf0-config0-hs0-exported0] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file <_io.BytesIO object at 0xeb57b7d0>... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from <_io.BytesIO object at 0xeb57bd98> Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. PASSED mne/io/bti/tests/test_bti.py::test_bytes_io[pdf1-config1-hs1-exported1] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_solaris... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_solaris Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Reading 4D PDF file <_io.BytesIO object at 0xebc11168>... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from <_io.BytesIO object at 0xe6f01eb0> Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. PASSED mne/io/bti/tests/test_bti.py::test_setup_headshape[hs0] PASSED mne/io/bti/tests/test_bti.py::test_setup_headshape[hs1] PASSED mne/io/bti/tests/test_bti.py::test_nan_trans[pdf0-config0-hs0-exported0] Missing values BTI dev->head transform. Replacing with identity matrix. PASSED mne/io/bti/tests/test_bti.py::test_nan_trans[pdf1-config1-hs1-exported1] Missing values BTI dev->head transform. Replacing with identity matrix. PASSED mne/io/bti/tests/test_bti.py::test_bti_ch_data[True-fname0] SKIPPED mne/io/bti/tests/test_bti.py::test_bti_ch_data[True-fname1] SKIPPED mne/io/bti/tests/test_bti.py::test_bti_ch_data[False-fname0] SKIPPED mne/io/bti/tests/test_bti.py::test_bti_ch_data[False-fname1] SKIPPED mne/io/bti/tests/test_bti.py::test_bti_set_eog SKIPPED (Requires tes...) mne/io/bti/tests/test_bti.py::test_bti_ecg_eog_emg SKIPPED (Requires...) mne/io/cnt/tests/test_cnt.py::test_old_data SKIPPED (Requires testin...) mne/io/cnt/tests/test_cnt.py::test_new_data SKIPPED (Requires testin...) mne/io/cnt/tests/test_cnt.py::test_auto_data SKIPPED (Requires testi...) mne/io/cnt/tests/test_cnt.py::test_compare_events_and_annotations SKIPPED mne/io/cnt/tests/test_cnt.py::test_reading_bytes SKIPPED (Requires t...) mne/io/cnt/tests/test_cnt.py::test_bad_spans SKIPPED (Requires testi...) mne/io/ctf/tests/test_ctf.py::test_read_ctf SKIPPED (Requires testin...) mne/io/ctf/tests/test_ctf.py::test_rawctf_clean_names SKIPPED (Requi...) mne/io/ctf/tests/test_ctf.py::test_read_spm_ctf SKIPPED (Requires sp...) mne/io/ctf/tests/test_ctf.py::test_saving_picked[0] SKIPPED (Require...) mne/io/ctf/tests/test_ctf.py::test_saving_picked[1] SKIPPED (Require...) mne/io/ctf/tests/test_ctf.py::test_read_ctf_annotations SKIPPED (Req...) mne/io/ctf/tests/test_ctf.py::test_read_ctf_annotations_smoke_test SKIPPED mne/io/ctf/tests/test_ctf.py::test_missing_res4 SKIPPED (Requires te...) mne/io/ctf/tests/test_ctf.py::test_read_ctf_mag_bad_comp SKIPPED (Re...) mne/io/ctf/tests/test_ctf.py::test_invalid_meas_date SKIPPED (Requir...) mne/io/curry/tests/test_curry.py::test_read_raw_curry[True-curry 7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry[True-curry 8] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry[True-curry 7 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry[True-curry 8 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry[False-curry 7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry[False-curry 8] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry[False-curry 7 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry[False-curry 8 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_test_raw[curry 7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_test_raw[curry 8] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_test_raw[curry 7 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_test_raw[curry 8 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_rfDC[True-curry 7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_rfDC[True-curry 8] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_rfDC[False-curry 7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_raw_curry_rfDC[False-curry 8] SKIPPED mne/io/curry/tests/test_curry.py::test_read_events_curry_are_same_as_bdf[curry 7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_events_curry_are_same_as_bdf[curry 8] SKIPPED mne/io/curry/tests/test_curry.py::test_check_missing_files SKIPPED (...) mne/io/curry/tests/test_curry.py::test_sfreq[correct sfreq] SKIPPED mne/io/curry/tests/test_curry.py::test_sfreq[correct time_step] SKIPPED mne/io/curry/tests/test_curry.py::test_sfreq[both correct] SKIPPED (...) mne/io/curry/tests/test_curry.py::test_sfreq[both 0] SKIPPED (Requir...) mne/io/curry/tests/test_curry.py::test_sfreq[mismatch] SKIPPED (Requ...) mne/io/curry/tests/test_curry.py::test_read_curry_annotations[7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_curry_annotations[8] SKIPPED mne/io/curry/tests/test_curry.py::test_read_curry_annotations[7 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_curry_annotations[8 ascii] SKIPPED mne/io/curry/tests/test_curry.py::test_read_curry_annotations_using_mocked_info[7] SKIPPED mne/io/curry/tests/test_curry.py::test_read_curry_annotations_using_mocked_info[8] SKIPPED mne/io/curry/tests/test_curry.py::test_read_curry_annotations_using_mocked_info[7 (ascii)] SKIPPED mne/io/curry/tests/test_curry.py::test_read_curry_annotations_using_mocked_info[8 (ascii)] SKIPPED mne/io/curry/tests/test_curry.py::test_read_files_missing_channel[Ref omitted, normal order] SKIPPED mne/io/curry/tests/test_curry.py::test_read_files_missing_channel[Ref omitted, reordered] SKIPPED mne/io/curry/tests/test_curry.py::test_meas_date[valid start date] SKIPPED mne/io/curry/tests/test_curry.py::test_meas_date[start date year is 0] SKIPPED mne/io/curry/tests/test_curry.py::test_meas_date[start date seconds invalid] SKIPPED mne/io/curry/tests/test_curry.py::test_dot_names[curry7] SKIPPED (Re...) mne/io/curry/tests/test_curry.py::test_dot_names[curry8] SKIPPED (Re...) mne/io/edf/edf.py::mne.io.edf.edf.RawEDF SKIPPED (all tests skipped ...) mne/io/edf/edf.py::mne.io.edf.edf.read_raw_bdf SKIPPED (all tests sk...) mne/io/edf/edf.py::mne.io.edf.edf.read_raw_edf SKIPPED (all tests sk...) mne/io/edf/tests/test_edf.py::test_orig_units Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 3071 = 0.000 ... 5.998 secs... PASSED mne/io/edf/tests/test_edf.py::test_units_params Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_edf_temperature Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_subject_info SKIPPED (Requires te...) mne/io/edf/tests/test_edf.py::test_bdf_data Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... 1 projection items deactivated Reading 0 ... 2047 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Reading 0 ... 2047 = 0.000 ... 1.000 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Writing /tmp/tmp_mne_tempdir_sfk5yjpn/test_raw.fif Closing /tmp/tmp_mne_tempdir_sfk5yjpn/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_sfk5yjpn/test_raw.fif... Isotrak not found Range : 0 ... 2047 = 0.000 ... 1.000 secs Ready. Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... 1 projection items deactivated Reading 0 ... 2047 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Reading 0 ... 2047 = 0.000 ... 1.000 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Writing /tmp/tmp_mne_tempdir_0jvn_ldu/test_raw.fif Closing /tmp/tmp_mne_tempdir_0jvn_ldu/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_0jvn_ldu/test_raw.fif... Range : 0 ... 2047 = 0.000 ... 1.000 secs Ready. Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.bdf... BDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 2047 = 0.000 ... 1.000 secs... PASSED mne/io/edf/tests/test_edf.py::test_bdf_crop_save_stim_channel SKIPPED mne/io/edf/tests/test_edf.py::test_edf_others[None-fname0] SKIPPED (...) mne/io/edf/tests/test_edf.py::test_edf_others[None-fname1] SKIPPED (...) mne/io/edf/tests/test_edf.py::test_edf_others[False-fname0] SKIPPED mne/io/edf/tests/test_edf.py::test_edf_others[False-fname1] SKIPPED mne/io/edf/tests/test_edf.py::test_edf_others[auto-fname0] SKIPPED (...) mne/io/edf/tests/test_edf.py::test_edf_others[auto-fname1] SKIPPED (...) mne/io/edf/tests/test_edf.py::test_edf_different_sfreqs[None] SKIPPED mne/io/edf/tests/test_edf.py::test_edf_different_sfreqs[False] SKIPPED mne/io/edf/tests/test_edf.py::test_edf_different_sfreqs[auto] SKIPPED mne/io/edf/tests/test_edf.py::test_edf_data_broken 1 projection items deactivated Reading 0 ... 3071 = 0.000 ... 5.998 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 3071 = 0.000 ... 5.998 secs... Reading 0 ... 3071 = 0.000 ... 5.998 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 3071 = 0.000 ... 5.998 secs... Reading 0 ... 3071 = 0.000 ... 5.998 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Writing /tmp/tmp_mne_tempdir_yq8omd2z/test_raw.fif Closing /tmp/tmp_mne_tempdir_yq8omd2z/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_yq8omd2z/test_raw.fif... Isotrak not found Range : 0 ... 3071 = 0.000 ... 5.998 secs Ready. Reading 0 ... 3071 = 0.000 ... 5.998 secs... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_edf_data_broken0/broken.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 1023 = 0.000 ... 1.998 secs... Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_edf_data_broken0/broken.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 1023 = 0.000 ... 1.998 secs... Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_edf_data_broken0/broken.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_duplicate_channel_labels_edf Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_parse_annotation PASSED mne/io/edf/tests/test_edf.py::test_find_events_backward_compatibility Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 3071 = 0.000 ... 5.998 secs... Used Annotations descriptions: ['type A', 'type B'] PASSED mne/io/edf/tests/test_edf.py::test_no_data_channels SKIPPED (Require...) mne/io/edf/tests/test_edf.py::test_to_data_frame[fname0] SKIPPED (co...) mne/io/edf/tests/test_edf.py::test_to_data_frame[fname1] SKIPPED (co...) mne/io/edf/tests/test_edf.py::test_read_raw_edf_stim_channel_input_parameters Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected PASSED mne/io/edf/tests/test_edf.py::test_read_annot PASSED mne/io/edf/tests/test_edf.py::test_read_annotations[fname0] SKIPPED mne/io/edf/tests/test_edf.py::test_read_annotations[fname1] SKIPPED mne/io/edf/tests/test_edf.py::test_read_utf8_annotations SKIPPED (Re...) mne/io/edf/tests/test_edf.py::test_read_annotations_edf PASSED mne/io/edf/tests/test_edf.py::test_read_latin1_annotations PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[basic edf] PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[reversed order] PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[w/o Hz] PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[using comma] PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[with notch filter] PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[empty string] PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[bdf_dc] PASSED mne/io/edf/tests/test_edf.py::test_edf_parse_prefilter_string[multi-ch] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[0-0] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[1.1-1.1_0] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[DC-0] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[-nan] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[1.1.1-nan] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[1.1-1.1_1] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[1-1] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[prefilter_string7-expected7] PASSED mne/io/edf/tests/test_edf.py::test_edf_prefilter_float[nan-nan] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info0--1-1.1-False-False] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info1--1--1-False-False] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info2--1--1-False-False] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info3-1-2-False-False] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info4--1--1-False-False] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info5-2-3-True-True] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info6-1-3-True-True] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info7--1-1-False-True] PASSED mne/io/edf/tests/test_edf.py::test_edf_set_prefilter[edf_info8--1--1-False-False] PASSED mne/io/edf/tests/test_edf.py::test_load_generator[fname0] SKIPPED (R...) mne/io/edf/tests/test_edf.py::test_load_generator[fname1] SKIPPED (R...) mne/io/edf/tests/test_edf.py::test_edf_stim_ch_pick_up[auto] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_edf_stim_ch_pick_up[None] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_edf_stim_ch_pick_up[single string] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_edf_stim_ch_pick_up[single int] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_edf_stim_ch_pick_up[single int (revers indexing)] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_edf_stim_ch_pick_up[int list] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_bdf_multiple_annotation_channels[False-False] SKIPPED mne/io/edf/tests/test_edf.py::test_bdf_multiple_annotation_channels[True-True] SKIPPED mne/io/edf/tests/test_edf.py::test_edf_lowpass_zero SKIPPED (Require...) mne/io/edf/tests/test_edf.py::test_edf_annot_sub_s_onset SKIPPED (Re...) mne/io/edf/tests/test_edf.py::test_invalid_date Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_invalid_date0/temp.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_invalid_date0/temp.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_invalid_date0/temp.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_invalid_date0/temp.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_empty_chars PASSED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname0-256-0-False-rev] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname1-50-0-False-rev] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_uneven_samp.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_uneven_samp.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname2-64-0-False-rev] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_edf_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test_edf_stim_channel.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname3-64-0-False-rev] SKIPPED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname4-256-0-False-rev] SKIPPED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname5-100-0-False-rev] SKIPPED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname6-256-0-False-rev] SKIPPED mne/io/edf/tests/test_edf.py::test_hp_lp_reversed[fname7-256-0-True-mod] SKIPPED mne/io/edf/tests/test_edf.py::test_degenerate TXT file detected PASSED mne/io/edf/tests/test_edf.py::test_exclude Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_exclude_duplicate_channel_data[EXPECTED0-EEG F1-Ref-False-False] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_exclude_duplicate_channel_data[EXPECTED1-EEG F1-Ref-1-False-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_exclude_duplicate_channel_data[EXPECTED2-exclude2-False-False] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_exclude_duplicate_channel_data[EXPECTED3-EEG F1-Ref-True-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_exclude_duplicate_channel_data[EXPECTED4-EEG F1-Ref-1-True-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_exclude_duplicate_channel_data[EXPECTED5-exclude5-True-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_include Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected PASSED mne/io/edf/tests/test_edf.py::test_include_duplicate_channel_data[EXPECTED0-EEG F1-Ref-False-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_include_duplicate_channel_data[EXPECTED1-EEG F1-Ref-1-False-False] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_include_duplicate_channel_data[EXPECTED2-include2-False-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_include_duplicate_channel_data[EXPECTED3-EEG F1-Ref-True-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_include_duplicate_channel_data[EXPECTED4-EEG F1-Ref-1-True-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_include_duplicate_channel_data[EXPECTED5-include5-True-True] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/duplicate_channel_labels.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/io/edf/tests/test_edf.py::test_ch_types SKIPPED (Requires testin...) mne/io/edf/tests/test_edf.py::test_anonymization SKIPPED (Requires t...) mne/io/edf/tests/test_gdf.py::test_gdf_data SKIPPED (Requires testin...) mne/io/edf/tests/test_gdf.py::test_gdf2_birthday SKIPPED (Requires t...) mne/io/edf/tests/test_gdf.py::test_gdf2_data SKIPPED (Requires testi...) mne/io/edf/tests/test_gdf.py::test_one_channel_gdf SKIPPED (Requires...) mne/io/edf/tests/test_gdf.py::test_gdf_exclude_channels SKIPPED (Req...) mne/io/edf/tests/test_gdf.py::test_gdf_include SKIPPED (Requires tes...) mne/io/eeglab/tests/test_eeglab.py::test_io_set_raw[test_raw.set] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_io_set_raw[test_raw_h5.set] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_io_set_raw[test_raw_chanloc.set] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_io_set_raw_more SKIPPED (Re...) mne/io/eeglab/tests/test_eeglab.py::test_io_set_epochs[fnames0] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_io_set_epochs[fnames1] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_io_set_epochs_events SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_degenerate SKIPPED (Require...) mne/io/eeglab/tests/test_eeglab.py::test_eeglab_annotations[fname0] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_eeglab_annotations[fname1] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_eeglab_read_annotations SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_eeglab_event_from_annot SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_position_information SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_estimate_montage_units SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_io_set_raw_2021 SKIPPED (Re...) mne/io/eeglab/tests/test_eeglab.py::test_read_single_epoch SKIPPED (...) mne/io/eeglab/tests/test_eeglab.py::test_get_montage_info_with_ch_type SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_fidsposition_information[True] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_fidsposition_information[False] SKIPPED mne/io/eeglab/tests/test_eeglab.py::test_eeglab_drop_nan_annotations SKIPPED mne/io/egi/tests/test_egi.py::test_egi_mff_pause[fname0-skip_times0-event_times0] SKIPPED mne/io/egi/tests/test_egi.py::test_egi_mff_pause[fname1-skip_times1-event_times1] SKIPPED mne/io/egi/tests/test_egi.py::test_egi_mff_pause[fname2-skip_times2-None] SKIPPED mne/io/egi/tests/test_egi.py::test_egi_mff_pause_chunks[fname0] SKIPPED mne/io/egi/tests/test_egi.py::test_egi_mff_pause_chunks[fname1] SKIPPED mne/io/egi/tests/test_egi.py::test_egi_mff_pause_chunks[fname2] SKIPPED mne/io/egi/tests/test_egi.py::test_io_egi_mff[True] SKIPPED (Require...) mne/io/egi/tests/test_egi.py::test_io_egi_mff[False] SKIPPED (Requir...) mne/io/egi/tests/test_egi.py::test_io_egi Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading 0 ... 76 = 0.000 ... 0.304 secs... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading 0 ... 76 = 0.000 ... 0.304 secs... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... 1 projection items deactivated Reading 0 ... 76 = 0.000 ... 0.304 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 76 = 0.000 ... 0.304 secs... Reading 0 ... 76 = 0.000 ... 0.304 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 76 = 0.000 ... 0.304 secs... Reading 0 ... 76 = 0.000 ... 0.304 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading 0 ... 76 = 0.000 ... 0.304 secs... Writing /tmp/tmp_mne_tempdir_rx1ph6zd/test_raw.fif Closing /tmp/tmp_mne_tempdir_rx1ph6zd/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_rx1ph6zd/test_raw.fif... Isotrak not found Range : 0 ... 76 = 0.000 ... 0.304 secs Ready. Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading 0 ... 76 = 0.000 ... 0.304 secs... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading 0 ... 76 = 0.000 ... 0.304 secs... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading 0 ... 76 = 0.000 ... 0.304 secs... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Excluding events {CELL, HXX1, SESS, XXY1} ... Synthesizing trigger channel "STI 014" ... Reading 0 ... 76 = 0.000 ... 0.304 secs... 2 events found on stim channel STI 014 Event IDs: [1 2] Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Reading EGI header from /build/reproducible-path/python-mne-1.8.0/mne/io/egi/tests/data/test_egi.raw... Reading events ... Assembling measurement info ... Used Annotations descriptions: ['TRSP', 'XXX1'] 2 events found on stim channel STI 014 Event IDs: [1 2] PASSED mne/io/egi/tests/test_egi.py::test_io_egi_pns_mff SKIPPED (Requires ...) mne/io/egi/tests/test_egi.py::test_io_egi_pns_mff_bug[True] SKIPPED mne/io/egi/tests/test_egi.py::test_io_egi_pns_mff_bug[False] SKIPPED mne/io/egi/tests/test_egi.py::test_io_egi_crop_no_preload SKIPPED (R...) mne/io/egi/tests/test_egi.py::test_io_egi_evokeds_mff[0-Category 1-0.016-signals0-bads0] SKIPPED mne/io/egi/tests/test_egi.py::test_io_egi_evokeds_mff[1-Category 2-0.0-signals1-bads1] SKIPPED mne/io/egi/tests/test_egi.py::test_read_evokeds_mff_bad_input SKIPPED mne/io/egi/tests/test_egi.py::test_egi_coord_frame SKIPPED (Requires...) mne/io/egi/tests/test_egi.py::test_meas_date[fname0-2017-02-23T11:35:13.220824+01:00-+0100] SKIPPED mne/io/egi/tests/test_egi.py::test_meas_date[fname1-2017-09-20T09:55:44.072000+01:00-+0100] SKIPPED mne/io/egi/tests/test_egi.py::test_meas_date[fname2-2018-07-30T10:46:09.621673-04:00--0400] SKIPPED mne/io/egi/tests/test_egi.py::test_meas_date[fname3-2019-10-14T10:54:27.395210-07:00--0700] SKIPPED mne/io/egi/tests/test_egi.py::test_set_standard_montage_mff[fname0-GSN-HydroCel-129] SKIPPED mne/io/egi/tests/test_egi.py::test_set_standard_montage_mff[fname1-GSN-HydroCel-257] SKIPPED mne/io/eximia/tests/test_eximia.py::test_eximia_nxe SKIPPED (Require...) mne/io/fiff/tests/test_raw_fiff.py::test_acq_skip SKIPPED (Requires ...) mne/io/fiff/tests/test_raw_fiff.py::test_fix_types Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. 102 of 102 magnetometer types replaced with T3. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 of 102 magnetometer types replaced with T3. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 0 of 304 magnetometer types replaced with T3. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_concat Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Reading 0 ... 1201 = 0.000 ... 2.000 secs... Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Reading 0 ... 1201 = 0.000 ... 2.000 secs... Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Reading 0 ... 2403 = 0.000 ... 4.001 secs... Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Reading 0 ... 1201 = 0.000 ... 2.000 secs... Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_concat0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Reading 0 ... 300 = 0.000 ... 0.499 secs... Reading 0 ... 900 = 0.000 ... 1.498 secs... Reading 0 ... 300 = 0.000 ... 0.499 secs... Reading 0 ... 900 = 0.000 ... 1.498 secs... Reading 0 ... 300 = 0.000 ... 0.499 secs... Reading 0 ... 1201 = 0.000 ... 2.000 secs... Reading 0 ... 300 = 0.000 ... 0.499 secs... Reading 0 ... 1201 = 0.000 ... 2.000 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_hash_raw SKIPPED (Requires ...) mne/io/fiff/tests/test_raw_fiff.py::test_maxshield SKIPPED (Requires...) mne/io/fiff/tests/test_raw_fiff.py::test_subject_info SKIPPED (Requi...) mne/io/fiff/tests/test_raw_fiff.py::test_copy_append SKIPPED (Requir...) mne/io/fiff/tests/test_raw_fiff.py::test_output_formats SKIPPED (Req...) mne/io/fiff/tests/test_raw_fiff.py::test_multiple_files SKIPPED (Req...) mne/io/fiff/tests/test_raw_fiff.py::test_concatenate_raws[ignore] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_concatenate_raws[warn] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_concatenate_raws[raise] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_concatenate_raws_bads_order Creating RawArray with float64 data, n_channels=3, n_times=12500 Range : 0 ... 12499 = 0.000 ... 49.996 secs Ready. Creating RawArray with float64 data, n_channels=3, n_times=12500 Range : 0 ... 12499 = 0.000 ... 49.996 secs Ready. Not setting metadata 50 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 50 events and 250 original time points ... 0 bad epochs dropped Creating RawArray with float64 data, n_channels=3, n_times=25000 Range : 0 ... 24999 = 0.000 ... 49.998 secs Ready. Creating RawArray with float64 data, n_channels=4, n_times=12500 Range : 0 ... 12499 = 0.000 ... 49.996 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_concatenate_raws_order Creating RawArray with float64 data, n_channels=2, n_times=12500 Range : 0 ... 12499 = 0.000 ... 49.996 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_split_files[meg] SKIPPED (R...) mne/io/fiff/tests/test_raw_fiff.py::test_split_files[raw] SKIPPED (R...) mne/io/fiff/tests/test_raw_fiff.py::test_bids_split_files SKIPPED (c...) mne/io/fiff/tests/test_raw_fiff.py::test_split_numbers Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw-1.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw-1.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw-2.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw-2.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw.fif... Range : 25800 ... 30607 = 42.956 ... 50.959 secs Ready. Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw-1.fif... Range : 30608 ... 35415 = 50.961 ... 58.965 secs Ready. Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_split_numbers0/sub-1_ses-2_task-3_raw-2.fif... Range : 35416 ... 40199 = 58.966 ... 66.930 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_load_bad_channels Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_withbads_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Updating bad channels: [] -> ['MEG 0422', 'MEG 0433'] Writing /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Updating bad channels: [] -> ['MEG 0422', 'MEG 0433'] Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Updating bad channels: ['MEG 0422', 'MEG 0433'] -> [] Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_load_bad_channels0/foo_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_io_raw SKIPPED (Requires te...) mne/io/fiff/tests/test_raw_fiff.py::test_io_raw_additional[fname_in0-raw.fif] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/test-bad-name.fif.gz Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/test-bad-name.fif.gz [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i0/test-bad-name.fif.gz... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_io_raw_additional[fname_in1-raw.fif.gz] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif.gz... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/raw.fif.gz Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/raw.fif.gz [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/raw.fif.gz... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/raw.fif.gz Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/raw.fif.gz [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/raw.fif.gz... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/test-bad-name.fif.gz Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/test-bad-name.fif.gz [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i1/test-bad-name.fif.gz... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 27001 = 42.956 ... 44.956 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_io_raw_additional[fname_in2-raw.fif] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/raw.fif... Read 5 compensation matrices Range : 24000 ... 28800 = 10.000 ... 12.000 secs Ready. Current compensation grade : 0 Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/raw.fif... Read 5 compensation matrices Range : 24000 ... 28800 = 10.000 ... 12.000 secs Ready. Current compensation grade : 0 Writing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/test-bad-name.fif.gz Closing /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/test-bad-name.fif.gz [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_io_raw_additional_fname_i2/test-bad-name.fif.gz... Read 5 compensation matrices Range : 24000 ... 28800 = 10.000 ... 12.000 secs Ready. Current compensation grade : 0 PASSED mne/io/fiff/tests/test_raw_fiff.py::test_io_complex[complex128] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_io_complex[complex64] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_getitem SKIPPED (Requires t...) mne/io/fiff/tests/test_raw_fiff.py::test_iter SKIPPED (Requires test...) mne/io/fiff/tests/test_raw_fiff.py::test_proj SKIPPED (Requires test...) mne/io/fiff/tests/test_raw_fiff.py::test_preload_modify[False] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_preload_modify[True] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_preload_modify[memmap2.dat] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_filter SKIPPED (Requires te...) mne/io/fiff/tests/test_raw_fiff.py::test_filter_picks Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 3.902 secs Ready. NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Filtering raw data in 1 contiguous segment Setting up band-pass filter from 10 - 30 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz) - Upper passband edge: 30.00 Hz - Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz) - Filter length: 339 samples (1.324 s) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). NOTE: pick_types() is a legacy function. New code should use inst.pick(...). PASSED mne/io/fiff/tests/test_raw_fiff.py::test_crop SKIPPED (Requires test...) mne/io/fiff/tests/test_raw_fiff.py::test_resample_equiv SKIPPED (Req...) mne/io/fiff/tests/test_raw_fiff.py::test_resample[True-512-auto-fft] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_resample[True-512-auto-polyphase] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_resample[False-512-0-fft] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_resample_stim Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_hilbert SKIPPED (Requires t...) mne/io/fiff/tests/test_raw_fiff.py::test_raw_copy SKIPPED (Requires ...) mne/io/fiff/tests/test_raw_fiff.py::test_to_data_frame SKIPPED (coul...) mne/io/fiff/tests/test_raw_fiff.py::test_to_data_frame_time_format[None] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_to_data_frame_time_format[ms] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_to_data_frame_time_format[timedelta] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_to_data_frame_time_format[datetime] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_add_channels Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 601 = 0.000 ... 1.001 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=602 Range : 0 ... 601 = 0.000 ... 1.001 secs Ready. PASSED mne/io/fiff/tests/test_raw_fiff.py::test_save SKIPPED (Requires test...) mne/io/fiff/tests/test_raw_fiff.py::test_annotation_crop SKIPPED (Re...) mne/io/fiff/tests/test_raw_fiff.py::test_with_statement SKIPPED (Req...) mne/io/fiff/tests/test_raw_fiff.py::test_compensation_raw Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 3 -> 0 Compensator constructed to change 0 -> 1 2 projection items deactivated Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 3 -> 1 Applying compensator to loaded data Compensator constructed to change 1 -> 3 Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 1 -> 3 Applying compensator to loaded data Compensator constructed to change 0 -> 3 Compensator constructed to change 1 -> 3 Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 0 -> 3 Applying compensator to loaded data Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 1 -> 3 Applying compensator to loaded data Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 3 -> 1 Applying compensator to loaded data Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Compensator constructed to change 3 -> 1 Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 1 Reading 0 ... 240 = 0.000 ... 0.500 secs... Compensator constructed to change 1 -> 3 Applying compensator to loaded data Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_compensation_raw0/raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 1 Compensator constructed to change 1 -> 3 PASSED mne/io/fiff/tests/test_raw_fiff.py::test_compensation_raw_mne SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_drop_channels_mixin SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_pick_channels_mixin[True] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_pick_channels_mixin[False] SKIPPED mne/io/fiff/tests/test_raw_fiff.py::test_equalize_channels SKIPPED (...) mne/io/fiff/tests/test_raw_fiff.py::test_memmap Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Creating RawArray with float64 data, n_channels=1, n_times=28800 Range : 0 ... 28799 = 0.000 ... 47.949 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[False-True-file] Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Writing /tmp/tmp_mne_tempdir_u3yuuwe1/test_raw.fif Closing /tmp/tmp_mne_tempdir_u3yuuwe1/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_u3yuuwe1/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[False-True-bytes] Opening raw data file <_io.BytesIO object at 0xe9d456b8>... Opening raw data file <_io.BytesIO object at 0xe9d456b8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Writing /tmp/tmp_mne_tempdir_1gvr8jed/test_raw.fif Closing /tmp/tmp_mne_tempdir_1gvr8jed/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_1gvr8jed/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file <_io.BytesIO object at 0xe9d456b8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BytesIO object at 0xe9d456b8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BytesIO object at 0xe9d456b8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[False-str-file] Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Writing /tmp/tmp_mne_tempdir_gchox382/test_raw.fif Closing /tmp/tmp_mne_tempdir_gchox382/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_gchox382/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BufferedReader name='/build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[False-str-bytes] Opening raw data file <_io.BytesIO object at 0xdcd98488>... Opening raw data file <_io.BytesIO object at 0xdcd98488>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Writing /tmp/tmp_mne_tempdir_xfguen1f/test_raw.fif Closing /tmp/tmp_mne_tempdir_xfguen1f/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_xfguen1f/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file <_io.BytesIO object at 0xdcd98488>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BytesIO object at 0xdcd98488>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file <_io.BytesIO object at 0xdcd98488>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[True-True-file] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-1.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-1.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-2.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-2.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-3.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-3.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-4.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-4.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-5.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-5.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-6.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-6.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-7.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-7.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-8.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-8.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-9.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-9.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-10.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-10.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-11.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-11.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-12.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-12.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-13.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-13.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-14.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-14.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-15.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-15.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-16.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-16.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-17.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-17.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-18.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-18.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-19.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-19.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-20.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-20.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-21.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-21.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-22.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-22.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-23.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw-23.fif [done] Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw.fif'>... Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Writing /tmp/tmp_mne_tempdir_3279813_/test_raw.fif Closing /tmp/tmp_mne_tempdir_3279813_/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_3279813_/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[True-True-bytes] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-1.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-1.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-2.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-2.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-3.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-3.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-4.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-4.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-5.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-5.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-6.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-6.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-7.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-7.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-8.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-8.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-9.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-9.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-10.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-10.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-11.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-11.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-12.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-12.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-13.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-13.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-14.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-14.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-15.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-15.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-16.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-16.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-17.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-17.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-18.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-18.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-19.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-19.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-20.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-20.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-21.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-21.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-22.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-22.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-23.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_True_bytes0/test_raw-23.fif [done] Opening raw data file <_io.BytesIO object at 0xdf038190>... Opening raw data file <_io.BytesIO object at 0xdf038190>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Writing /tmp/tmp_mne_tempdir_vbq9y9vw/test_raw.fif Closing /tmp/tmp_mne_tempdir_vbq9y9vw/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_vbq9y9vw/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Opening raw data file <_io.BytesIO object at 0xdf038190>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BytesIO object at 0xdf038190>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BytesIO object at 0xdf038190>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[True-str-file] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-1.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-1.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-2.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-2.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-3.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-3.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-4.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-4.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-5.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-5.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-6.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-6.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-7.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-7.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-8.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-8.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-9.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-9.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-10.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-10.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-11.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-11.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-12.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-12.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-13.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-13.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-14.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-14.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-15.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-15.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-16.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-16.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-17.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-17.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-18.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-18.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-19.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-19.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-20.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-20.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-21.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-21.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-22.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-22.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-23.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw-23.fif [done] Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw.fif'>... Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Writing /tmp/tmp_mne_tempdir__2cmp0lw/test_raw.fif Closing /tmp/tmp_mne_tempdir__2cmp0lw/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir__2cmp0lw/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BufferedReader name='/tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_file_0/test_raw.fif'>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_file_like[True-str-bytes] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-1.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-1.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-2.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-2.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-3.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-3.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-4.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-4.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-5.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-5.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-6.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-6.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-7.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-7.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-8.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-8.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-9.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-9.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-10.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-10.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-11.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-11.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-12.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-12.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-13.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-13.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-14.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-14.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-15.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-15.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-16.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-16.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-17.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-17.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-18.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-18.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-19.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-19.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-20.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-20.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-21.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-21.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-22.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-22.fif Writing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-23.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_file_like_True_str_bytes_0/test_raw-23.fif [done] Opening raw data file <_io.BytesIO object at 0xdef037f8>... Opening raw data file <_io.BytesIO object at 0xdef037f8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Writing /tmp/tmp_mne_tempdir_319b9zwp/test_raw.fif Closing /tmp/tmp_mne_tempdir_319b9zwp/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_319b9zwp/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Opening raw data file <_io.BytesIO object at 0xdef037f8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BytesIO object at 0xdef037f8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... Opening raw data file <_io.BytesIO object at 0xdef037f8>... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 26399 = 42.956 ... 43.953 secs Ready. Reading 0 ... 599 = 0.000 ... 0.997 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_str_like Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_bad_acq[fname0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/fiff/tests/test_raw_fiff.py::test_bad_acq[fname1] SKIPPED (Re...) mne/io/fiff/tests/test_raw_fiff.py::test_bad_acq[fname2] SKIPPED (Re...) mne/io/fiff/tests/test_raw_fiff.py::test_split_symlink SKIPPED (Requ...) mne/io/fiff/tests/test_raw_fiff.py::test_corrupted[0] SKIPPED (Requi...) mne/io/fiff/tests/test_raw_fiff.py::test_corrupted[1] SKIPPED (Requi...) mne/io/fiff/tests/test_raw_fiff.py::test_expand_user SKIPPED (Requir...) mne/io/fil/tests/test_fil.py::test_fil_complete SKIPPED (Requires te...) mne/io/fil/tests/test_fil.py::test_fil_no_positions SKIPPED (Require...) mne/io/fil/tests/test_fil.py::test_fil_bad_channel_spec SKIPPED (Req...) mne/io/hitachi/hitachi.py::mne.io.hitachi.hitachi.RawHitachi PASSED mne/io/hitachi/hitachi.py::mne.io.hitachi.hitachi.read_raw_hitachi PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.18-48-60-0.1-2-date0-None-True] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) 1 projection items deactivated Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Writing /tmp/tmp_mne_tempdir_vr7gfjo8/test_raw.fif Closing /tmp/tmp_mne_tempdir_vr7gfjo8/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_vr7gfjo8/test_raw.fif... Isotrak not found Range : 0 ... 59 = 0.000 ... 5.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_0/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.18-48-60-0.1-2-date0-None-False] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) 1 projection items deactivated Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Writing /tmp/tmp_mne_tempdir_o98o2on9/test_raw.fif Closing /tmp/tmp_mne_tempdir_o98o2on9/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_o98o2on9/test_raw.fif... Isotrak not found Range : 0 ... 59 = 0.000 ... 5.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_18_48_60_1/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Reading 0 ... 59 = 0.000 ... 5.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.25-108-10-5.0-1-date1-\r-True] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) 1 projection items deactivated Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Writing /tmp/tmp_mne_tempdir_vqidg3hm/test_raw.fif Closing /tmp/tmp_mne_tempdir_vqidg3hm/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_vqidg3hm/test_raw.fif... Isotrak not found Range : 0 ... 9 = 0.000 ... 0.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_100/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.25-108-10-5.0-1-date1-\r-False] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) 1 projection items deactivated Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Writing /tmp/tmp_mne_tempdir_c0ir47f5/test_raw.fif Closing /tmp/tmp_mne_tempdir_c0ir47f5/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_c0ir47f5/test_raw.fif... Isotrak not found Range : 0 ... 9 = 0.000 ... 0.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_101/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.25-108-10-5.0-1-date2-\n-True] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) 1 projection items deactivated Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Writing /tmp/tmp_mne_tempdir_2t8itkzo/test_raw.fif Closing /tmp/tmp_mne_tempdir_2t8itkzo/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_2t8itkzo/test_raw.fif... Isotrak not found Range : 0 ... 9 = 0.000 ... 0.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_102/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.25-108-10-5.0-1-date2-\n-False] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) 1 projection items deactivated Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Writing /tmp/tmp_mne_tempdir_b7criao4/test_raw.fif Closing /tmp/tmp_mne_tempdir_b7criao4/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_b7criao4/test_raw.fif... Isotrak not found Range : 0 ... 9 = 0.000 ... 0.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_103/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.25-108-10-5.0-1-date3-\r\n-True] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) 1 projection items deactivated Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Writing /tmp/tmp_mne_tempdir_mlsrd218/test_raw.fif Closing /tmp/tmp_mne_tempdir_mlsrd218/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_mlsrd218/test_raw.fif... Isotrak not found Range : 0 ... 9 = 0.000 ... 0.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_104/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[1.25-108-10-5.0-1-date3-\r\n-False] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) 1 projection items deactivated Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Writing /tmp/tmp_mne_tempdir_3l5y10z9/test_raw.fif Closing /tmp/tmp_mne_tempdir_3l5y10z9/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_3l5y10z9/test_raw.fif... Isotrak not found Range : 0 ... 9 = 0.000 ... 0.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_1_25_108_105/test1.csv Reading Hitachi fNIRS file version 1.25 Constructing pairing matrix for ETG-4000 (3x11) Reading 0 ... 9 = 0.000 ... 0.900 secs... Reading 0 ... 9 = 0.000 ... 0.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[version4-92-60-0.1-2-date4-None-True] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) 1 projection items deactivated Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Writing /tmp/tmp_mne_tempdir_76nmbh3c/test_raw.fif Closing /tmp/tmp_mne_tempdir_76nmbh3c/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_76nmbh3c/test_raw.fif... Range : 0 ... 59 = 0.000 ... 5.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_920/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_hitachi_basic[version4-92-60-0.1-2-date4-None-False] Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) 1 projection items deactivated Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Reading 0 ... 59 = 0.000 ... 5.900 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Writing /tmp/tmp_mne_tempdir_3x37_hdi/test_raw.fif Closing /tmp/tmp_mne_tempdir_3x37_hdi/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_3x37_hdi/test_raw.fif... Range : 0 ... 59 = 0.000 ... 5.900 secs Ready. Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test1.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Loading /tmp/pytest-of-pbuilder1/pytest-0/test_hitachi_basic_version4_921/test2.csv Reading Hitachi fNIRS file version 1.18 Constructing pairing matrix for ETG-7000 (3x5) Reading 0 ... 59 = 0.000 ... 5.900 secs... Reading 0 ... 59 = 0.000 ... 5.900 secs... PASSED mne/io/hitachi/tests/test_hitachi.py::test_compute_pairs[3-3-2] PASSED mne/io/hitachi/tests/test_hitachi.py::test_compute_pairs[3-5-1] PASSED mne/io/hitachi/tests/test_hitachi.py::test_compute_pairs[4-4-1] PASSED mne/io/hitachi/tests/test_hitachi.py::test_compute_pairs[3-11-1] PASSED mne/io/kit/tests/test_coreg.py::test_io_mrk PASSED mne/io/kit/tests/test_kit.py::test_data SKIPPED (Requires testing da...) mne/io/kit/tests/test_kit.py::test_unknown_format SKIPPED (Requires ...) mne/io/kit/tests/test_kit.py::test_ricoh_data[fname0-Meg160/Analysis (1001) V3R000 PQA160C] SKIPPED mne/io/kit/tests/test_kit.py::test_ricoh_data[fname1-Meg160/Analysis (1001) V3R000 PQA160C] SKIPPED mne/io/kit/tests/test_kit.py::test_epochs Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Loading data for 2 events and 100 original time points ... 0 bad epochs dropped Extracting KIT Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test-epoch.raw... Setting channel info structure... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Ready. 0 bad epochs dropped PASSED mne/io/kit/tests/test_kit.py::test_raw_events Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) 5 events found on stim channel STI 014 Event IDs: [ 0 254 255] Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. 4 events found on stim channel STI 014 Event IDs: [0 1] Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. 4 events found on stim channel STI 014 Event IDs: [ 0 128] Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. 4 events found on stim channel STI 014 Event IDs: [ 0 160] Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. 4 events found on stim channel STI 014 Event IDs: [ 0 160] PASSED mne/io/kit/tests/test_kit.py::test_ch_loc Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test_bin_raw.fif... Range : 0 ... 1999 = 0.000 ... 1.999 secs Ready. Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. PASSED mne/io/kit/tests/test_kit.py::test_hsp_elp Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. PASSED mne/io/kit/tests/test_kit.py::test_decimate Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. PASSED mne/io/kit/tests/test_kit.py::test_raw_system_id[fname0-Meg160/Analysis (1001) V2R004 PQA160C-1001] SKIPPED mne/io/kit/tests/test_kit.py::test_raw_system_id[fname1-RICOH MEG System (10020) V3R000 RICOH160-1-10020] SKIPPED mne/io/kit/tests/test_kit.py::test_raw_system_id[fname2-RICOH MEG System (10021) V3R000 RICOH160-1-10021] SKIPPED mne/io/kit/tests/test_kit.py::test_raw_system_id[fname3-Yokogawa Electric Corporation/MEG device for infants/151-channel MEG System (903) V2R004 PQ1151R-903] SKIPPED mne/io/kit/tests/test_kit.py::test_berlin SKIPPED (Requires testing ...) mne/io/nedf/tests/test_nedf.py::test_nedf_header_parser[0] PASSED mne/io/nedf/tests/test_nedf.py::test_nedf_header_parser[3] PASSED mne/io/nedf/tests/test_nedf.py::test_invalid_headers PASSED mne/io/nedf/tests/test_nedf.py::test_nedf_data SKIPPED (Requires tes...) mne/io/nicolet/tests/test_nicolet.py::test_data Reading header... Reading 0 ... 511 = 0.000 ... 1.996 secs... Reading header... Reading 0 ... 511 = 0.000 ... 1.996 secs... Reading header... Reading header... 1 projection items deactivated Reading 0 ... 511 = 0.000 ... 1.996 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 511 = 0.000 ... 1.996 secs... Reading 0 ... 511 = 0.000 ... 1.996 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 511 = 0.000 ... 1.996 secs... Reading 0 ... 511 = 0.000 ... 1.996 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading header... Reading 0 ... 511 = 0.000 ... 1.996 secs... Writing /tmp/tmp_mne_tempdir_mg93_jga/test_raw.fif Closing /tmp/tmp_mne_tempdir_mg93_jga/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_mg93_jga/test_raw.fif... Isotrak not found Range : 0 ... 511 = 0.000 ... 1.996 secs Ready. Reading header... Reading 0 ... 511 = 0.000 ... 1.996 secs... Reading header... Reading header... Reading 0 ... 511 = 0.000 ... 1.996 secs... Reading header... Reading header... Reading 0 ... 511 = 0.000 ... 1.996 secs... Reading header... Reading header... Reading 0 ... 511 = 0.000 ... 1.996 secs... PASSED mne/io/nihon/tests/test_nihon.py::test_nihon_eeg SKIPPED (Requires t...) mne/io/nihon/tests/test_nihon.py::test_nihon_duplicate_channels SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirsport_v2_matches_snirf[nirx_snirf0] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirsport_v2_matches_snirf[nirx_snirf1] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirsport_v2 SKIPPED (Requires t...) mne/io/nirx/tests/test_nirx.py::test_nirsport_v1_wo_sat SKIPPED (Req...) mne/io/nirx/tests/test_nirx.py::test_nirsport_v1_w_sat SKIPPED (Requ...) mne/io/nirx/tests/test_nirx.py::test_nirsport_v1_w_bad_sat[None-True] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirsport_v1_w_bad_sat[None-False] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirsport_v1_w_bad_sat[orig-True] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirsport_v1_w_bad_sat[orig-False] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirx_hdr_load SKIPPED (Requires...) mne/io/nirx/tests/test_nirx.py::test_nirx_missing_warn SKIPPED (Requ...) mne/io/nirx/tests/test_nirx.py::test_nirx_missing_evt SKIPPED (Requi...) mne/io/nirx/tests/test_nirx.py::test_nirx_dat_warn SKIPPED (Requires...) mne/io/nirx/tests/test_nirx.py::test_nirx_15_2_short SKIPPED (Requir...) mne/io/nirx/tests/test_nirx.py::test_nirx_15_3_short SKIPPED (Requir...) mne/io/nirx/tests/test_nirx.py::test_locale_encoding SKIPPED (Requir...) mne/io/nirx/tests/test_nirx.py::test_nirx_15_2 SKIPPED (Requires tes...) mne/io/nirx/tests/test_nirx.py::test_nirx_aurora_2021_9_6 SKIPPED (R...) mne/io/nirx/tests/test_nirx.py::test_nirx_15_0 SKIPPED (Requires tes...) mne/io/nirx/tests/test_nirx.py::test_nirx_standard[fname0-1] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirx_standard[fname1-0] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirx_standard[fname2-0] SKIPPED mne/io/nirx/tests/test_nirx.py::test_nirx_standard[fname3-0] SKIPPED mne/io/nirx/tests/test_nirx.py::test_channel_order[fname0-want_order0] SKIPPED mne/io/nirx/tests/test_nirx.py::test_channel_order[fname1-want_order1] SKIPPED mne/io/nirx/tests/test_nirx.py::test_channel_order[fname2-want_order2] SKIPPED mne/io/nirx/tests/test_nirx.py::test_channel_order[fname3-want_order3] SKIPPED mne/io/nirx/tests/test_nirx.py::test_channel_order[fname4-want_order4] SKIPPED mne/io/nirx/tests/test_nirx.py::test_channel_order[fname5-want_order5] SKIPPED mne/io/nirx/tests/test_nirx.py::test_channel_order[fname6-want_order6] SKIPPED mne/io/nsx/tests/test_nsx.py::test_decode_online_filters PASSED mne/io/nsx/tests/test_nsx.py::test_filetype_checks SKIPPED (Requires...) mne/io/nsx/tests/test_nsx.py::test_nsx_ver_31 SKIPPED (Requires test...) mne/io/nsx/tests/test_nsx.py::test_nsx_ver_22 SKIPPED (Requires test...) mne/io/nsx/tests/test_nsx.py::test_stim_eog_misc_chs_in_nsx SKIPPED mne/io/nsx/tests/test_nsx.py::test_nsx_ver_21 SKIPPED (Requires test...) mne/io/nsx/tests/test_nsx.py::test_nsx SKIPPED (Requires testing dat...) mne/io/persyst/tests/test_persyst.py::test_persyst_lay_load SKIPPED mne/io/persyst/tests/test_persyst.py::test_persyst_raw SKIPPED (Requ...) mne/io/persyst/tests/test_persyst.py::test_persyst_dates SKIPPED (Re...) mne/io/persyst/tests/test_persyst.py::test_persyst_wrong_file SKIPPED mne/io/persyst/tests/test_persyst.py::test_persyst_moved_file SKIPPED mne/io/persyst/tests/test_persyst.py::test_persyst_standard SKIPPED mne/io/persyst/tests/test_persyst.py::test_persyst_annotations SKIPPED mne/io/persyst/tests/test_persyst.py::test_persyst_errors SKIPPED (R...) mne/io/tests/test_apply_function.py::test_apply_function_verbose Creating RawArray with float64 data, n_channels=2, n_times=3 Range : 0 ... 2 = 0.000 ... 2.000 secs Ready. ... MNE_FORCE_SERIAL set. Processing in forced serial mode. ... MNE_FORCE_SERIAL set. Processing in forced serial mode. exec PASSED mne/io/tests/test_apply_function.py::test_apply_function_ch_access Creating RawArray with float64 data, n_channels=2, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. apply_function requested to access ch_idx apply_function requested to access ch_idx ... MNE_FORCE_SERIAL set. Processing in forced serial mode. apply_function requested to access ch_name apply_function requested to access ch_name ... MNE_FORCE_SERIAL set. Processing in forced serial mode. PASSED mne/io/tests/test_raw.py::test_orig_units PASSED mne/io/tests/test_raw.py::test_time_as_index Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/io/tests/test_raw.py::test_crop_by_annotations[0-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=376, n_times=14400 Range : 0 ... 14399 = 0.000 ... 23.974 secs Ready. PASSED mne/io/tests/test_raw.py::test_crop_by_annotations[0-orig] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=376, n_times=14400 Range : 0 ... 14399 = 0.000 ... 23.974 secs Ready. PASSED mne/io/tests/test_raw.py::test_crop_by_annotations[10000-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=376, n_times=14400 Range : 10000 ... 24399 = 16.650 ... 40.623 secs Ready. PASSED mne/io/tests/test_raw.py::test_crop_by_annotations[10000-orig] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=376, n_times=14400 Range : 10000 ... 24399 = 16.650 ... 40.623 secs Ready. PASSED mne/io/tests/test_raw.py::test_time_as_index_ref[times in s. relative to first_samp (default)] Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. PASSED mne/io/tests/test_raw.py::test_time_as_index_ref[times in s. relative to first_samp] Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. PASSED mne/io/tests/test_raw.py::test_time_as_index_ref[times in s. relative to meas_date] Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. PASSED mne/io/tests/test_raw.py::test_time_as_index_ref[absolute times in s. relative to 0] Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. PASSED mne/io/tests/test_raw.py::test_meas_date_orig_time Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. PASSED mne/io/tests/test_raw.py::test_get_data_reject Creating RawArray with float64 data, n_channels=3, n_times=2560 Range : 0 ... 2559 = 0.000 ... 9.996 secs Ready. PASSED mne/io/tests/test_raw.py::test_5839 Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. PASSED mne/io/tests/test_raw.py::test_repr Creating RawArray with float64 data, n_channels=3, n_times=2560 Range : 0 ... 2559 = 0.000 ... 9.996 secs Ready. PASSED mne/io/tests/test_raw.py::test_test_raw_reader Reading 0 ... 999 = 0.000 ... 0.999 secs... Reading 0 ... 999 = 0.000 ... 0.999 secs... 1 projection items deactivated Reading 0 ... 999 = 0.000 ... 0.999 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 999 = 0.000 ... 0.999 secs... Reading 0 ... 999 = 0.000 ... 0.999 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 999 = 0.000 ... 0.999 secs... Reading 0 ... 999 = 0.000 ... 0.999 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 999 = 0.000 ... 0.999 secs... Writing /tmp/tmp_mne_tempdir_3q0rfg7q/test_raw.fif Closing /tmp/tmp_mne_tempdir_3q0rfg7q/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_3q0rfg7q/test_raw.fif... Isotrak not found Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Reading 0 ... 999 = 0.000 ... 0.999 secs... <_RawArange | 8 x 1000 (1.0 s), ~69 kB, data loaded> <_RawArange | 8 x 1000 (1.0 s), ~6 kB, data not loaded> Reading 0 ... 999 = 0.000 ... 0.999 secs... Reading 0 ... 999 = 0.000 ... 0.999 secs... PASSED mne/io/tests/test_raw.py::test_describe_print Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/io/tests/test_raw.py::test_describe_df SKIPPED (could not import...) mne/io/tests/test_raw.py::test_get_data_units Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/io/tests/test_raw.py::test_repr_dig_point PASSED mne/io/tests/test_raw.py::test_get_data_tmin_tmax Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/io/tests/test_raw.py::test_resamp_noop Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Sampling frequency of the instance is already 600.614990234375, returning unmodified. PASSED mne/io/tests/test_raw.py::test_concatenate_raw_dev_head_t Creating RawArray with float64 data, n_channels=3, n_times=10 Range : 0 ... 9 = 0.000 ... 0.009 secs Ready. PASSED mne/io/tests/test_raw.py::test_last_samp Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 60 = 0.000 ... 0.100 secs... PASSED mne/io/tests/test_read_raw.py::test_read_raw_unsupported_single[x.xxx] PASSED mne/io/tests/test_read_raw.py::test_read_raw_unsupported_single[x] PASSED mne/io/tests/test_read_raw.py::test_read_raw_unsupported_multi[x.bin] Reading header... PASSED mne/io/tests/test_read_raw.py::test_read_raw_suggested[x.vmrk] PASSED mne/io/tests/test_read_raw.py::test_read_raw_suggested[y.amrk] PASSED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif.gz... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif.gz... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname2] Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Extracting EDF parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/edf/tests/data/test.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 3071 = 0.000 ... 5.998 secs... PASSED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname3] SKIPPED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname4] Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Reading 0 ... 7899 = 0.000 ... 7.899 secs... PASSED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname5] Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Reading 0 ... 1999 = 0.000 ... 1.999 secs... Ready. PASSED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname6] SKIPPED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname7] SKIPPED mne/io/tests/test_read_raw.py::test_read_raw_supported[fname8] SKIPPED mne/io/tests/test_read_raw.py::test_split_name_ext PASSED mne/io/tests/test_read_raw.py::test_read_raw_multiple_dots Extracting EDF parameters from /tmp/pytest-of-pbuilder1/pytest-0/test_read_raw_multiple_dots0/test.this.file.edf... EDF file detected Setting channel info structure... Creating raw.info structure... PASSED mne/minimum_norm/tests/test_inverse.py::test_io_inverse_operator SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_cov[normal-MNE] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_cov[normal-dSPM] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_cov[normal-sLORETA] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_cov[None-MNE] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_cov[None-dSPM] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_cov[None-sLORETA] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_mne_inverse_raw SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_mne_inverse_fixed_raw SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_mne_inverse_epochs SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_tfr[True] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_apply_inverse_tfr[False] SKIPPED mne/minimum_norm/tests/test_inverse.py::test_inverse_ctf_comp SKIPPED mne/minimum_norm/tests/test_inverse.py::test_sss_rank SKIPPED (Requi...) mne/minimum_norm/tests/test_resolution_matrix.py::test_resolution_matrix_fixed SKIPPED mne/minimum_norm/tests/test_resolution_metrics.py::test_resolution_metrics_surface SKIPPED mne/minimum_norm/tests/test_snr.py::test_snr SKIPPED (Requires MNE-C) mne/minimum_norm/tests/test_time_frequency.py::test_tfr_with_inverse_operator[MNE] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_tfr_with_inverse_operator[dSPM] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_tfr_with_inverse_operator[sLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_tfr_with_inverse_operator[eLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_tfr_multi_label SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-None-MNE] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-None-dSPM] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-None-sLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-None-eLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-normal-MNE] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-normal-dSPM] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-normal-sLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[True-normal-eLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-None-MNE] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-None-dSPM] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-None-sLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-None-eLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-normal-MNE] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-normal-dSPM] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-normal-sLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd[False-normal-eLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd_epochs[MNE] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd_epochs[dSPM] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd_epochs[sLORETA] SKIPPED mne/minimum_norm/tests/test_time_frequency.py::test_source_psd_epochs[eLORETA] SKIPPED mne/preprocessing/_peak_finder.py::mne.preprocessing._peak_finder.peak_finder PASSED mne/preprocessing/_regress.py::mne.preprocessing._regress.regress_artifact SKIPPED mne/preprocessing/eyetracking/tests/test_calibration.py::test_create_calibration[0-H3-right-0.5-1.0-positions0-offsets0-gaze0-screen_size0-0.065-screen_resolution0] PASSED mne/preprocessing/eyetracking/tests/test_calibration.py::test_create_calibration[None-None-None-None-None-None-None-None-None-None-None] PASSED mne/preprocessing/eyetracking/tests/test_calibration.py::test_read_calibration[fname0] SKIPPED mne/preprocessing/eyetracking/tests/test_calibration.py::test_plot_calibration[fname0-None] SKIPPED mne/preprocessing/eyetracking/tests/test_calibration.py::test_plot_calibration[fname1-True] SKIPPED mne/preprocessing/eyetracking/tests/test_eyetracking.py::test_set_channel_types_eyetrack Creating RawArray with float64 data, n_channels=3, n_times=100 Range : 0 ... 99 = 0.000 ... 0.990 secs Ready. PASSED mne/preprocessing/eyetracking/tests/test_eyetracking.py::test_convert_units Creating RawArray with float64 data, n_channels=3, n_times=100 Range : 0 ... 99 = 0.000 ... 0.990 secs Ready. Converted ['xpos', 'ypos'] to radians. Converted ['xpos', 'ypos'] to pixels. Converted ['xpos', 'ypos'] to radians. PASSED mne/preprocessing/eyetracking/tests/test_eyetracking.py::test_get_screen_visual_angle PASSED mne/preprocessing/ieeg/tests/test_projection.py::test_project_sensors_onto_brain SKIPPED mne/preprocessing/ieeg/tests/test_projection.py::test_project_sensors_onto_inflated SKIPPED mne/preprocessing/ieeg/tests/test_volume.py::test_warp_montage SKIPPED mne/preprocessing/ieeg/tests/test_volume.py::test_make_montage_volume SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert[nirx-fname0] SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert[nirx-fname1] SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert[nirx-fname2] SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert[fif-fname0] SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert[fif-fname1] SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert[fif-fname2] SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert_unordered_errors SKIPPED mne/preprocessing/nirs/tests/test_beer_lambert_law.py::test_beer_lambert_v_matlab SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_picks SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_check_bads[fname0] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_check_bads[fname1] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_check_bads[fname2] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_spread_bads[fname0] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_spread_bads[fname1] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_spread_bads[fname2] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_channel_naming_and_order_readers[fname0] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_channel_naming_and_order_readers[fname1] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_channel_naming_and_order_readers[fname2] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_channel_naming_and_order_custom_raw Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. PASSED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_channel_naming_and_order_custom_optical_density Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. PASSED mne/preprocessing/nirs/tests/test_nirs.py::test_fnirs_channel_naming_and_order_custom_chroma Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Creating RawArray with float64 data, n_channels=6, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. PASSED mne/preprocessing/nirs/tests/test_nirs.py::test_optode_names PASSED mne/preprocessing/nirs/tests/test_nirs.py::test_optode_loc SKIPPED (...) mne/preprocessing/nirs/tests/test_nirs.py::test_order_agnostic[nirx_snirf0] SKIPPED mne/preprocessing/nirs/tests/test_nirs.py::test_order_agnostic[nirx_snirf1] SKIPPED mne/preprocessing/nirs/tests/test_optical_density.py::test_optical_density SKIPPED mne/preprocessing/nirs/tests/test_optical_density.py::test_optical_density_zeromean SKIPPED mne/preprocessing/nirs/tests/test_optical_density.py::test_optical_density_manual SKIPPED mne/preprocessing/nirs/tests/test_scalp_coupling_index.py::test_scalp_coupling_index[nirx-fname0] SKIPPED mne/preprocessing/nirs/tests/test_scalp_coupling_index.py::test_scalp_coupling_index[nirx-fname1] SKIPPED mne/preprocessing/nirs/tests/test_scalp_coupling_index.py::test_scalp_coupling_index[nirx-fname2] SKIPPED mne/preprocessing/nirs/tests/test_scalp_coupling_index.py::test_scalp_coupling_index[fif-fname0] SKIPPED mne/preprocessing/nirs/tests/test_scalp_coupling_index.py::test_scalp_coupling_index[fif-fname1] SKIPPED mne/preprocessing/nirs/tests/test_scalp_coupling_index.py::test_scalp_coupling_index[fif-fname2] SKIPPED mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py::test_temporal_derivative_distribution_repair[fname0] SKIPPED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude[0-None] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude[0-meas_date1] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude[10000-None] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude[10000-meas_date1] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_with_overlap[0-None] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_with_overlap[0-meas_date1] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_with_overlap[10000-None] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_with_overlap[10000-meas_date1] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_multiple_ch_types[0-None] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_multiple_ch_types[0-meas_date1] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_multiple_ch_types[10000-None] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_annotate_amplitude_multiple_ch_types[10000-meas_date1] Creating RawArray with float64 data, n_channels=11, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. Finding segments below or above PTP threshold. PASSED mne/preprocessing/tests/test_annotate_amplitude.py::test_flat_bad_acq_skip SKIPPED mne/preprocessing/tests/test_annotate_amplitude.py::test_invalid_arguments Creating RawArray with float64 data, n_channels=2, n_times=100 Range : 0 ... 99 = 0.000 ... 0.990 secs Ready. PASSED mne/preprocessing/tests/test_annotate_nan.py::test_annotate_nan[None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] PASSED mne/preprocessing/tests/test_annotate_nan.py::test_annotate_nan[orig] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] PASSED mne/preprocessing/tests/test_artifact_detection.py::test_movement_annotation_head_correction[None] SKIPPED mne/preprocessing/tests/test_artifact_detection.py::test_movement_annotation_head_correction[orig] SKIPPED mne/preprocessing/tests/test_artifact_detection.py::test_muscle_annotation[None] SKIPPED mne/preprocessing/tests/test_artifact_detection.py::test_muscle_annotation[orig] SKIPPED mne/preprocessing/tests/test_artifact_detection.py::test_muscle_annotation_without_meeg_data[None] SKIPPED mne/preprocessing/tests/test_artifact_detection.py::test_muscle_annotation_without_meeg_data[orig] SKIPPED mne/preprocessing/tests/test_artifact_detection.py::test_annotate_breaks[None] SKIPPED mne/preprocessing/tests/test_artifact_detection.py::test_annotate_breaks[orig] SKIPPED mne/preprocessing/tests/test_csd.py::test_csd_matlab[testing_data] SKIPPED mne/preprocessing/tests/test_csd.py::test_csd_degenerate[testing_data] SKIPPED mne/preprocessing/tests/test_csd.py::test_csd_fif Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Fitted sphere radius: 91.0 mm Origin head coordinates: -4.1 16.0 51.7 mm Origin device coordinates: 1.4 17.8 -10.3 mm PASSED mne/preprocessing/tests/test_csd.py::test_csd_epochs Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 31 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 006', 'EEG 007', 'EEG 015'] 4 bad epochs dropped Fitted sphere radius: 91.0 mm Origin head coordinates: -4.1 16.0 51.7 mm Origin device coordinates: 1.4 17.8 -10.3 mm Reading /tmp/pytest-of-pbuilder1/pytest-0/test_csd_epochs0/test_csd_epo.fif ... Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 27 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_csd.py::test_compute_bridged_electrodes Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Local minimum 1.647928159978911e-11 found Bridge detected between EEG 001 and EEG 002 PASSED mne/preprocessing/tests/test_css.py::test_cortical_signal_suppression SKIPPED mne/preprocessing/tests/test_ctps.py::test_ctps PASSED mne/preprocessing/tests/test_ecg.py::test_find_ecg Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 11 (average pulse 55.02292621925802 / min.) Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 0 (average pulse 0.0 / min.) Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 11 (average pulse 55.051562123919354 / min.) Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 23 (average pulse 57.54795663052544 / min.) Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 0 (average pulse 0.0 / min.) Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 23 (average pulse 57.56293399009914 / min.) Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 23 (average pulse 57.558936564127606 / min.) Not setting metadata 23 matching events found No baseline correction applied Loading data for 23 events and 601 original time points ... 0 bad epochs dropped Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 23 matching events found No baseline correction applied Using data from preloaded Raw for 23 events and 601 original time points ... 0 bad epochs dropped Using channel MEG 2641 to identify heart beats. Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 32 (average pulse 80.06672226855713 / min.) Not setting metadata 32 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 32 events and 101 original time points ... 2 bad epochs dropped Using channel MEG 2641 to identify heart beats. Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 32 (average pulse 80.06672226855713 / min.) Not setting metadata 32 matching events found No baseline correction applied Using data from preloaded Raw for 32 events and 101 original time points ... 2 bad epochs dropped Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 23 (average pulse 57.54795663052544 / min.) Not setting metadata 23 matching events found No baseline correction applied Using data from preloaded Raw for 23 events and 101 original time points ... 0 bad epochs dropped Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 0 (average pulse 0.0 / min.) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/preprocessing/tests/test_eeglab_infomax.py::test_mne_python_vs_eeglab SKIPPED mne/preprocessing/tests/test_eog.py::test_find_eog Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Using EOG channel: EOG 061 EOG channel index for this subject is: [375] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Using EOG channel: EOG 061 EOG channel index for this subject is: [375] Omitting 1201 of 14400 (8.34%) samples, retaining 13199 (91.66%) samples. Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Using EOG channel: EOG 061 EOG channel index for this subject is: [375] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 5 significant peaks Number of EOG events detected: 5 Using EOG channel: EOG 061 EOG channel index for this subject is: [375] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Using EOG channels: EEG 060, EOG 061 EOG channel index for this subject is: [374 375] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 PASSED mne/preprocessing/tests/test_fine_cal.py::test_fine_cal_io[fname0] SKIPPED mne/preprocessing/tests/test_fine_cal.py::test_fine_cal_io[fname1] SKIPPED mne/preprocessing/tests/test_fine_cal.py::test_fine_cal_io[fname2] SKIPPED mne/preprocessing/tests/test_fine_cal.py::test_compute_fine_cal SKIPPED mne/preprocessing/tests/test_hfc.py::test_correction[1] SKIPPED (Req...) mne/preprocessing/tests/test_hfc.py::test_correction[2] SKIPPED (Req...) mne/preprocessing/tests/test_hfc.py::test_correction[3] SKIPPED (Req...) mne/preprocessing/tests/test_hfc.py::test_l1_basis_orientations SKIPPED mne/preprocessing/tests/test_hfc.py::test_ref_degenerate Read 5 compensation matrices Removing 5 compensators from info because not all compensation channels were picked. Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle PASSED mne/preprocessing/tests/test_ica.py::test_ica_full_data_recovery[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 241 original time points ... 0 bad epochs dropped Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 301 of 3305 (9.11%) samples, retaining 3004 (90.89%) samples. Selecting by number: 2 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=5, n_times=3305 Range : 0 ... 3304 = 0.000 ... 5.501 secs Ready. Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 5 PCA components Fitting ICA to data using 5 channels (please be patient, this may take a while) Selecting by number: 2 components Fitting ICA took 0.0s. Creating RawArray with float64 data, n_channels=5, n_times=241 Range : 0 ... 240 = 0.000 ... 0.400 secs Ready. Applying ICA to Epochs instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 5 PCA components Applying ICA to Evoked instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 5 PCA components Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 301 of 3305 (9.11%) samples, retaining 3004 (90.89%) samples. Selecting by number: 2 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=5, n_times=3305 Range : 0 ... 3304 = 0.000 ... 5.501 secs Ready. Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 2 PCA components Fitting ICA to data using 5 channels (please be patient, this may take a while) Selecting by number: 2 components Fitting ICA took 0.0s. Creating RawArray with float64 data, n_channels=5, n_times=241 Range : 0 ... 240 = 0.000 ... 0.400 secs Ready. Applying ICA to Epochs instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 2 PCA components Applying ICA to Evoked instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 2 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_full_data_recovery[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_simple[fastica] Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Fitting ICA to data using 3 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 2.7e-19 (2.2e-16 eps * 3 dim * 0.0004 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 3 (0 small eigenvalues omitted) Selecting by number: 3 components Fitting ICA took 0.0s. PASSED mne/preprocessing/tests/test_ica.py::test_ica_simple[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_warnings Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 31 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 31 events and 421 original time points ... 29 bad epochs dropped Fitting ICA to data using 366 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 2 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 3.2s. Fitting ICA to data using 366 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 2 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 3.2s. Fitting ICA to data using 366 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 2 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 3.1s. Applying ICA to Epochs instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 366 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[8-None] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 8 components Fitting ICA took 2.5s. Applying ICA to Raw instance Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 8 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[8-0.9999] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[8-8] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 8 components Fitting ICA took 2.5s. Applying ICA to Raw instance Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 8 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[8-9] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[8-10] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[9-None] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_None_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_None_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[9-0.9999] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by explained variance: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_0_9999_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_0_9999_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[9-8] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 8 components Fitting ICA took 2.5s. Applying ICA to Raw instance Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_8_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_8_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[9-9] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_9_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_9_9_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[9-10] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[0.9999-None] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_None_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_None_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[0.9999-0.9999] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by explained variance: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_0_9999_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_0_9999_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[0.9999-8] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 8 components Fitting ICA took 2.5s. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_8_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_8_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[0.9999-9] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_9_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_0_9999_9_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Selected 9 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 9 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[0.9999-10] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[10-None] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_None_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_None_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 10 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[10-0.9999] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by explained variance: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_0_9999_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_0_9999_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 10 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[10-8] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 8 components Fitting ICA took 2.5s. Applying ICA to Raw instance Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_8_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_8_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (8 components) Zeroing out 0 ICA components Projecting back using 10 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[10-9] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 9 components Fitting ICA took 2.8s. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_9_0/temp-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_noop_10_9_0/temp-ica.fif ... Isotrak not found Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (9 components) Zeroing out 0 ICA components Projecting back using 10 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_noop[10-10] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 10 components Fitting ICA took 3.1s. Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 10 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_max_iter_[fastica-1000] PASSED mne/preprocessing/tests/test_ica.py::test_ica_max_iter_[infomax-500] PASSED mne/preprocessing/tests/test_ica.py::test_ica_max_iter_[picard-500] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_n_iter_[infomax] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 366 channels (please be patient, this may take a while) Selecting by number: 3 components Computing Infomax ICA Fitting ICA took 5.3s. Creating RawArray with float64 data, n_channels=366, n_times=3305 Range : 0 ... 3304 = 0.000 ... 5.501 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_n_iter__infomax_0/test_ica-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_n_iter__infomax_0/test_ica-ica.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_ica_n_iter_[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 366 channels (please be patient, this may take a while) Selecting by number: 3 components Fitting ICA took 5.3s. Creating RawArray with float64 data, n_channels=366, n_times=3305 Range : 0 ... 3304 = 0.000 ... 5.501 secs Ready. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_n_iter__fastica_0/test_ica-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_n_iter__fastica_0/test_ica-ica.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_ica_n_iter_[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_rank_reduction[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 5 components Fitting ICA took 0.1s. Computing rank from data with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 10 dim * 66 max singular value) Estimated rank (mag + grad): 10 MEG: rank 10 computed from 10 data channels with 0 projectors Applying ICA to Raw instance Transforming to ICA space (5 components) Zeroing out 0 ICA components Projecting back using 6 PCA components Computing rank from data with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 10 dim * 66 max singular value) Estimated rank (mag + grad): 7 MEG: rank 7 computed from 10 data channels with 0 projectors Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 5 components Fitting ICA took 0.1s. Computing rank from data with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 10 dim * 66 max singular value) Estimated rank (mag + grad): 10 MEG: rank 10 computed from 10 data channels with 0 projectors Applying ICA to Raw instance Transforming to ICA space (5 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Computing rank from data with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 10 dim * 66 max singular value) Estimated rank (mag + grad): 10 MEG: rank 10 computed from 10 data channels with 0 projectors PASSED mne/preprocessing/tests/test_ica.py::test_ica_rank_reduction[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-False-True-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-False-True-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-False-False-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-False-False-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-True-True-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-True-True-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-True-False-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks0-True-False-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-False-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 21 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-False-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 21 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Selected 18 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 18 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-False-False-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 21 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.1s. Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-False-False-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 21 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.1s. Applying ICA to Raw instance Selected 21 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-True-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 21 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 1.9e-18 (2.2e-16 eps * 21 dim * 0.0004 max singular value) Estimated rank (mag): 18 MAG: rank 18 computed from 21 data channels with 3 projectors Setting small MAG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 18 (3 small eigenvalues omitted) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-True-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 21 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 1.9e-18 (2.2e-16 eps * 21 dim * 0.0004 max singular value) Estimated rank (mag): 18 MAG: rank 18 computed from 21 data channels with 3 projectors Setting small MAG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 18 (3 small eigenvalues omitted) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Selected 18 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 18 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 21 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-True-False-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks1-True-False-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-False-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 62 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.4s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-False-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 62 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.4s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Selected 59 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 59 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-False-False-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 62 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.3s. Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-False-False-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 62 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.2s. Applying ICA to Raw instance Selected 62 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-True-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 62 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 3.4e-17 (2.2e-16 eps * 62 dim * 0.0025 max singular value) Estimated rank (mag + grad): 59 MEG: rank 59 computed from 62 data channels with 3 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 59 (3 small eigenvalues omitted) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.4s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-True-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Fitting ICA to data using 62 channels (please be patient, this may take a while) Applying projection operator with 3 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 3.4e-17 (2.2e-16 eps * 62 dim * 0.0025 max singular value) Estimated rank (mag + grad): 59 MEG: rank 59 computed from 62 data channels with 3 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 59 (3 small eigenvalues omitted) Applying projection operator with 3 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 3 vectors (pre-whitener application) Fitting ICA took 0.4s. Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Selected 59 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 59 PCA components Applying ICA to Raw instance Applying projection operator with 3 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 62 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-True-False-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks2-True-False-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-False-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Fitting ICA to data using 12 channels (please be patient, this may take a while) Applying projection operator with 1 vector (pre-whitener computation) Applying projection operator with 1 vector (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 1 vector (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-False-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Fitting ICA to data using 12 channels (please be patient, this may take a while) Applying projection operator with 1 vector (pre-whitener computation) Applying projection operator with 1 vector (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 1 vector (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Selected 11 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 11 PCA components Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-False-False-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 12 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.1s. Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-False-False-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 12 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.1s. Applying ICA to Raw instance Selected 12 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-True-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Fitting ICA to data using 12 channels (please be patient, this may take a while) Applying projection operator with 1 vector (pre-whitener computation) Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 1.1e-18 (2.2e-16 eps * 12 dim * 0.0004 max singular value) Estimated rank (eeg): 11 EEG: rank 11 computed from 12 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 11 (1 small eigenvalues omitted) Applying projection operator with 1 vector (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 1 vector (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-True-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Fitting ICA to data using 12 channels (please be patient, this may take a while) Applying projection operator with 1 vector (pre-whitener computation) Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 1.1e-18 (2.2e-16 eps * 12 dim * 0.0004 max singular value) Estimated rank (eeg): 11 EEG: rank 11 computed from 12 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 11 (1 small eigenvalues omitted) Applying projection operator with 1 vector (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 1 vector (pre-whitener application) Fitting ICA took 0.1s. Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Selected 11 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 11 PCA components Applying ICA to Raw instance Applying projection operator with 1 vector (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 12 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-True-False-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks3-True-False-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-False-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 33 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.2s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-False-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 33 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.2s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Selected 29 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 29 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-False-False-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 33 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.1s. Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-False-False-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 33 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.1s. Applying ICA to Raw instance Selected 33 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-True-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 33 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.9e-18 (2.2e-16 eps * 21 dim * 0.0004 max singular value) Estimated rank (mag): 18 MAG: rank 18 computed from 21 data channels with 3 projectors Using tolerance 1.1e-18 (2.2e-16 eps * 12 dim * 0.0004 max singular value) Estimated rank (eeg): 11 EEG: rank 11 computed from 12 data channels with 1 projector Setting small MAG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 29 (4 small eigenvalues omitted) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.2s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-True-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 33 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.9e-18 (2.2e-16 eps * 21 dim * 0.0004 max singular value) Estimated rank (mag): 18 MAG: rank 18 computed from 21 data channels with 3 projectors Using tolerance 1.1e-18 (2.2e-16 eps * 12 dim * 0.0004 max singular value) Estimated rank (eeg): 11 EEG: rank 11 computed from 12 data channels with 1 projector Setting small MAG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 29 (4 small eigenvalues omitted) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.2s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Selected 29 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 29 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 33 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-True-False-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks4-True-False-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-False-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 74 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.5s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-False-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 74 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.5s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Selected 70 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 70 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-False-False-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 74 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.3s. Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-False-False-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 74 channels (please be patient, this may take a while) Selecting by number: 10 components Computing Infomax ICA Fitting ICA took 0.3s. Applying ICA to Raw instance Selected 74 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components Applying ICA to Raw instance Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-True-True-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 74 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 3.4e-17 (2.2e-16 eps * 62 dim * 0.0025 max singular value) Estimated rank (mag + grad): 59 MEG: rank 59 computed from 62 data channels with 3 projectors Using tolerance 1.1e-18 (2.2e-16 eps * 12 dim * 0.0004 max singular value) Estimated rank (eeg): 11 EEG: rank 11 computed from 12 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 70 (4 small eigenvalues omitted) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.6s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-True-True-0.999999] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 3304 = 0.000 ... 5.501 secs... Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Fitting ICA to data using 74 channels (please be patient, this may take a while) Applying projection operator with 4 vectors (pre-whitener computation) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 3.4e-17 (2.2e-16 eps * 62 dim * 0.0025 max singular value) Estimated rank (mag + grad): 59 MEG: rank 59 computed from 62 data channels with 3 projectors Using tolerance 1.1e-18 (2.2e-16 eps * 12 dim * 0.0004 max singular value) Estimated rank (eeg): 11 EEG: rank 11 computed from 12 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 70 (4 small eigenvalues omitted) Applying projection operator with 4 vectors (pre-whitener application) Selecting by number: 10 components Computing Infomax ICA Applying projection operator with 4 vectors (pre-whitener application) Fitting ICA took 0.5s. Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Selected 70 PCA components by explained variance (100.0≥99.9999%) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 70 PCA components Applying ICA to Raw instance Applying projection operator with 4 vectors (pre-whitener application) Transforming to ICA space (10 components) Zeroing out 0 ICA components Projecting back using 74 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-True-False-None] PASSED mne/preprocessing/tests/test_ica.py::test_ica_projs[picks5-True-False-0.999999] PASSED mne/preprocessing/tests/test_ica.py::test_ica_reset[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 3 components Fitting ICA took 0.1s. PASSED mne/preprocessing/tests/test_ica.py::test_ica_reset[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-False-2-fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3604 = 0.000 ... 6.001 secs... Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 241 original time points ... 0 bad epochs dropped Fitting ICA to data using 20 channels (please be patient, this may take a while) Fitting ICA to data using 20 channels (please be patient, this may take a while) Selecting by number: 2 components Fitting ICA took 0.1s. Fitting ICA to data using 20 channels (please be patient, this may take a while) Selecting by number: 2 components Fitting ICA took 0.1s. Applying ICA to Raw instance Fitting ICA to data using 20 channels (please be patient, this may take a while) Selecting by number: 2 components Fitting ICA took 0.0s. Creating RawArray with float64 data, n_channels=20, n_times=241 Range : 0 ... 240 = 0.000 ... 0.400 secs Ready. Applying ICA to Epochs instance Applying ICA to Epochs instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 20 PCA components Fitting ICA to data using 20 channels (please be patient, this may take a while) Fitting ICA to data using 2 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 1 components Fitting ICA to data using 2 channels (please be patient, this may take a while) PASSED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-False-2-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-False-0.6-fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3604 = 0.000 ... 6.001 secs... Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 241 original time points ... 0 bad epochs dropped Fitting ICA to data using 20 channels (please be patient, this may take a while) Fitting ICA to data using 20 channels (please be patient, this may take a while) Selecting by explained variance: 6 components Fitting ICA took 0.1s. Fitting ICA to data using 20 channels (please be patient, this may take a while) Selecting by explained variance: 6 components Fitting ICA took 0.1s. Applying ICA to Raw instance Fitting ICA to data using 20 channels (please be patient, this may take a while) Selecting by explained variance: 4 components Fitting ICA took 0.2s. Creating RawArray with float64 data, n_channels=20, n_times=241 Range : 0 ... 240 = 0.000 ... 0.400 secs Ready. Applying ICA to Epochs instance Applying ICA to Epochs instance Transforming to ICA space (4 components) Zeroing out 0 ICA components Projecting back using 20 PCA components Fitting ICA to data using 20 channels (please be patient, this may take a while) Fitting ICA to data using 2 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 1 components Fitting ICA to data using 2 channels (please be patient, this may take a while) PASSED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-False-0.6-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-True-2-fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3604 = 0.000 ... 6.001 secs... 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 241 original time points ... 0 bad epochs dropped Fitting ICA to data using 20 channels (please be patient, this may take a while) Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Selecting by number: 2 components Fitting ICA took 0.1s. Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Selecting by number: 2 components Fitting ICA took 0.1s. Applying ICA to Raw instance Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Selecting by number: 2 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=20, n_times=241 Range : 0 ... 240 = 0.000 ... 0.400 secs Ready. Applying ICA to Epochs instance Applying ICA to Epochs instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 20 PCA components Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Fitting ICA to data using 2 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 1.6e-16 (2.2e-16 eps * 2 dim * 0.35 max singular value) Estimated rank (grad): 2 GRAD: rank 2 computed from 2 data channels with 0 projectors Setting small GRAD eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 2 (0 small eigenvalues omitted) Selecting by non-zero PCA components: 1 components Fitting ICA to data using 2 channels (please be patient, this may take a while) PASSED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-True-2-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-True-0.6-fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3604 = 0.000 ... 6.001 secs... 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 241 original time points ... 0 bad epochs dropped Fitting ICA to data using 20 channels (please be patient, this may take a while) Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Selecting by explained variance: 2 components Fitting ICA took 0.1s. Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Selecting by explained variance: 2 components Fitting ICA took 0.1s. Applying ICA to Raw instance Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Selecting by explained variance: 2 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=20, n_times=241 Range : 0 ... 240 = 0.000 ... 0.400 secs Ready. Applying ICA to Epochs instance Applying ICA to Epochs instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 20 PCA components Fitting ICA to data using 20 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 20 MEG: rank 20 computed from 20 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 20 (0 small eigenvalues omitted) Fitting ICA to data using 2 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 1.6e-16 (2.2e-16 eps * 2 dim * 0.35 max singular value) Estimated rank (grad): 2 GRAD: rank 2 computed from 2 data channels with 0 projectors Setting small GRAD eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 2 (0 small eigenvalues omitted) Selecting by non-zero PCA components: 1 components Fitting ICA to data using 2 channels (please be patient, this may take a while) PASSED mne/preprocessing/tests/test_ica.py::test_ica_core[matplotlib-20-True-0.6-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-False-2-fastica] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-False-2-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-False-0.6-fastica] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-False-0.6-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-True-2-fastica] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-True-2-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-True-0.6-fastica] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_core[qt-20-True-0.6-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_additional[picard] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) Using data from preloaded Raw for 5 events and 41 original time points ... 2 bad epochs dropped Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 41 original time points ... 0 bad epochs dropped SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_additional[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) Using data from preloaded Raw for 5 events and 41 original time points ... 2 bad epochs dropped Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 41 original time points ... 0 bad epochs dropped Fitting ICA to data using 38 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 38 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=38, n_times=123 Range : 0 ... 122 = 0.000 ... 1.220 secs Ready. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Created an SSP operator (subspace dimension = 2) Computing rank from covariance with rank=None Using tolerance 4.5e-16 (2.2e-16 eps * 5 dim * 0.41 max singular value) Estimated rank (mag + grad): 3 MEG: rank 3 computed from 5 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 3 (2 small eigenvalues omitted) Selecting by number: 3 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=5, n_times=500 Range : 0 ... 499 = 0.000 ... 4.990 secs Ready. Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by number: 3 components Fitting ICA took 0.0s. Creating RawArray with float64 data, n_channels=5, n_times=500 Range : 0 ... 499 = 0.000 ... 4.990 secs Ready. Reconstructing ECG signal from Magnetometers Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 3 (average pulse 40.0 / min.) Not setting metadata 3 matching events found No baseline correction applied Using data from preloaded Raw for 3 events and 101 original time points ... 0 bad epochs dropped Reconstructing ECG signal from Magnetometers Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Using threshold: 0.50 for CTPS ECG detection Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 3 (average pulse 40.0 / min.) Not setting metadata 3 matching events found No baseline correction applied Using data from preloaded Raw for 3 events and 101 original time points ... 0 bad epochs dropped Using threshold: 0.50 for CTPS ECG detection Using channel MEG 0121 to identify heart beats. Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 6 (average pulse 80.0 / min.) Not setting metadata 6 matching events found No baseline correction applied Using data from preloaded Raw for 6 events and 101 original time points ... 2 bad epochs dropped Median correlation with constructed map: 1.000 Displaying selected ICs per subject. At least 1 IC detected for each subject. Median correlation with constructed map: 1.000 Displaying selected ICs per subject. At least 1 IC detected for each subject. Median correlation with constructed map: 1.000 At least 1 IC detected for each subject. Fitting ICA to data using 4 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by number: 2 components Fitting ICA took 0.0s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_additional_fastica_0/test-bad-name.fif.gz... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_additional_fastica_0/test-bad-name.fif.gz ... Now restoring ICA solution ... Ready. Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by number: 3 components Fitting ICA took 0.0s. Fitting ICA to data using 39 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by number: 4 components Fitting ICA took 0.1s. Filtering raw data in 1 contiguous segment Setting up band-pass filter from 4 - 20 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Lower passband edge: 4.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 3.00 Hz) - Upper passband edge: 20.00 Hz - Upper transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 22.50 Hz) - Filter length: 331 samples (3.310 s) Filtering raw data in 1 contiguous segment Setting up band-stop filter from 5 - 15 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.97 - Lower transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 2.47 Hz) - Upper passband edge: 15.03 Hz - Upper transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 17.52 Hz) - Filter length: 67 samples (0.670 s) Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_additional_fastica_0/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_additional_fastica_0/test-ica.fif ... Now restoring ICA solution ... Ready. Overwriting existing file. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_additional_fastica_0/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_additional_fastica_0/test-ica.fif ... Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (4 components) Zeroing out 1 ICA component Projecting back using 4 PCA components Applying ICA to Raw instance Transforming to ICA space (4 components) Zeroing out 1 ICA component Projecting back using 4 PCA components Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by number: 2 components Fitting ICA took 0.0s. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Reconstructing ECG signal from Magnetometers Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Using threshold: 0.50 for CTPS ECG detection Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 3 (average pulse 40.0 / min.) Not setting metadata 3 matching events found No baseline correction applied Using data from preloaded Raw for 3 events and 101 original time points ... 0 bad epochs dropped Reconstructing ECG signal from Magnetometers Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. ... filtering ICA sources Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) ... filtering target Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Using EOG channel: EOG 061 Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. ... filtering ICA sources Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) ... filtering target Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) Reconstructing ECG signal from Magnetometers Reconstructing ECG signal from Magnetometers Using threshold: 0.50 for CTPS ECG detection Reconstructing ECG signal from Magnetometers Using threshold: 0.50 for CTPS ECG detection Using EOG channel: EOG 061 Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. ... filtering ICA sources Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) ... filtering target Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) Using EOG channels: EEG 056, EOG 061 Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. ... filtering ICA sources Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) ... filtering target Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. ... filtering ICA sources Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) ... filtering target Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) Using EOG channel: MEG 1441 Reconstructing ECG signal from Magnetometers Reconstructing ECG signal from Magnetometers Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Using ICA source to identify heart beats Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (10.000 s) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Filtering the data to remove DC offset to help distinguish blinks from saccades Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (10.000 s) Now detecting blinks and generating corresponding events Found 21 significant peaks Number of EOG events detected: 21 Writing /build/reproducible-path/python-mne-1.8.0/test-ica_raw.fif Closing /build/reproducible-path/python-mne-1.8.0/test-ica_raw.fif [done] Opening raw data file /build/reproducible-path/python-mne-1.8.0/test-ica_raw.fif... Range : 4396 ... 4495 = 43.960 ... 44.950 secs Ready. Reading 0 ... 99 = 0.000 ... 0.990 secs... Selected 2 PCA components by explained variance (40.0≥30.0%) Selected 5 PCA components by explained variance (100.0≥90.0%) Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by non-zero PCA components: 5 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=5, n_times=500 Range : 0 ... 499 = 0.000 ... 4.990 secs Ready. Reconstructing ECG signal from Magnetometers Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Using threshold: 0.50 for CTPS ECG detection Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 3 (average pulse 40.0 / min.) Not setting metadata 3 matching events found No baseline correction applied Using data from preloaded Raw for 3 events and 101 original time points ... 0 bad epochs dropped Using EOG channel: MEG 0121 Using EOG channels: EEG 056, EOG 061 Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Reconstructing ECG signal from Magnetometers Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Using threshold: 0.50 for CTPS ECG detection Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 1000 samples (10.000 s) Number of ECG events detected : 3 (average pulse 40.0 / min.) Not setting metadata 3 matching events found No baseline correction applied Using data from preloaded Raw for 3 events and 101 original time points ... 0 bad epochs dropped Fitting ICA to data using 5 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by explained variance: 4 components Fitting ICA took 0.0s. PASSED mne/preprocessing/tests/test_ica.py::test_get_explained_variance_ratio Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) Using data from preloaded Raw for 5 events and 41 original time points ... 2 bad epochs dropped Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 41 original time points ... 0 bad epochs dropped Fitting ICA to data using 38 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 38 components Fitting ICA took 0.1s. PASSED mne/preprocessing/tests/test_ica.py::test_ica_cov[picard-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) Using data from preloaded Raw for 5 events and 41 original time points ... 2 bad epochs dropped Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 41 original time points ... 0 bad epochs dropped SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_cov[picard-cov1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) Using data from preloaded Raw for 5 events and 41 original time points ... 2 bad epochs dropped Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 41 original time points ... 0 bad epochs dropped SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_cov[fastica-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) Using data from preloaded Raw for 5 events and 41 original time points ... 2 bad epochs dropped Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 41 original time points ... 0 bad epochs dropped Fitting ICA to data using 10 channels (please be patient, this may take a while) Omitting 50 of 500 (10.00%) samples, retaining 450 (90.00%) samples. Selecting by number: 2 components Fitting ICA took 0.1s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_cov_fastica_None_0/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_cov_fastica_None_0/test-ica.fif ... Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 4 PCA components Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 1 ICA component Projecting back using 4 PCA components Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 2 ICA components Projecting back using 4 PCA components Overwriting existing file. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_cov_fastica_None_0/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_cov_fastica_None_0/test-ica.fif ... Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 1 ICA component Projecting back using 4 PCA components Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 1 ICA component Projecting back using 4 PCA components Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 2 ICA components Projecting back using 4 PCA components Overwriting existing file. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_ica_cov_fastica_None_0/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_ica_cov_fastica_None_0/test-ica.fif ... Now restoring ICA solution ... Ready. Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 1 ICA component Projecting back using 4 PCA components Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 1 ICA component Projecting back using 4 PCA components Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 2 ICA components Projecting back using 4 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_reject_buffer[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 2703 = 0.000 ... 4.500 secs... PASSED mne/preprocessing/tests/test_ica.py::test_ica_reject_buffer[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_twice[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 2703 = 0.000 ... 4.500 secs... Fitting ICA to data using 21 channels (please be patient, this may take a while) Selecting by explained variance: 11 components Fitting ICA took 1.5s. Applying ICA to Raw instance Selected 21 PCA components by explained variance (100.0≥99.99%) Transforming to ICA space (11 components) Zeroing out 0 ICA components Projecting back using 21 PCA components Fitting ICA to data using 21 channels (please be patient, this may take a while) Selecting by explained variance: 11 components Fitting ICA took 0.6s. PASSED mne/preprocessing/tests/test_ica.py::test_ica_twice[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_fit_methods[fastica] PASSED mne/preprocessing/tests/test_ica.py::test_fit_methods[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_fit_methods[infomax] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3304 = 0.000 ... 5.501 secs... Fitting ICA to data using 366 channels (please be patient, this may take a while) Selecting by number: 3 components Computing Extended Infomax ICA Fitting ICA took 5.7s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_fit_methods_infomax_0/test_ica-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_fit_methods_infomax_0/test_ica-ica.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[start-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Computing Infomax ICA Loading data for 30 events and 421 original time points ... Fitting ICA took 1.0s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_0/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_0/test-ica.fif ... Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[stop-500] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Computing Infomax ICA Loading data for 30 events and 421 original time points ... Fitting ICA took 1.0s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_1/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_1/test-ica.fif ... Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[reject-param_val2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Computing Infomax ICA Loading data for 30 events and 421 original time points ... Fitting ICA took 1.0s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_2/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_2/test-ica.fif ... Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[flat-param_val3] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Computing Infomax ICA Loading data for 30 events and 421 original time points ... Fitting ICA took 0.9s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_3/test-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_fit_params_epochs_vs_raw_3/test-ica.fif ... Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_bad_channels[True-fastica] Creating RawArray with float64 data, n_channels=31, n_times=50 Range : 0 ... 49 = 0.000 ... 0.098 secs Ready. Not setting metadata 100 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_ica.py::test_bad_channels[True-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_bad_channels[False-fastica] Creating RawArray with float64 data, n_channels=31, n_times=50 Range : 0 ... 49 = 0.000 ... 0.098 secs Ready. Not setting metadata 100 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_ica.py::test_bad_channels[False-picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_eog_channel[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 7 events and 241 original time points ... 0 bad epochs dropped Fitting ICA to data using 5 channels (please be patient, this may take a while) Selecting by explained variance: 4 components Fitting ICA took 0.2s. Creating RawArray with float64 data, n_channels=5, n_times=14400 Range : 0 ... 14399 = 0.000 ... 23.974 secs Ready. Fitting ICA to data using 5 channels (please be patient, this may take a while) Selecting by explained variance: 4 components Fitting ICA took 0.1s. Creating RawArray with float64 data, n_channels=5, n_times=1687 Range : 0 ... 1686 = 0.000 ... 2.807 secs Ready. Fitting ICA to data using 5 channels (please be patient, this may take a while) Selecting by explained variance: 4 components Fitting ICA took 0.2s. Fitting ICA to data using 5 channels (please be patient, this may take a while) Selecting by explained variance: 4 components Fitting ICA took 0.1s. PASSED mne/preprocessing/tests/test_ica.py::test_eog_channel[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_n_components_none[fastica] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 2703 = 0.000 ... 4.500 secs... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 7 events and 241 original time points ... 6 bad epochs dropped Fitting ICA to data using 12 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 12 components Fitting ICA took 1.9s. Writing ICA solution to /tmp/pytest-of-pbuilder1/pytest-0/test_n_components_none_fastica0/test_ica-ica.fif... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_n_components_none_fastica0/test_ica-ica.fif ... Now restoring ICA solution ... Ready. PASSED mne/preprocessing/tests/test_ica.py::test_n_components_none[picard] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_ctf SKIPPED (Requires ...) mne/preprocessing/tests/test_ica.py::test_ica_labels SKIPPED (Requir...) mne/preprocessing/tests/test_ica.py::test_ica_eeg[fname0-None] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_eeg[fname1-None] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_eeg[fname2-0] SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_eeg[fname3-1] SKIPPED mne/preprocessing/tests/test_ica.py::test_read_ica_eeglab SKIPPED (R...) mne/preprocessing/tests/test_ica.py::test_read_ica_eeglab_mismatch SKIPPED mne/preprocessing/tests/test_ica.py::test_ica_ch_types[dbs] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Not setting metadata 3 matching events found No baseline correction applied Using data from preloaded Raw for 3 events and 201 original time points ... 1 bad epochs dropped Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 2 components Computing Infomax ICA Fitting ICA took 0.0s. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 2 components Computing Infomax ICA Fitting ICA took 0.0s. Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Applying ICA to Epochs instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Applying ICA to Evoked instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 10 PCA components PASSED mne/preprocessing/tests/test_ica.py::test_ica_ch_types[seeg] Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Not setting metadata 3 matching events found No baseline correction applied Using data from preloaded Raw for 3 events and 201 original time points ... 1 bad epochs dropped Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 2 components Computing Infomax ICA Fitting ICA took 0.0s. Fitting ICA to data using 10 channels (please be patient, this may take a while) Selecting by number: 2 components Computing Infomax ICA Fitting ICA took 0.0s. Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Applying ICA to Epochs instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 10 PCA components Applying ICA to Evoked instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 10 PCA components PASSED mne/preprocessing/tests/test_infomax.py::test_infomax_blowup Computing Extended Infomax ICA PASSED mne/preprocessing/tests/test_infomax.py::test_infomax_simple Computing Extended Infomax ICA Computing Infomax ICA Computing Extended Infomax ICA Computing Infomax ICA PASSED mne/preprocessing/tests/test_infomax.py::test_infomax_weights_ini Computing Extended Infomax ICA Computing Infomax ICA PASSED mne/preprocessing/tests/test_infomax.py::test_non_square_infomax Computing Extended Infomax ICA Computing Extended Infomax ICA PASSED mne/preprocessing/tests/test_infomax.py::test_infomax_n_iter[True] Computing Extended Infomax ICA PASSED mne/preprocessing/tests/test_infomax.py::test_infomax_n_iter[False] Computing Extended Infomax ICA PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[raw-0.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 58 sensor positions Interpolating 2 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 58 sensor positions Interpolating 2 sensors PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[raw-0.5] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 57 sensor positions Interpolating 1 sensors PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[raw-1.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[epochs-0.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 13 sensor positions Interpolating 2 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 13 sensor positions Interpolating 2 sensors PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[epochs-0.5] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 12 sensor positions Interpolating 1 sensors PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[epochs-1.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[evoked-0.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 13 sensor positions Interpolating 2 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 58 sensor positions Interpolating 2 sensors PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[evoked-0.5] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 12 sensor positions Interpolating 1 sensors PASSED mne/preprocessing/tests/test_interpolate.py::test_equalize_bads[evoked-1.0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/preprocessing/tests/test_interpolate.py::test_interpolate_bridged_electrodes Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 58 sensor positions Interpolating 2 sensors Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 59 sensor positions Interpolating 2 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 13 sensor positions Interpolating 2 sensors Not setting metadata 7 matching events found No baseline correction applied 0 projection items activated Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 14 sensor positions Interpolating 2 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 13 sensor positions Interpolating 2 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 14 sensor positions Interpolating 2 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 57 sensor positions Interpolating 3 sensors Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 58 sensor positions Interpolating 3 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 12 sensor positions Interpolating 3 sensors Not setting metadata 7 matching events found No baseline correction applied 0 projection items activated Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 13 sensor positions Interpolating 3 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 12 sensor positions Interpolating 3 sensors Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 13 sensor positions Interpolating 3 sensors Creating RawArray with float64 data, n_channels=88, n_times=1024 Range : 0 ... 1023 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1024 Range : 0 ... 1023 = 0.000 ... 0.999 secs Ready. Setting channel interpolation method to {'eeg': 'spline'}. Interpolating bad channels. Automatic origin fit: head of radius 99.1 mm Computing interpolation matrix from 84 sensor positions Interpolating 5 sensors PASSED mne/preprocessing/tests/test_interpolate.py::test_find_centroid PASSED mne/preprocessing/tests/test_lof.py::test_lof[8-eeg-60-8] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... LOF: Detected bad channel(s): ['EEG 001', 'EEG 003', 'EEG 004', 'EEG 009', 'EEG 010', 'EEG 016', 'EEG 019', 'EEG 024'] LOF: Detected bad channel(s): ['EEG 001', 'EEG 003', 'EEG 004', 'EEG 009', 'EEG 010', 'EEG 016', 'EEG 019', 'EEG 024'] PASSED mne/preprocessing/tests/test_lof.py::test_lof[10-grad-204-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... LOF: Detected bad channel(s): ['MEG 1032', 'MEG 2443'] LOF: Detected bad channel(s): ['MEG 1032', 'MEG 2443'] PASSED mne/preprocessing/tests/test_lof.py::test_lof[20-mag-102-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... LOF: Detected bad channel(s): [] LOF: Detected bad channel(s): [] PASSED mne/preprocessing/tests/test_lof.py::test_lof[30-grad-204-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... LOF: Detected bad channel(s): ['MEG 1032', 'MEG 2443'] LOF: Detected bad channel(s): ['MEG 1032', 'MEG 2443'] PASSED mne/preprocessing/tests/test_maxwell.py::test_movement_compensation SKIPPED mne/preprocessing/tests/test_maxwell.py::test_other_systems Extracting SQD Parameters from /build/reproducible-path/python-mne-1.8.0/mne/io/kit/tests/data/test.sqd... Creating Raw.info structure... Setting channel info structure... Creating Info structure... Ready. Maxwell filtering raw data No bad MEG channels Processing 0 gradiometers and 160 magnetometers (of which 157 are actually KIT gradiometers) Automatic origin fit: head of radius 89.2 mm Using origin 155.3, -27.9, 102.2 mm in the head frame Maxwell filtering raw data No bad MEG channels Processing 0 gradiometers and 157 magnetometers (of which 157 are actually KIT gradiometers) Setting mag_scale=100.00 because only one coil type is present Using origin 0.0, 0.0, 40.0 mm in the head frame Using 77/95 harmonic components for 0.000 (65/80 in, 12/15 out) Loading raw data from disk Processing 1 data chunk [done] Reading 4D PDF file /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_pdf_linux... Creating Neuromag info structure ... ... Setting channel info structure. ... putting coil transforms in Neuromag coordinates ... Reading digitization points from /build/reproducible-path/python-mne-1.8.0/mne/io/bti/tests/data/test_hs_linux Currently direct inclusion of 4D weight tables is not supported. For critical use cases please take into account the MNE command "mne_create_comp_data" to include weights as printed out by the 4D "print_table" routine. Maxwell filtering raw data No bad MEG channels Processing 5 gradiometers and 266 magnetometers Automatic origin fit: head of radius 97.4 mm Using origin -5.3, 3.9, 35.1 mm in the head frame Using 85/95 harmonic components for 0.000 (70/80 in, 15/15 out) Loading raw data from disk Processing 1 data chunk [done] Maxwell filtering raw data No bad MEG channels Processing 5 gradiometers and 266 magnetometers Setting mag_scale=7.41 based on gradiometer distance 135.00 mm Automatic origin fit: head of radius 97.4 mm Using origin -5.3, 3.9, 35.1 mm in the head frame Using 82/95 harmonic components for 0.000 (67/80 in, 15/15 out) Loading raw data from disk Processing 1 data chunk [done] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Compensator constructed to change 3 -> 0 Maxwell filtering raw data No bad MEG channels Processing 0 gradiometers and 303 magnetometers (of which 20 are actually KIT gradiometers) Using origin 0.0, 0.0, 40.0 mm in the head frame Accounting for compensation grade 3 Using 85/95 harmonic components for 0.000 (70/80 in, 15/15 out) Loading raw data from disk Processing 1 data chunk [done] Compensator constructed to change 3 -> 0 Applying compensator to loaded data Maxwell filtering raw data No bad MEG channels Processing 0 gradiometers and 303 magnetometers (of which 294 are actually KIT gradiometers) Maxwell filtering raw data No bad MEG channels Processing 0 gradiometers and 303 magnetometers (of which 294 are actually KIT gradiometers) Using origin 0.0, 0.0, 40.0 mm in the head frame Using 83/95 harmonic components for 0.000 (68/80 in, 15/15 out) Loading raw data from disk Processing 1 data chunk [done] Compensator constructed to change 0 -> 3 Applying compensator to loaded data Maxwell filtering raw data Maxwell filtering raw data Removing 5 compensators from info because not all compensation channels were picked. No bad MEG channels Processing 0 gradiometers and 274 magnetometers (of which 274 are actually KIT gradiometers) Setting mag_scale=100.00 because only one coil type is present Using origin 0.0, 0.0, 40.0 mm in the head frame Using 82/95 harmonic components for 0.000 (70/80 in, 12/15 out) Loading raw data from disk Processing 1 data chunk [done] PASSED mne/preprocessing/tests/test_maxwell.py::test_spherical_conversions PASSED mne/preprocessing/tests/test_maxwell.py::test_multipolar_bases SKIPPED mne/preprocessing/tests/test_maxwell.py::test_basic SKIPPED (Require...) mne/preprocessing/tests/test_maxwell.py::test_maxwell_filter_additional SKIPPED mne/preprocessing/tests/test_maxwell.py::test_bads_reconstruction SKIPPED mne/preprocessing/tests/test_maxwell.py::test_spatiotemporal SKIPPED mne/preprocessing/tests/test_maxwell.py::test_spatiotemporal_only SKIPPED mne/preprocessing/tests/test_maxwell.py::test_fine_calibration SKIPPED mne/preprocessing/tests/test_maxwell.py::test_regularization SKIPPED mne/preprocessing/tests/test_maxwell.py::test_cross_talk SKIPPED (Re...) mne/preprocessing/tests/test_maxwell.py::test_head_translation SKIPPED mne/preprocessing/tests/test_maxwell.py::test_esss[bads0-in] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_esss[bads0-None] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_esss[bads1-in] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_esss[bads1-None] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_shielding_factor SKIPPED mne/preprocessing/tests/test_maxwell.py::test_all SKIPPED (Requires ...) mne/preprocessing/tests/test_maxwell.py::test_triux SKIPPED (Require...) mne/preprocessing/tests/test_maxwell.py::test_MGH_cross_talk SKIPPED mne/preprocessing/tests/test_maxwell.py::test_mf_skips SKIPPED (Requ...) mne/preprocessing/tests/test_maxwell.py::test_find_bad_channels_maxwell[fname0-bads0-False-False-False-want_bads0-False-None-raw] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_find_bad_channels_maxwell[fname1-bads1-True-True-False-want_bads1-False-None-raw] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_find_bad_channels_maxwell[fname2-bads2-False-False-False-want_bads2-False-None-raw] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_find_bad_channels_maxwell[fname3-bads3-False-False-True-want_bads3-False-None-raw] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_find_bad_channels_maxwell[fname4-bads4-True-True-False-want_bads4-True-50-raw] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_find_bad_channels_maxwell[fname5-bads5-True-True-False-want_bads5-True-50-None] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_find_bads_maxwell_flat Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Applying low-pass filter with 40.0 Hz cutoff frequency ... Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 199 samples (0.331 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.5s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.9s Scanning for bad channels in 4 intervals (5.0 s) ... No bad MEG channels Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Interval 1: 0.000 - 4.998 Interval 2: 5.000 - 9.998 Interval 3: 10.000 - 14.998 Interval 4: 15.000 - 23.974 Static bad channels: ['MEG 1032', 'MEG 2313', 'MEG 2443'] Static flat channels: [] [done] [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.6s [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.5s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.9s Finding segments below or above PTP threshold. Omitting 6008 of 14400 (41.72%) samples, retaining 8392 (58.28%) samples. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.5s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.9s PASSED mne/preprocessing/tests/test_maxwell.py::test_compute_maxwell_basis[None-80-8] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Computing rank from data with rank=None Using tolerance 3.6e-11 (2.2e-16 eps * 306 dim * 5.2e+02 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Maxwell filtering raw data Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Loading raw data from disk Processing 1 data chunk [done] Computing rank from data with rank=None Using tolerance 9.1e-12 (2.2e-16 eps * 306 dim * 1.3e+02 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing Maxwell basis Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame PASSED mne/preprocessing/tests/test_maxwell.py::test_compute_maxwell_basis[in-71-8] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Computing rank from data with rank=None Using tolerance 3.6e-11 (2.2e-16 eps * 306 dim * 5.2e+02 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Maxwell filtering raw data Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Using 86/95 harmonic components for 0.000 (71/80 in, 15/15 out) Loading raw data from disk Processing 1 data chunk [done] Computing rank from data with rank=None Using tolerance 9.2e-12 (2.2e-16 eps * 306 dim * 1.4e+02 max singular value) Estimated rank (mag + grad): 71 MEG: rank 71 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing Maxwell basis Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Using 86/95 harmonic components for 0.000 (71/80 in, 15/15 out) PASSED mne/preprocessing/tests/test_maxwell.py::test_compute_maxwell_basis[None-0-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Computing rank from data with rank=None Using tolerance 3.6e-11 (2.2e-16 eps * 306 dim * 5.2e+02 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Maxwell filtering raw data Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Loading raw data from disk Processing 1 data chunk [done] Computing rank from data with rank=None Using tolerance 0 (2.2e-16 eps * 306 dim * 0 max singular value) Estimated rank (mag + grad): 0 MEG: rank 0 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing Maxwell basis Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame PASSED mne/preprocessing/tests/test_maxwell.py::test_compute_maxwell_basis[in-0-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Computing rank from data with rank=None Using tolerance 3.6e-11 (2.2e-16 eps * 306 dim * 5.2e+02 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Maxwell filtering raw data Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Using 15/15 harmonic components for 0.000 (0/0 in, 15/15 out) Loading raw data from disk Processing 1 data chunk [done] Computing rank from data with rank=None Using tolerance 0 (2.2e-16 eps * 306 dim * 0 max singular value) Estimated rank (mag + grad): 0 MEG: rank 0 computed from 306 data channels with 0 projectors Using tolerance 2.4e-11 (2.2e-16 eps * 60 dim * 1.8e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing Maxwell basis Bad MEG channels being reconstructed: ['MEG 2443'] Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Using 15/15 harmonic components for 0.000 (0/0 in, 15/15 out) PASSED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_bads[from_raw] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_bads[union] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_bads[keep] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-orig-orig-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-orig-orig-after] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-orig-None-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-orig-None-after] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-None-orig-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-None-orig-after] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-None-None-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[False-None-None-after] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-orig-orig-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-orig-orig-after] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-orig-None-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-orig-None-after] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-None-orig-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-None-orig-after] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-None-None-before] SKIPPED mne/preprocessing/tests/test_maxwell.py::test_prepare_emptyroom_annot_first_samp[True-None-None-after] SKIPPED mne/preprocessing/tests/test_otp.py::test_otp_array Creating RawArray with float64 data, n_channels=10, n_times=2000 Range : 0 ... 1999 = 0.000 ... 1.999 secs Ready. Processing MEG data using oversampled temporal projection Processing 1 data chunk of (at least) 2.0 s with 1.0 s overlap and hann windowing Denoising 0.00 – 2.00 s Creating RawArray with float64 data, n_channels=200, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Processing MEG data using oversampled temporal projection Processing MEG data using oversampled temporal projection Processing MEG data using oversampled temporal projection Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Denoising 0.00 – 1.00 s PASSED mne/preprocessing/tests/test_otp.py::test_otp_real SKIPPED (Requires...) mne/preprocessing/tests/test_peak_finder.py::test_peak_finder Found 4 significant peaks FAILED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-0-0.9] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4500 Range : 222 ... 4721 = 2.220 ... 47.210 secs Ready. 46 events found on stim channel raw_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.1316282072803006e-14 First order coefficient: 1.1111111111111112 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 111111.1 μs/s (total drift over 50.0 s recording: 5554.4 ms) Cropping 0.000 s from the start of raw Resampling other 46 events found on stim channel other_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-0-0.999] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4995 Range : 222 ... 5216 = 2.220 ... 52.160 secs Ready. 46 events found on stim channel raw_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -0.0008661413091388681 First order coefficient: 1.0010123834267886 Linear correlation computed as R=1.000 and p=2.49e-162 Drift rate: 1012.4 μs/s (total drift over 50.0 s recording: 50.6 ms) Cropping 0.001 s from the start of other Resampling other 46 events found on stim channel other_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-0-1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 222 ... 5221 = 2.220 ... 52.210 secs Ready. 46 events found on stim channel raw_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 3.197442310920451e-14 First order coefficient: 0.9999999999999997 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 50.0 s recording: 0.0 ms) Cropping 0.000 s from the start of raw Resampling other Sampling frequency of the instance is already 99.99999999999997, returning unmodified. Correcting annotations in other 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 1.0408340855860843e-17 First order coefficient: 0.9999999999999997 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 50.0 s recording: 0.0 ms) Cropping 0.000 s from the start of raw Resampling other Sampling frequency of the instance is already 99.99999999999997, returning unmodified. Correcting annotations in other Zero order coefficient: -0.04300299571422036 First order coefficient: 0.0011313042083790827 Zero order coefficient: -43.00299571421809 First order coefficient: 2.1313042083790723 Drift rate: 1131304.2 μs/s (total drift over 50.0 s recording: 56553.9 ms) Cropping 20.177 s from the start of other Resampling other Trigger channel other_stim has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 27 events found on stim channel other_stim Event IDs: [1] Trigger channel other_stim has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 27 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 13.560 s from the end of other PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-0-1.001] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5005 Range : 222 ... 5226 = 2.220 ... 52.260 secs Ready. 46 events found on stim channel raw_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 0.0008668187673990246 First order coefficient: 0.9989895632804722 Linear correlation computed as R=1.000 and p=2.28e-162 Drift rate: 1010.4 μs/s (total drift over 50.0 s recording: 50.5 ms) Cropping 0.001 s from the start of raw Resampling other 46 events found on stim channel other_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-0-1.1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5500 Range : 222 ... 5721 = 2.220 ... 57.210 secs Ready. 46 events found on stim channel raw_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.4868995751603507e-14 First order coefficient: 0.9090909090909087 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 50.0 s recording: 4544.5 ms) Cropping 0.000 s from the start of raw Resampling other 46 events found on stim channel other_stim Event IDs: [1] 46 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 46 events found on stim channel other_stim Event IDs: [1] Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated Not setting metadata 46 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-3-0.9] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4230 Range : 222 ... 4451 = 2.220 ... 44.510 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999574 First order coefficient: 1.111111111111112 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 111111.1 μs/s (total drift over 50.0 s recording: 5554.4 ms) Cropping 3.000 s from the start of raw Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-3-0.999] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4696 Range : 222 ... 4917 = 2.220 ... 49.170 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9993330761686465 First order coefficient: 1.0010006454442604 Linear correlation computed as R=1.000 and p=8.31e-151 Drift rate: 1000.6 μs/s (total drift over 50.0 s recording: 50.0 ms) Cropping 2.999 s from the start of raw Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 0.010 s from the end of other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-3-1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 222 ... 4921 = 2.220 ... 49.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999645 First order coefficient: 1.0000000000000002 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 50.0 s recording: 0.0 ms) Cropping 3.000 s from the start of raw Resampling other Sampling frequency of the instance is already 100.00000000000003, returning unmodified. Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-3-1.001] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4705 Range : 222 ... 4926 = 2.220 ... 49.260 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 3.000668016704644 First order coefficient: 0.9990012478097088 Linear correlation computed as R=1.000 and p=7.66e-151 Drift rate: 998.8 μs/s (total drift over 50.0 s recording: 49.9 ms) Cropping 3.001 s from the start of raw Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-0-3-1.1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5170 Range : 222 ... 5391 = 2.220 ... 53.910 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999574 First order coefficient: 0.9090909090909098 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 50.0 s recording: 4544.5 ms) Cropping 3.000 s from the start of raw Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-3-0-0.9] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4500 Range : 222 ... 4721 = 2.220 ... 47.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000000000000046 First order coefficient: 1.1111111111111123 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 111111.1 μs/s (total drift over 47.0 s recording: 5221.1 ms) Cropping 2.700 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-3-0-0.999] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4995 Range : 222 ... 5216 = 2.220 ... 52.160 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000134543588743 First order coefficient: 1.0009900486832441 Linear correlation computed as R=1.000 and p=5.07e-150 Drift rate: 990.0 μs/s (total drift over 47.0 s recording: 46.5 ms) Cropping 2.997 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-3-0-1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 222 ... 5221 = 2.220 ... 52.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000000000000046 First order coefficient: 1.0000000000000009 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 47.0 s recording: 0.0 ms) Cropping 3.000 s from the start of other Resampling other Sampling frequency of the instance is already 100.00000000000009, returning unmodified. Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-3-0-1.001] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5005 Range : 222 ... 5226 = 2.220 ... 52.260 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -2.999862728065658 First order coefficient: 0.9990117927860089 Linear correlation computed as R=1.000 and p=4.68e-150 Drift rate: 988.2 μs/s (total drift over 47.0 s recording: 46.4 ms) Cropping 3.003 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-0-3-0-1.1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5500 Range : 222 ... 5721 = 2.220 ... 57.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000000000000046 First order coefficient: 0.90909090909091 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 47.0 s recording: 4271.8 ms) Cropping 3.300 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-0-0.9] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4230 Range : 222 ... 4451 = 2.220 ... 44.510 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.197442310920451e-14 First order coefficient: 1.1111111111111118 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 111111.1 μs/s (total drift over 50.0 s recording: 5554.4 ms) Cropping 0.000 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-0-0.999] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4696 Range : 222 ... 4917 = 2.220 ... 49.170 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -0.0006669238313428139 First order coefficient: 1.0010006454442604 Linear correlation computed as R=1.000 and p=8.31e-151 Drift rate: 1000.6 μs/s (total drift over 50.0 s recording: 50.0 ms) Cropping 0.001 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 2.990 s from the end of raw 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-0-1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 222 ... 4921 = 2.220 ... 49.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -2.842170943040401e-14 First order coefficient: 1.0000000000000002 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 50.0 s recording: 0.0 ms) Cropping 0.000 s from the start of other Resampling other Sampling frequency of the instance is already 100.00000000000003, returning unmodified. Correcting annotations in other Cropping 3.000 s from the end of raw 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-0-1.001] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4705 Range : 222 ... 4926 = 2.220 ... 49.260 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 0.0006680167046582142 First order coefficient: 0.9990012478097088 Linear correlation computed as R=1.000 and p=7.66e-151 Drift rate: 998.8 μs/s (total drift over 50.0 s recording: 49.9 ms) Cropping 0.001 s from the start of raw Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-0-1.1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5170 Range : 222 ... 5391 = 2.220 ... 53.910 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.197442310920451e-14 First order coefficient: 0.9090909090909096 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 50.0 s recording: 4544.5 ms) Cropping 0.000 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-3-0.9] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=3960 Range : 222 ... 4181 = 2.220 ... 41.810 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999964 First order coefficient: 1.111111111111111 Linear correlation computed as R=1.000 and p=2.58e-293 Drift rate: 111111.1 μs/s (total drift over 50.0 s recording: 5554.4 ms) Cropping 3.000 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-3-0.999] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4396 Range : 222 ... 4617 = 2.220 ... 46.170 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9988951138899296 First order coefficient: 1.0010291394436457 Linear correlation computed as R=1.000 and p=1.16e-138 Drift rate: 1029.1 μs/s (total drift over 50.0 s recording: 51.4 ms) Cropping 2.999 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 2.990 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-3-1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4400 Range : 222 ... 4621 = 2.220 ... 46.210 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999822 First order coefficient: 1.0000000000000002 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 50.0 s recording: 0.0 ms) Cropping 3.000 s from the start of raw Resampling other Sampling frequency of the instance is already 100.00000000000003, returning unmodified. Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-3-1.001] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4405 Range : 222 ... 4626 = 2.220 ... 46.260 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 3.0011052420187845 First order coefficient: 0.9989728524688074 Linear correlation computed as R=1.000 and p=1.07e-138 Drift rate: 1027.1 μs/s (total drift over 50.0 s recording: 51.3 ms) Cropping 3.001 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-0-3-1.1] Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 111 ... 5110 = 1.110 ... 51.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4840 Range : 222 ... 5061 = 2.220 ... 50.610 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999822 First order coefficient: 0.9090909090909093 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 50.0 s recording: 4544.5 ms) Cropping 3.000 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-3-0-0.9] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4230 Range : 222 ... 4451 = 2.220 ... 44.510 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000000000000014 First order coefficient: 1.1111111111111114 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 111111.1 μs/s (total drift over 47.0 s recording: 5221.1 ms) Cropping 2.700 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-3-0-0.999] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4696 Range : 222 ... 4917 = 2.220 ... 49.170 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -2.9998088218422296 First order coefficient: 1.0009727415362895 Linear correlation computed as R=1.000 and p=1.16e-138 Drift rate: 972.7 μs/s (total drift over 47.0 s recording: 45.7 ms) Cropping 2.997 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-3-0-1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 222 ... 4921 = 2.220 ... 49.210 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000000000000014 First order coefficient: 1.0000000000000002 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 47.0 s recording: 0.0 ms) Cropping 3.000 s from the start of other Resampling other Sampling frequency of the instance is already 100.00000000000003, returning unmodified. Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-3-0-1.001] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4705 Range : 222 ... 4926 = 2.220 ... 49.260 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.0001878148651713 First order coefficient: 0.9990290252350824 Linear correlation computed as R=1.000 and p=1.07e-138 Drift rate: 971.0 μs/s (total drift over 47.0 s recording: 45.6 ms) Cropping 3.003 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 2.990 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[0-3-3-0-1.1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5170 Range : 222 ... 5391 = 2.220 ... 53.910 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.0000000000000178 First order coefficient: 0.9090909090909095 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 47.0 s recording: 4271.8 ms) Cropping 3.300 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of raw 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-0-0.9] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4500 Range : 222 ... 4721 = 2.220 ... 47.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.197442310920451e-14 First order coefficient: 1.1111111111111118 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 111111.1 μs/s (total drift over 47.0 s recording: 5221.1 ms) Cropping 0.000 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-0-0.999] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4995 Range : 222 ... 5216 = 2.220 ... 52.160 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -0.0006669238313428139 First order coefficient: 1.0010006454442604 Linear correlation computed as R=1.000 and p=8.31e-151 Drift rate: 1000.6 μs/s (total drift over 47.0 s recording: 47.0 ms) Cropping 0.001 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-0-1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 222 ... 5221 = 2.220 ... 52.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -2.842170943040401e-14 First order coefficient: 1.0000000000000002 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 47.0 s recording: 0.0 ms) Cropping 0.000 s from the start of other Resampling other Sampling frequency of the instance is already 100.00000000000003, returning unmodified. Correcting annotations in other Cropping 3.000 s from the end of other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-0-1.001] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5005 Range : 222 ... 5226 = 2.220 ... 52.260 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 0.0006680167046582142 First order coefficient: 0.9990012478097088 Linear correlation computed as R=1.000 and p=7.66e-151 Drift rate: 998.8 μs/s (total drift over 47.0 s recording: 46.9 ms) Cropping 0.001 s from the start of raw Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-0-1.1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5500 Range : 222 ... 5721 = 2.220 ... 57.210 secs Ready. 43 events found on stim channel raw_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.197442310920451e-14 First order coefficient: 0.9090909090909096 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 47.0 s recording: 4271.8 ms) Cropping 0.000 s from the start of other Resampling other 43 events found on stim channel other_stim Event IDs: [1] 43 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 43 events found on stim channel other_stim Event IDs: [1] Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated Not setting metadata 43 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-3-0.9] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4230 Range : 222 ... 4451 = 2.220 ... 44.510 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999964 First order coefficient: 1.111111111111111 Linear correlation computed as R=1.000 and p=2.58e-293 Drift rate: 111111.1 μs/s (total drift over 47.0 s recording: 5221.1 ms) Cropping 3.000 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-3-0.999] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4696 Range : 222 ... 4917 = 2.220 ... 49.170 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9988951138899296 First order coefficient: 1.0010291394436457 Linear correlation computed as R=1.000 and p=1.16e-138 Drift rate: 1029.1 μs/s (total drift over 47.0 s recording: 48.4 ms) Cropping 2.999 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.010 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-3-1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 222 ... 4921 = 2.220 ... 49.210 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999822 First order coefficient: 1.0000000000000002 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 47.0 s recording: 0.0 ms) Cropping 3.000 s from the start of raw Resampling other Sampling frequency of the instance is already 100.00000000000003, returning unmodified. Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-3-1.001] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4705 Range : 222 ... 4926 = 2.220 ... 49.260 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 3.0011052420187845 First order coefficient: 0.9989728524688074 Linear correlation computed as R=1.000 and p=1.07e-138 Drift rate: 1027.1 μs/s (total drift over 47.0 s recording: 48.3 ms) Cropping 3.001 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-0-3-1.1] Creating RawArray with float64 data, n_channels=2, n_times=4700 Range : 111 ... 4810 = 1.110 ... 48.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5170 Range : 222 ... 5391 = 2.220 ... 53.910 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: 2.9999999999999822 First order coefficient: 0.9090909090909093 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 47.0 s recording: 4271.8 ms) Cropping 3.000 s from the start of raw Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-3-0-0.9] Creating RawArray with float64 data, n_channels=2, n_times=4400 Range : 111 ... 4510 = 1.110 ... 45.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4500 Range : 222 ... 4721 = 2.220 ... 47.210 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000000000000014 First order coefficient: 1.1111111111111114 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 111111.1 μs/s (total drift over 44.0 s recording: 4887.8 ms) Cropping 2.700 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-3-0-0.999] Creating RawArray with float64 data, n_channels=2, n_times=4400 Range : 111 ... 4510 = 1.110 ... 45.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=4995 Range : 222 ... 5216 = 2.220 ... 52.160 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -2.9998088218422296 First order coefficient: 1.0009727415362895 Linear correlation computed as R=1.000 and p=1.16e-138 Drift rate: 972.7 μs/s (total drift over 44.0 s recording: 42.8 ms) Cropping 2.997 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-3-0-1] Creating RawArray with float64 data, n_channels=2, n_times=4400 Range : 111 ... 4510 = 1.110 ... 45.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5000 Range : 222 ... 5221 = 2.220 ... 52.210 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.000000000000014 First order coefficient: 1.0000000000000002 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 0.0 μs/s (total drift over 44.0 s recording: 0.0 ms) Cropping 3.000 s from the start of other Resampling other Sampling frequency of the instance is already 100.00000000000003, returning unmodified. Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-3-0-1.001] Creating RawArray with float64 data, n_channels=2, n_times=4400 Range : 111 ... 4510 = 1.110 ... 45.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5005 Range : 222 ... 5226 = 2.220 ... 52.260 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.0001878148651713 First order coefficient: 0.9990290252350824 Linear correlation computed as R=1.000 and p=1.07e-138 Drift rate: 971.0 μs/s (total drift over 44.0 s recording: 42.7 ms) Cropping 3.003 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_realign.py::test_realign[3-0-3-0-1.1] Creating RawArray with float64 data, n_channels=2, n_times=4400 Range : 111 ... 4510 = 1.110 ... 45.100 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=5500 Range : 222 ... 5721 = 2.220 ... 57.210 secs Ready. 40 events found on stim channel raw_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Zero order coefficient: -3.0000000000000178 First order coefficient: 0.9090909090909095 Linear correlation computed as R=1.000 and p=0.00e+00 Drift rate: 90909.1 μs/s (total drift over 44.0 s recording: 3999.1 ms) Cropping 3.300 s from the start of other Resampling other 40 events found on stim channel other_stim Event IDs: [1] 40 events found on stim channel other_stim Event IDs: [1] Correcting annotations in other Cropping 3.000 s from the end of other 40 events found on stim channel other_stim Event IDs: [1] Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated Not setting metadata 40 matching events found No baseline correction applied 0 projection items activated PASSED mne/preprocessing/tests/test_regress.py::test_regress_artifact SKIPPED mne/preprocessing/tests/test_regress.py::test_eog_regression SKIPPED mne/preprocessing/tests/test_regress.py::test_read_eog_regression SKIPPED mne/preprocessing/tests/test_regress.py::test_regress_artifact_bads SKIPPED mne/preprocessing/tests/test_ssp.py::test_compute_proj_ecg[True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (1.665 s) Number of ECG events detected : 6 (average pulse 51.42007050758026 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 3124 original time points ... 4 bad epochs dropped Adding projection: planar--0.200-5.000-PCA-01 (exp var=19.8%) Adding projection: planar--0.200-5.000-PCA-02 (exp var=13.1%) Adding projection: axial--0.200-5.000-PCA-01 (exp var=43.3%) Adding projection: axial--0.200-5.000-PCA-02 (exp var=22.9%) Adding projection: eeg--0.200-5.000-PCA-01 (exp var=99.0%) Adding projection: eeg--0.200-5.000-PCA-02 (exp var=0.4%) Done. Including 3 SSP projectors from raw file Running ECG SSP computation Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 6 (average pulse 51.42007050758026 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 361 original time points ... 0 bad epochs dropped No channels 'grad' found. Skipping. Adding projection: axial--0.200-0.400-PCA-01 (exp var=76.7%) Adding projection: axial--0.200-0.400-PCA-02 (exp var=9.0%) No channels 'eeg' found. Skipping. Done. Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 14 (average pulse 119.98016451768729 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.3s Not setting metadata 14 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 14 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 14 bad epochs dropped PASSED mne/preprocessing/tests/test_ssp.py::test_compute_proj_ecg[False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (1.665 s) Number of ECG events detected : 6 (average pulse 51.42007050758026 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 3124 original time points ... 4 bad epochs dropped Adding projection: planar-999--0.200-5.000-PCA-01 (exp var=16.8%) Adding projection: planar-999--0.200-5.000-PCA-02 (exp var=12.3%) Adding projection: axial-999--0.200-5.000-PCA-01 (exp var=39.9%) Adding projection: axial-999--0.200-5.000-PCA-02 (exp var=22.6%) Adding projection: eeg-999--0.200-5.000-PCA-01 (exp var=98.0%) Adding projection: eeg-999--0.200-5.000-PCA-02 (exp var=1.0%) Done. Including 3 SSP projectors from raw file Running ECG SSP computation Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 6 (average pulse 51.42007050758026 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 361 original time points ... 0 bad epochs dropped No channels 'grad' found. Skipping. Adding projection: axial--0.200-0.400-PCA-01 (exp var=76.7%) Adding projection: axial--0.200-0.400-PCA-02 (exp var=9.0%) No channels 'eeg' found. Skipping. Done. Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 14 (average pulse 119.98016451768729 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.3s Not setting metadata 14 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 14 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 14 bad epochs dropped PASSED mne/preprocessing/tests/test_ssp.py::test_compute_proj_eog[True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (1.665 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 3124 original time points ... 2 bad epochs dropped Adding projection: planar--0.200-5.000-PCA-01 (exp var=17.7%) Adding projection: planar--0.200-5.000-PCA-02 (exp var=12.3%) Adding projection: axial--0.200-5.000-PCA-01 (exp var=38.6%) Adding projection: axial--0.200-5.000-PCA-02 (exp var=24.4%) Adding projection: eeg--0.200-5.000-PCA-01 (exp var=97.7%) Adding projection: eeg--0.200-5.000-PCA-02 (exp var=1.3%) Done. Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.3s Not setting metadata 4 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 4 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Including 3 SSP projectors from raw file Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment Setting up band-pass filter from 1 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped PASSED mne/preprocessing/tests/test_ssp.py::test_compute_proj_eog[False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (1.665 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 3124 original time points ... 2 bad epochs dropped Adding projection: planar-998--0.200-5.000-PCA-01 (exp var=17.7%) Adding projection: planar-998--0.200-5.000-PCA-02 (exp var=12.3%) Adding projection: axial-998--0.200-5.000-PCA-01 (exp var=38.6%) Adding projection: axial-998--0.200-5.000-PCA-02 (exp var=24.4%) Adding projection: eeg-998--0.200-5.000-PCA-01 (exp var=97.7%) Adding projection: eeg-998--0.200-5.000-PCA-02 (exp var=1.3%) Done. Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.3s Not setting metadata 4 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 4 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Including 3 SSP projectors from raw file Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment Setting up band-pass filter from 1 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped PASSED mne/preprocessing/tests/test_ssp.py::test_compute_proj_parallel Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... Including 0 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [60] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 100 samples (1.000 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 100 samples (1.000 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Not setting metadata 3 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 3 events and 521 original time points ... 2 bad epochs dropped No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-998--0.200-5.000-PCA-01 (exp var=98.0%) Adding projection: eeg-998--0.200-5.000-PCA-02 (exp var=1.2%) Done. Including 0 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [60] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 100 samples (1.000 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 100 samples (1.000 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 18 tasks | elapsed: 2.1s [Parallel(n_jobs=2)]: Done 60 out of 60 | elapsed: 2.1s finished Not setting metadata 3 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 3 events and 521 original time points ... 2 bad epochs dropped ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-998--0.200-5.000-PCA-01 (exp var=98.0%) Adding projection: eeg-998--0.200-5.000-PCA-02 (exp var=1.2%) Done. 3 projection items activated 3 projection items activated PASSED mne/preprocessing/tests/test_ssp.py::test_compute_proj_ctf SKIPPED (...) mne/preprocessing/tests/test_stim.py::test_fix_stim_artifact Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped PASSED mne/preprocessing/tests/test_xdawn.py::test_xdawn PASSED mne/preprocessing/tests/test_xdawn.py::test_xdawn_picks Not setting metadata 10 matching events found No baseline correction applied 0 projection items activated Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Transforming to Xdawn space Inverse transforming to sensor space PASSED mne/preprocessing/tests/test_xdawn.py::test_xdawn_fit EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Using up to 119 segments Number of samples used : 14280 [done] Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. PASSED mne/preprocessing/tests/test_xdawn.py::test_xdawn_apply_transform EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space Transforming to Xdawn space Zeroing out 58 Xdawn components Inverse transforming to sensor space PASSED mne/preprocessing/tests/test_xdawn.py::test_xdawn_regularization Estimating covariance using OAS Done. Estimating covariance using OAS Done. Estimating covariance using OAS Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using LEDOIT_WOLF Done. Estimating covariance using LEDOIT_WOLF Done. Estimating covariance using OAS Done. Estimating covariance using OAS Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 GRAD regularization : 0.1 Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 GRAD regularization : 0.1 Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 GRAD regularization : 0.1 Done. PASSED mne/preprocessing/tests/test_xdawn.py::test_XdawnTransformer EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Using up to 119 segments Number of samples used : 14280 [done] Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. PASSED mne/preprocessing/tests/test_xdawn.py::test_xdawn_decoding_performance Not setting metadata 100 matching events found No baseline correction applied 0 projection items activated Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using EMPIRICAL Done. PASSED mne/report/tests/test_report.py::test_render_report[pyvistaqt] SKIPPED mne/report/tests/test_report.py::test_render_mne_qt_browser[matplotlib] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/report/tests/test_report.py::test_render_mne_qt_browser[qt] SKIPPED mne/report/tests/test_report.py::test_render_report_extra[pyvistaqt] SKIPPED mne/report/tests/test_report.py::test_add_custom_css Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css Saving report to : /tmp/pytest-of-pbuilder1/pytest-0/test_add_custom_css0/report.html PASSED mne/report/tests/test_report.py::test_add_custom_js Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css Saving report to : /tmp/pytest-of-pbuilder1/pytest-0/test_add_custom_js0/report.html PASSED mne/report/tests/test_report.py::test_render_non_fiff SKIPPED (Requi...) mne/report/tests/test_report.py::test_report_raw_psd_and_date SKIPPED mne/report/tests/test_report.py::test_render_add_sections[pyvistaqt] SKIPPED mne/report/tests/test_report.py::test_render_mri[pyvistaqt] SKIPPED mne/report/tests/test_report.py::test_add_bem_n_jobs[1] SKIPPED (Req...) mne/report/tests/test_report.py::test_add_bem_n_jobs[2] SKIPPED (Req...) mne/report/tests/test_report.py::test_render_mri_without_bem SKIPPED mne/report/tests/test_report.py::test_add_html SKIPPED (Requires tes...) mne/report/tests/test_report.py::test_multiple_figs SKIPPED (Require...) mne/report/tests/test_report.py::test_validate_input Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_open_report SKIPPED (could not...) mne/report/tests/test_report.py::test_remove Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_add_or_replace[True] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_add_or_replace[False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_add_or_replace_section Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_scraper Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css Saving report to : /tmp/pytest-of-pbuilder1/pytest-0/test_scraper0/my_html.html PASSED mne/report/tests/test_report.py::test_split_files[neuromag] SKIPPED mne/report/tests/test_report.py::test_split_files[bids] SKIPPED (Req...) mne/report/tests/test_report.py::test_survive_pickle SKIPPED (Requir...) mne/report/tests/test_report.py::test_manual_report_2d SKIPPED (Requ...) mne/report/tests/test_report.py::test_manual_report_3d[pyvistaqt] SKIPPED mne/report/tests/test_report.py::test_sorting Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css Saving report to : /tmp/pytest-of-pbuilder1/pytest-0/test_sorting0/report.html PASSED mne/report/tests/test_report.py::test_tags[123-False-True-False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags1-True-True-False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags2-True-True-False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags3-True-False-True] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags4-True-False-True] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags5-True-False-True] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[foo-True-False-False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags7-True-False-False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags8-True-False-False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_tags[tags9-True-False-False] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_image_format[png] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_image_format[svg] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_image_format[webp] Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/report/tests/test_report.py::test_gif Embedding : jquery-3.6.0.min.js Embedding : bootstrap.bundle.min.js Embedding : bootstrap.min.css Embedding : bootstrap-table/bootstrap-table.min.js Embedding : bootstrap-table/bootstrap-table.min.css Embedding : bootstrap-table/bootstrap-table-copy-rows.min.js Embedding : bootstrap-table/bootstrap-table-export.min.js Embedding : bootstrap-table/tableExport.min.js Embedding : bootstrap-icons/bootstrap-icons.mne.min.css Embedding : highlightjs/highlight.min.js Embedding : highlightjs/atom-one-dark-reasonable.min.css PASSED mne/simulation/metrics/tests/test_metrics.py::test_uniform_and_thresholding SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_cosine_score SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_region_localization_error SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_precision_score SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_recall_score SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_f1_score SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_roc_auc_score SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_peak_position_error SKIPPED mne/simulation/metrics/tests/test_metrics.py::test_spatial_deviation SKIPPED mne/simulation/tests/test_evoked.py::test_simulate_evoked SKIPPED (R...) mne/simulation/tests/test_evoked.py::test_add_noise Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Using up to 119 segments Number of samples used : 14280 [done] Reading 0 ... 601 = 0.000 ... 1.001 secs... Adding noise to 366/376 channels (366 channels in cov) Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using up to 5 segments Number of samples used : 600 [done] Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 160 Estimating covariance using EMPIRICAL Done. Number of samples used : 100 [done] Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 160 Estimating covariance using EMPIRICAL Done. Number of samples used : 100 [done] PASSED mne/simulation/tests/test_evoked.py::test_rank_deficiency Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Computing rank from covariance with rank=None Using tolerance 3.6e-15 (2.2e-16 eps * 20 dim * 0.81 max singular value) Estimated rank (mag + grad): 17 MEG: rank 17 computed from 20 data channels with 3 projectors 3 projection items activated MAG regularization : 0.1 Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank={'meg': 17} Using tolerance 1.1e-16 (2.2e-16 eps * 7 dim * 0.071 max singular value) Estimated rank (mag): 4 MAG: rank 4 computed from 7 data channels with 3 projectors Setting small MAG eigenvalues to zero (without PCA) GRAD regularization : 0.1 Computing rank from covariance with rank={'meg': 17, 'mag': 4} Using tolerance 2.3e-15 (2.2e-16 eps * 13 dim * 0.79 max singular value) Estimated rank (grad): 13 GRAD: rank 13 computed from 13 data channels with 0 projectors Setting small GRAD eigenvalues to zero (without PCA) Adding noise to 20/20 channels (20 channels in cov) Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/simulation/tests/test_evoked.py::test_order SKIPPED (Requires te...) mne/simulation/tests/test_metrics.py::test_metrics SKIPPED (Requires...) mne/simulation/tests/test_raw.py::test_iterable Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Sphere : origin at (0.0 0.0 0.0) mm radius : 95.0 mm Source location file : dict() Assuming input in millimeters Assuming input in MRI coordinates Positions (in meters) and orientations 2 sources Setting up raw simulation: 1 position, "cos2" interpolation Setting up raw simulation: 1 position, "cos2" interpolation Event information stored on channel: STI 014 Interval 0.000–1.665 s Setting up forward solutions Computing gain matrix for transform #1/1 Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s 15 STC iterations provided [done] 15 events found on stim channel STI 014 Event IDs: [1] Setting up raw simulation: 1 position, "cos2" interpolation Setting up raw simulation: 1 position, "cos2" interpolation Event information stored on channel: STI 014 Interval 0.000–1.665 s Setting up forward solutions Computing gain matrix for transform #1/1 Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s 15 STC iterations provided [done] 15 events found on stim channel STI 014 Event IDs: [1] 15 events found on stim channel STI 014 Event IDs: [3] Setting up raw simulation: 1 position, "cos2" interpolation Setting up raw simulation: 1 position, "cos2" interpolation Setting up raw simulation: 1 position, "cos2" interpolation Event information stored on channel: STI 014 Interval 0.000–1.665 s Setting up forward solutions Computing gain matrix for transform #1/1 Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s 15 STC iterations provided [done] 15 events found on stim channel STI 014 Event IDs: [1] 15 events found on stim channel STI 014 Event IDs: [4] Setting up raw simulation: 1 position, "cos2" interpolation 15 events found on stim channel STI 014 Event IDs: [1] 15 events found on stim channel STI 014 Event IDs: [4] Setting up raw simulation: 1 position, "cos2" interpolation Event information stored on channel: STI 014 Interval 0.000–1.665 s Setting up forward solutions Computing gain matrix for transform #1/1 Interval 0.000–1.665 s inner skull CM is 0.00 -0.00 -0.00 mm Surfaces passed the basic topology checks. Homogeneous model surface loaded. Computing the linear collocation solution... Matrix coefficients... inner skull (642) -> inner skull (642) ... Inverting the coefficient matrix... Solution ready. BEM geometry computations complete. Setting up raw simulation: 1 position, "cos2" interpolation Event information stored on channel: STI 014 Interval 0.000–1.665 s Setting up forward solutions Computing gain matrix for transform #1/1 1 STC iteration provided [done] PASSED mne/simulation/tests/test_raw.py::test_simulate_raw_sphere[testing_data] SKIPPED mne/simulation/tests/test_raw.py::test_degenerate[testing_data] SKIPPED mne/simulation/tests/test_raw.py::test_simulate_raw_bem[testing_data] SKIPPED mne/simulation/tests/test_raw.py::test_simulate_round_trip[testing_data] SKIPPED mne/simulation/tests/test_raw.py::test_simulate_raw_chpi SKIPPED (Re...) mne/simulation/tests/test_raw.py::test_simulation_cascade SKIPPED (R...) mne/simulation/tests/test_source.py::test_simulate_stc[testing_data] SKIPPED mne/simulation/tests/test_source.py::test_simulate_sparse_stc[testing_data] SKIPPED mne/simulation/tests/test_source.py::test_generate_stc_single_hemi[testing_data] SKIPPED mne/simulation/tests/test_source.py::test_simulate_sparse_stc_single_hemi[testing_data] SKIPPED mne/simulation/tests/test_source.py::test_simulate_stc_labels_overlap[testing_data] SKIPPED mne/simulation/tests/test_source.py::test_source_simulator[testing_data] SKIPPED mne/source_estimate.py::mne.source_estimate._BaseVolSourceEstimate.plot SKIPPED mne/source_space/_source_space.py::mne.source_space._source_space.SourceSpaces SKIPPED mne/source_space/tests/test_source_space.py::test_compute_distance_to_sensors[meg-limits0] SKIPPED mne/source_space/tests/test_source_space.py::test_compute_distance_to_sensors[None-limits1] SKIPPED mne/source_space/tests/test_source_space.py::test_compute_distance_to_sensors[eeg-limits2] SKIPPED mne/source_space/tests/test_source_space.py::test_add_patch_info Reading a source space... Computing patch statistics... Patch information added... [done] Reading a source space... Computing patch statistics... Patch information added... [done] 2 source spaces read Reading a source space... Computing patch statistics... Patch information added... [done] Reading a source space... Computing patch statistics... Patch information added... [done] 2 source spaces read Calculating source space distances (limit=0.01 mm)... Not adding patch information, dist_limit too small Calculating source space distances (limit=inf mm)... Computing patch statistics... Patch information added... Computing patch statistics... Patch information added... Calculating patch information (limit=0.0 mm)... Computing patch statistics... Patch information added... Computing patch statistics... Patch information added... PASSED mne/source_space/tests/test_source_space.py::test_surface_source_space_doc[fwd] SKIPPED mne/source_space/tests/test_source_space.py::test_surface_source_space_doc[src] SKIPPED mne/source_space/tests/test_source_space.py::test_add_source_space_distances_limited SKIPPED mne/source_space/tests/test_source_space.py::test_add_source_space_distances SKIPPED mne/source_space/tests/test_source_space.py::test_discrete_source_space SKIPPED mne/source_space/tests/test_source_space.py::test_volume_source_space SKIPPED mne/source_space/tests/test_source_space.py::test_other_volume_source_spaces SKIPPED mne/source_space/tests/test_source_space.py::test_triangle_neighbors SKIPPED mne/source_space/tests/test_source_space.py::test_accumulate_normals PASSED mne/source_space/tests/test_source_space.py::test_setup_source_space SKIPPED mne/source_space/tests/test_source_space.py::test_setup_source_space_spacing[2] SKIPPED mne/source_space/tests/test_source_space.py::test_setup_source_space_spacing[7] SKIPPED mne/source_space/tests/test_source_space.py::test_read_source_spaces SKIPPED mne/source_space/tests/test_source_space.py::test_write_source_space SKIPPED mne/source_space/tests/test_source_space.py::test_source_space_from_label[True] SKIPPED mne/source_space/tests/test_source_space.py::test_source_space_from_label[False] SKIPPED mne/source_space/tests/test_source_space.py::test_source_space_exclusive_complete SKIPPED mne/source_space/tests/test_source_space.py::test_read_volume_from_src SKIPPED mne/source_space/tests/test_source_space.py::test_combine_source_spaces SKIPPED mne/source_space/tests/test_source_space.py::test_morph_source_spaces SKIPPED mne/source_space/tests/test_source_space.py::test_morphed_source_space_return SKIPPED mne/source_space/tests/test_source_space.py::test_get_decimated_surfaces[src0-2-10242] SKIPPED mne/source_space/tests/test_source_space.py::test_get_decimated_surfaces[src1-2-258] SKIPPED mne/source_space/tests/test_source_space.py::test_get_decimated_surfaces[src2-0-0] SKIPPED mne/stats/_adjacency.py::mne.stats._adjacency.combine_adjacency PASSED mne/stats/cluster_level.py::mne.stats.cluster_level.bin_perm_rep PASSED mne/stats/erp.py::mne.stats.erp.compute_sme SKIPPED (all tests skipp...) mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape0] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape1] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape2] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape3] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape4] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape5] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape6] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape7] PASSED mne/stats/tests/test_adjacency.py::test_adjacency_equiv[shape8] PASSED mne/stats/tests/test_cluster_level.py::test_thresholds[Numba] SKIPPED mne/stats/tests/test_cluster_level.py::test_thresholds[NumPy] 0%| | Permuting (exact test) : 0/511 [00:00 124.0 - 124.5) [1] Keeping (1970-01-01 00:02:08.141593+00:00 - 1970-01-01 00:02:08.641593+00:00 -> 125.0 - 125.5) [2] Keeping (1970-01-01 00:02:17.141593+00:00 - 1970-01-01 00:02:17.641593+00:00 -> 134.0 - 134.5) [3] Keeping (1970-01-01 00:02:18.141593+00:00 - 1970-01-01 00:02:18.641593+00:00 -> 135.0 - 135.5) [4] Keeping (1970-01-01 00:02:27.141593+00:00 - 1970-01-01 00:02:27.641593+00:00 -> 144.0 - 144.5) [5] Keeping (1970-01-01 00:02:28.141593+00:00 - 1970-01-01 00:02:28.641593+00:00 -> 145.0 - 145.5) [6] Keeping (1970-01-01 00:02:37.141593+00:00 - 1970-01-01 00:02:37.641593+00:00 -> 154.0 - 154.5) Cropping complete (kept 7) PASSED mne/tests/test_annotations.py::test_crop Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Cropping annotations 2002-12-03 19:01:53.676071+00:00 - 2002-12-03 19:02:17.651497+00:00 [0] Keeping (2002-12-03 19:01:58.622667+00:00 - 2002-12-03 19:01:59.122667+00:00 -> 47.902567 - 48.402567) [1] Keeping (2002-12-03 19:02:01.418135+00:00 - 2002-12-03 19:02:01.918135+00:00 -> 50.698035 - 51.198035) [2] Keeping (2002-12-03 19:02:04.166984+00:00 - 2002-12-03 19:02:04.666984+00:00 -> 53.446884 - 53.946884) [3] Keeping (2002-12-03 19:02:06.849235+00:00 - 2002-12-03 19:02:07.349235+00:00 -> 56.129135 - 56.629135) [4] Keeping (2002-12-03 19:02:09.706307+00:00 - 2002-12-03 19:02:10.206307+00:00 -> 58.98620700000001 - 59.48620700000001) [5] Keeping (2002-12-03 19:02:12.335279+00:00 - 2002-12-03 19:02:12.835279+00:00 -> 61.615179000000005 - 62.115179000000005) [6] Keeping (2002-12-03 19:02:15.172371+00:00 - 2002-12-03 19:02:15.672371+00:00 -> 64.452271 - 64.952271) Cropping complete (kept 7) Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 54599 = 42.956 ... 90.905 secs Ready. Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 54599 = 42.956 ... 90.905 secs Ready. Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 19.980 secs Ready. Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_crop0/test_raw.fif... Isotrak not found Range : 250 ... 900 = 5.000 ... 18.000 secs Ready. PASSED mne/tests/test_annotations.py::test_chunk_duration[0] Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. Used Annotations descriptions: ['foo'] Used Annotations descriptions: ['foo'] PASSED mne/tests/test_annotations.py::test_chunk_duration[10000] Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10000 ... 10009 = 10000.000 ... 10009.000 secs Ready. Used Annotations descriptions: ['foo'] Used Annotations descriptions: ['foo'] PASSED mne/tests/test_annotations.py::test_events_from_annotation_orig_time_none Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 9.990 secs Ready. Used Annotations descriptions: ['0'] Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 101 original time points ... 0 bad epochs dropped PASSED mne/tests/test_annotations.py::test_crop_more Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 6607 = 0.000 ... 11.000 secs... PASSED mne/tests/test_annotations.py::test_read_brainstorm_annotations SKIPPED mne/tests/test_annotations.py::test_read_edf_annotations[fname0-154] SKIPPED mne/tests/test_annotations.py::test_read_edf_annotations[fname1-5] SKIPPED mne/tests/test_annotations.py::test_raw_reject[0] Creating RawArray with float64 data, n_channels=5, n_times=15000 Range : 0 ... 14999 = 0.000 ... 149.990 secs Ready. Omitting 1200 of 11100 (10.81%) samples, retaining 9900 (89.19%) samples. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Omitting 2402 of 10811 (22.22%) samples, retaining 8409 (77.78%) samples. Setting 2402 of 10811 (22.22%) samples to NaN, retaining 8409 (77.78%) samples. PASSED mne/tests/test_annotations.py::test_raw_reject[10000] Creating RawArray with float64 data, n_channels=5, n_times=15000 Range : 10000 ... 24999 = 100.000 ... 249.990 secs Ready. Omitting 1200 of 11100 (10.81%) samples, retaining 9900 (89.19%) samples. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Omitting 2402 of 10811 (22.22%) samples, retaining 8409 (77.78%) samples. Setting 2402 of 10811 (22.22%) samples to NaN, retaining 8409 (77.78%) samples. PASSED mne/tests/test_annotations.py::test_annotation_filtering[0] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 4 contiguous segments Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 4 contiguous segments Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) PASSED mne/tests/test_annotations.py::test_annotation_filtering[10000] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 10000 ... 10999 = 10.000 ... 10.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 4 contiguous segments Setting up low-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 50.00 Hz - Upper transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 56.25 Hz) - Filter length: 265 samples (0.265 s) Filtering raw data in 4 contiguous segments Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. Filtering raw data in 1 contiguous segment Setting up high-pass filter at 50 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 50.00 - Lower transition bandwidth: 12.50 Hz (-6 dB cutoff frequency: 43.75 Hz) - Filter length: 265 samples (0.265 s) PASSED mne/tests/test_annotations.py::test_annotation_omit[0] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 0 ... 1999 = 0.000 ... 1.999 secs Ready. Setting 1000 of 2000 (50.00%) samples to NaN, retaining 1000 (50.00%) samples. Setting 500 of 1000 (50.00%) samples to NaN, retaining 500 (50.00%) samples. Setting 500 of 1000 (50.00%) samples to NaN, retaining 500 (50.00%) samples. Omitting 1000 of 2000 (50.00%) samples, retaining 1000 (50.00%) samples. Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. PASSED mne/tests/test_annotations.py::test_annotation_omit[10000] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 10000 ... 11999 = 10.000 ... 11.999 secs Ready. Setting 1000 of 2000 (50.00%) samples to NaN, retaining 1000 (50.00%) samples. Setting 500 of 1000 (50.00%) samples to NaN, retaining 500 (50.00%) samples. Setting 500 of 1000 (50.00%) samples to NaN, retaining 500 (50.00%) samples. Omitting 1000 of 2000 (50.00%) samples, retaining 1000 (50.00%) samples. Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. Omitting 500 of 1000 (50.00%) samples, retaining 500 (50.00%) samples. PASSED mne/tests/test_annotations.py::test_annotation_epoching Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 5 events and 1000 original time points ... 2 bad epochs dropped PASSED mne/tests/test_annotations.py::test_annotation_concat PASSED mne/tests/test_annotations.py::test_annotations_crop PASSED mne/tests/test_annotations.py::test_events_from_annot_in_raw_objects SKIPPED mne/tests/test_annotations.py::test_events_from_annot_onset_alingment Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 10 ... 19 = 1.000 ... 1.900 secs Ready. Used Annotations descriptions: ['dummy'] PASSED mne/tests/test_annotations.py::test_events_from_annot_with_tolerance[rounding-notol] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 9.990 secs Ready. Used Annotations descriptions: ['0', '1', '2'] PASSED mne/tests/test_annotations.py::test_events_from_annot_with_tolerance[rounding-tol] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 9.990 secs Ready. Used Annotations descriptions: ['0', '1', '2'] PASSED mne/tests/test_annotations.py::test_events_from_annot_with_tolerance[norounding-notol] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 9.990 secs Ready. Used Annotations descriptions: ['0', '1', '2'] PASSED mne/tests/test_annotations.py::test_events_from_annot_with_tolerance[norounding-tol] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 9.990 secs Ready. Used Annotations descriptions: ['0', '1', '2'] PASSED mne/tests/test_annotations.py::test_events_from_annot_with_tolerance[default] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 9.990 secs Ready. Used Annotations descriptions: ['0', '1', '2'] PASSED mne/tests/test_annotations.py::test_event_id_function_default Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 0 ... 9 = 0.000 ... 0.009 secs Ready. Used Annotations descriptions: ['a', 'b', 'c', 'd', 'e', 'f', 'g'] PASSED mne/tests/test_annotations.py::test_event_id_function_using_custom_function Creating RawArray with float64 data, n_channels=10, n_times=10 Range : 0 ... 9 = 0.000 ... 0.009 secs Ready. Used Annotations descriptions: ['a', 'b', 'c', 'd', 'e', 'f', 'g'] PASSED mne/tests/test_annotations.py::test_io_annotation[ch_names-csv-False] SKIPPED mne/tests/test_annotations.py::test_io_annotation[ch_names-csv-True] SKIPPED mne/tests/test_annotations.py::test_io_annotation[ch_names-txt-False] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation[ch_names-txt-True] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation[ch_names-fif-False] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation[ch_names-fif-True] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation[fmt-csv-False] SKIPPED mne/tests/test_annotations.py::test_io_annotation[fmt-csv-True] SKIPPED mne/tests/test_annotations.py::test_io_annotation[fmt-txt-False] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation[fmt-txt-True] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation[fmt-fif-False] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation[fmt-fif-True] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_broken_csv SKIPPED (could not im...) mne/tests/test_annotations.py::test_io_annotation_txt[ch_names-False] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_io_annotation_txt[ch_names-True] Overwriting existing file. PASSED mne/tests/test_annotations.py::test_handle_meas_date[invalid string] PASSED mne/tests/test_annotations.py::test_handle_meas_date[None] PASSED mne/tests/test_annotations.py::test_handle_meas_date[Scalar] PASSED mne/tests/test_annotations.py::test_handle_meas_date[Float] PASSED mne/tests/test_annotations.py::test_handle_meas_date[Scalar tuple] PASSED mne/tests/test_annotations.py::test_handle_meas_date[valid iso8601 string] PASSED mne/tests/test_annotations.py::test_handle_meas_date[invalid iso8601 string] PASSED mne/tests/test_annotations.py::test_read_annotation_txt_header PASSED mne/tests/test_annotations.py::test_read_annotation_txt_one_segment PASSED mne/tests/test_annotations.py::test_read_annotation_txt_empty PASSED mne/tests/test_annotations.py::test_annotations_simple_iteration PASSED mne/tests/test_annotations.py::test_annotations_slices PASSED mne/tests/test_annotations.py::test_sorting PASSED mne/tests/test_annotations.py::test_date_none Creating RawArray with float64 data, n_channels=139, n_times=20 Range : 0 ... 19 = 0.000 ... 0.009 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_date_none0/test-raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_date_none0/test-raw.fif [done] Opening raw data file /tmp/pytest-of-pbuilder1/pytest-0/test_date_none0/test-raw.fif... Isotrak not found Range : 0 ... 19 = 0.000 ... 0.009 secs Ready. Reading 0 ... 19 = 0.000 ... 0.009 secs... PASSED mne/tests/test_annotations.py::test_negative_meas_dates Creating RawArray with float64 data, n_channels=1, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready. Used Annotations descriptions: ['foo'] PASSED mne/tests/test_annotations.py::test_crop_when_negative_orig_time Cropping annotations 1941-03-22 11:04:14.988669+00:00 - 1941-03-22 11:04:15.888669+00:00 [0] Keeping (1941-03-22 11:04:14.988669+00:00 - 1941-03-22 11:04:14.988669+00:00 -> 0.0 - 0.0) [1] Keeping (1941-03-22 11:04:15.088669+00:00 - 1941-03-22 11:04:15.088669+00:00 -> 0.1 - 0.1) [2] Keeping (1941-03-22 11:04:15.188669+00:00 - 1941-03-22 11:04:15.188669+00:00 -> 0.2 - 0.2) [3] Keeping (1941-03-22 11:04:15.288669+00:00 - 1941-03-22 11:04:15.288669+00:00 -> 0.30000000000000004 - 0.30000000000000004) [4] Keeping (1941-03-22 11:04:15.388669+00:00 - 1941-03-22 11:04:15.388669+00:00 -> 0.4 - 0.4) [5] Keeping (1941-03-22 11:04:15.488669+00:00 - 1941-03-22 11:04:15.488669+00:00 -> 0.5 - 0.5) [6] Keeping (1941-03-22 11:04:15.588669+00:00 - 1941-03-22 11:04:15.588669+00:00 -> 0.6000000000000001 - 0.6000000000000001) [7] Keeping (1941-03-22 11:04:15.688669+00:00 - 1941-03-22 11:04:15.688669+00:00 -> 0.7000000000000001 - 0.7000000000000001) [8] Keeping (1941-03-22 11:04:15.788669+00:00 - 1941-03-22 11:04:15.788669+00:00 -> 0.8 - 0.8) [9] Keeping (1941-03-22 11:04:15.888669+00:00 - 1941-03-22 11:04:15.888669+00:00 -> 0.9 - 0.9) Cropping complete (kept 10) PASSED mne/tests/test_annotations.py::test_crop_with_none PASSED mne/tests/test_annotations.py::test_crop_wo_orig_time PASSED mne/tests/test_annotations.py::test_allow_nan_durations Creating RawArray with float64 data, n_channels=2, n_times=10 Range : 0 ... 9 = 0.000 ... 9.000 secs Ready. PASSED mne/tests/test_annotations.py::test_annotations_from_events SKIPPED mne/tests/test_annotations.py::test_repr PASSED mne/tests/test_annotations.py::test_annotation_to_data_frame[None] SKIPPED mne/tests/test_annotations.py::test_annotation_to_data_frame[ms] SKIPPED mne/tests/test_annotations.py::test_annotation_to_data_frame[datetime] SKIPPED mne/tests/test_annotations.py::test_annotation_to_data_frame[timedelta] SKIPPED mne/tests/test_annotations.py::test_annotation_ch_names Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/tests/test_annotations.py::test_annotation_rename PASSED mne/tests/test_annotations.py::test_annotation_duration_setting PASSED mne/tests/test_annotations.py::test_annot_noop[0-before-None] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 0 ... 1999 = 0.000 ... 1.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[0-before-1] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 0 ... 1999 = 0.000 ... 1.999 secs Ready. Cropping annotations 1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:03+00:00 [0] Keeping (1970-01-01 00:00:01.500000+00:00 - 1970-01-01 00:00:01.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:03+00:00 [0] Keeping (1970-01-01 00:00:01.500000+00:00 - 1970-01-01 00:00:01.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[0-after-None] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 0 ... 1999 = 0.000 ... 1.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[0-after-1] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 0 ... 1999 = 0.000 ... 1.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:03+00:00 [0] Keeping (1970-01-01 00:00:01.500000+00:00 - 1970-01-01 00:00:01.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[100-before-None] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 100 ... 2099 = 0.100 ... 2.099 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[100-before-1] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 100 ... 2099 = 0.100 ... 2.099 secs Ready. Cropping annotations 1970-01-01 00:00:01.100000+00:00 - 1970-01-01 00:00:03.100000+00:00 [0] Keeping (1970-01-01 00:00:01.600000+00:00 - 1970-01-01 00:00:01.700000+00:00 -> 0.6 - 0.7) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:01.100000+00:00 - 1970-01-01 00:00:03.100000+00:00 [0] Keeping (1970-01-01 00:00:01.600000+00:00 - 1970-01-01 00:00:01.700000+00:00 -> 0.6 - 0.7) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[100-after-None] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 100 ... 2099 = 0.100 ... 2.099 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[100-after-1] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 100 ... 2099 = 0.100 ... 2.099 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:01.100000+00:00 - 1970-01-01 00:00:03.100000+00:00 [0] Keeping (1970-01-01 00:00:01.600000+00:00 - 1970-01-01 00:00:01.700000+00:00 -> 0.6 - 0.7) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[3000-before-None] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 3000 ... 4999 = 3.000 ... 4.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[3000-before-1] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 3000 ... 4999 = 3.000 ... 4.999 secs Ready. Cropping annotations 1970-01-01 00:00:04+00:00 - 1970-01-01 00:00:06+00:00 [0] Keeping (1970-01-01 00:00:04.500000+00:00 - 1970-01-01 00:00:04.600000+00:00 -> 3.5 - 3.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:04+00:00 - 1970-01-01 00:00:06+00:00 [0] Keeping (1970-01-01 00:00:04.500000+00:00 - 1970-01-01 00:00:04.600000+00:00 -> 3.5 - 3.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[3000-after-None] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 3000 ... 4999 = 3.000 ... 4.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_noop[3000-after-1] Creating RawArray with float64 data, n_channels=1, n_times=2000 Range : 3000 ... 4999 = 3.000 ... 4.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:02+00:00 [0] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) Cropping complete (kept 1) Cropping annotations 1970-01-01 00:00:04+00:00 - 1970-01-01 00:00:06+00:00 [0] Keeping (1970-01-01 00:00:04.500000+00:00 - 1970-01-01 00:00:04.600000+00:00 -> 3.5 - 3.6) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-first-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-first-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-second-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-second-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-both-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-both-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-None-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-0-None-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-first-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-first-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-second-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-second-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-both-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-both-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:06+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:09.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [2] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [3] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00 -> 8.0 - 8.1) [4] Keeping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.006250+00:00 -> 9.0 - 9.1) [5] Dropping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00: on) [6] Dropping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:01+00:00 Cropping data to 2022-01-01 00:00:12.500000+00:00 Second annot at 2022-01-01 00:00:06+00:00 Cropping annotations 2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:16.006250+00:00 [0] Dropping (2022-01-01 00:00:03.500000+00:00 - 2022-01-01 00:00:03.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: on) [2] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:09+00:00 - 2022-01-01 00:00:09.100000+00:00: off) [5] Keeping (2022-01-01 00:00:12.500000+00:00 - 2022-01-01 00:00:12.600000+00:00 -> 12.5 - 12.6) [6] Keeping (2022-01-01 00:00:13+00:00 - 2022-01-01 00:00:13.100000+00:00 -> 13.0 - 13.1) [7] Keeping (2022-01-01 00:00:13.700000+00:00 - 2022-01-01 00:00:13.800000+00:00 -> 13.7 - 13.799999999999999) [8] Keeping (2022-01-01 00:00:16+00:00 - 2022-01-01 00:00:16.006250+00:00 -> 16.0 - 16.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-None-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[160-320-None-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 160 ... 1759 = 1.000 ... 10.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-first-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-first-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-second-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-second-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-both-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-both-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-None-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-0-None-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-first-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-first-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-second-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-second-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-both-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-both-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:05+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:08.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Keeping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00 -> 5.0 - 5.1) [2] Keeping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00 -> 6.0 - 6.1) [3] Keeping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00 -> 7.0 - 7.1) [4] Keeping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.006250+00:00 -> 8.0 - 8.1) [5] Dropping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00: on) [6] Dropping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00: 3) [7] Dropping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00: 4) [8] Dropping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.100000+00:00: off) Cropping complete (kept 4) meas_info set to 2022-01-01 00:00:00+00:00 Data starts at 2022-01-01 00:00:00+00:00 Cropping data to 2022-01-01 00:00:11.500000+00:00 Second annot at 2022-01-01 00:00:05+00:00 Cropping annotations 2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:15.006250+00:00 [0] Dropping (2022-01-01 00:00:02.500000+00:00 - 2022-01-01 00:00:02.600000+00:00: 12) [1] Dropping (2022-01-01 00:00:05+00:00 - 2022-01-01 00:00:05.100000+00:00: on) [2] Dropping (2022-01-01 00:00:06+00:00 - 2022-01-01 00:00:06.100000+00:00: 1) [3] Dropping (2022-01-01 00:00:07+00:00 - 2022-01-01 00:00:07.100000+00:00: 2) [4] Dropping (2022-01-01 00:00:08+00:00 - 2022-01-01 00:00:08.100000+00:00: off) [5] Keeping (2022-01-01 00:00:11.500000+00:00 - 2022-01-01 00:00:11.600000+00:00 -> 11.5 - 11.6) [6] Keeping (2022-01-01 00:00:12+00:00 - 2022-01-01 00:00:12.100000+00:00 -> 12.0 - 12.1) [7] Keeping (2022-01-01 00:00:12.700000+00:00 - 2022-01-01 00:00:12.800000+00:00 -> 12.7 - 12.799999999999999) [8] Keeping (2022-01-01 00:00:15+00:00 - 2022-01-01 00:00:15.006250+00:00 -> 15.0 - 15.1) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-None-before] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_concat_crop[0-320-None-after] Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 0 ... 1599 = 0.000 ... 9.994 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=1600 Range : 320 ... 1919 = 2.000 ... 11.994 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.006250+00:00 [0] Dropping (1969-12-31 23:59:57.500000+00:00 - 1969-12-31 23:59:57.600000+00:00: 12) [1] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [2] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.100000+00:00 -> 1.0 - 1.1) [3] Keeping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.100000+00:00 -> 2.0 - 2.1) [4] Keeping (1970-01-01 00:00:03+00:00 - 1970-01-01 00:00:03.006250+00:00 -> 3.0 - 3.1) [5] Dropping (1970-01-01 00:00:06.500000+00:00 - 1970-01-01 00:00:06.600000+00:00: on) [6] Dropping (1970-01-01 00:00:07+00:00 - 1970-01-01 00:00:07.100000+00:00: 3) [7] Dropping (1970-01-01 00:00:07.700000+00:00 - 1970-01-01 00:00:07.800000+00:00: 4) [8] Dropping (1970-01-01 00:00:10+00:00 - 1970-01-01 00:00:10.100000+00:00: off) Cropping complete (kept 4) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:03.506250+00:00 [0] Dropping (1969-12-31 23:59:51+00:00 - 1969-12-31 23:59:51.100000+00:00: 12) [1] Dropping (1969-12-31 23:59:53.500000+00:00 - 1969-12-31 23:59:53.600000+00:00: on) [2] Dropping (1969-12-31 23:59:54.500000+00:00 - 1969-12-31 23:59:54.600000+00:00: 1) [3] Dropping (1969-12-31 23:59:55.500000+00:00 - 1969-12-31 23:59:55.600000+00:00: 2) [4] Dropping (1969-12-31 23:59:56.500000+00:00 - 1969-12-31 23:59:56.600000+00:00: off) [5] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [6] Keeping (1970-01-01 00:00:00.500000+00:00 - 1970-01-01 00:00:00.600000+00:00 -> 0.5 - 0.6) [7] Keeping (1970-01-01 00:00:01.200000+00:00 - 1970-01-01 00:00:01.300000+00:00 -> 1.1999999999999993 - 1.2999999999999994) [8] Keeping (1970-01-01 00:00:03.500000+00:00 - 1970-01-01 00:00:03.506250+00:00 -> 3.5 - 3.6) Cropping complete (kept 4) PASSED mne/tests/test_annotations.py::test_annot_meas_date_first_samp_crop[None-0] Creating RawArray with float64 data, n_channels=1, n_times=3000 Range : 0 ... 2999 = 0.000 ... 2.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:01.501000+00:00 [0] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [1] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.200000+00:00 -> 1.0 - 1.2) [2] Dropping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.300000+00:00: c) Cropping complete (kept 2) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.501000+00:00 [0] Dropping (1969-12-31 23:59:58+00:00 - 1969-12-31 23:59:58.100000+00:00: a) [1] Dropping (1969-12-31 23:59:59+00:00 - 1969-12-31 23:59:59.200000+00:00: b) [2] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.300000+00:00 -> 0.0 - 0.3) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_meas_date_first_samp_crop[None-10000] Creating RawArray with float64 data, n_channels=1, n_times=3000 Range : 10000 ... 12999 = 10.000 ... 12.999 secs Ready. Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:01.501000+00:00 [0] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.100000+00:00 -> 0.0 - 0.1) [1] Keeping (1970-01-01 00:00:01+00:00 - 1970-01-01 00:00:01.200000+00:00 -> 1.0 - 1.2) [2] Dropping (1970-01-01 00:00:02+00:00 - 1970-01-01 00:00:02.300000+00:00: c) Cropping complete (kept 2) Cropping annotations 1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.501000+00:00 [0] Dropping (1969-12-31 23:59:58+00:00 - 1969-12-31 23:59:58.100000+00:00: a) [1] Dropping (1969-12-31 23:59:59+00:00 - 1969-12-31 23:59:59.200000+00:00: b) [2] Keeping (1970-01-01 00:00:00+00:00 - 1970-01-01 00:00:00.300000+00:00 -> 0.0 - 0.3) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_meas_date_first_samp_crop[86400-0] Creating RawArray with float64 data, n_channels=1, n_times=3000 Range : 0 ... 2999 = 0.000 ... 2.999 secs Ready. Cropping annotations 1970-01-02 00:00:00+00:00 - 1970-01-02 00:00:01.501000+00:00 [0] Keeping (1970-01-02 00:00:00+00:00 - 1970-01-02 00:00:00.100000+00:00 -> 0.0 - 0.1) [1] Keeping (1970-01-02 00:00:01+00:00 - 1970-01-02 00:00:01.200000+00:00 -> 1.0 - 1.2) [2] Dropping (1970-01-02 00:00:02+00:00 - 1970-01-02 00:00:02.300000+00:00: c) Cropping complete (kept 2) Cropping annotations 1970-01-02 00:00:02+00:00 - 1970-01-02 00:00:02.501000+00:00 [0] Dropping (1970-01-02 00:00:00+00:00 - 1970-01-02 00:00:00.100000+00:00: a) [1] Dropping (1970-01-02 00:00:01+00:00 - 1970-01-02 00:00:01.200000+00:00: b) [2] Keeping (1970-01-02 00:00:02+00:00 - 1970-01-02 00:00:02.300000+00:00 -> 2.0 - 2.3) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_annot_meas_date_first_samp_crop[86400-10000] Creating RawArray with float64 data, n_channels=1, n_times=3000 Range : 10000 ... 12999 = 10.000 ... 12.999 secs Ready. Cropping annotations 1970-01-02 00:00:10+00:00 - 1970-01-02 00:00:11.501000+00:00 [0] Keeping (1970-01-02 00:00:10+00:00 - 1970-01-02 00:00:10.100000+00:00 -> 10.0 - 10.1) [1] Keeping (1970-01-02 00:00:11+00:00 - 1970-01-02 00:00:11.200000+00:00 -> 11.0 - 11.2) [2] Dropping (1970-01-02 00:00:12+00:00 - 1970-01-02 00:00:12.300000+00:00: c) Cropping complete (kept 2) Cropping annotations 1970-01-02 00:00:12+00:00 - 1970-01-02 00:00:12.501000+00:00 [0] Dropping (1970-01-02 00:00:10+00:00 - 1970-01-02 00:00:10.100000+00:00: a) [1] Dropping (1970-01-02 00:00:11+00:00 - 1970-01-02 00:00:11.200000+00:00: b) [2] Keeping (1970-01-02 00:00:12+00:00 - 1970-01-02 00:00:12.300000+00:00 -> 12.0 - 12.3) Cropping complete (kept 1) PASSED mne/tests/test_annotations.py::test_count_annotations PASSED mne/tests/test_bem.py::test_io_bem[fif] SKIPPED (Requires testing da...) mne/tests/test_bem.py::test_io_bem[h5] SKIPPED (Requires testing dat...) mne/tests/test_bem.py::test_make_sphere_model Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Fitted sphere radius: 91.0 mm Origin head coordinates: -4.1 16.0 51.7 mm Origin device coordinates: 1.4 17.8 -10.3 mm PASSED mne/tests/test_bem.py::test_make_bem_model[kwargs0-fname0] SKIPPED (...) mne/tests/test_bem.py::test_make_bem_model[kwargs1-fname1] SKIPPED (...) mne/tests/test_bem.py::test_bem_model_topology SKIPPED (Requires tes...) mne/tests/test_bem.py::test_bem_solution[cond0-fname0] SKIPPED (Requ...) mne/tests/test_bem.py::test_bem_solution[cond1-fname1] SKIPPED (Requ...) mne/tests/test_bem.py::test_fit_sphere_to_headshape Fitted sphere radius: 90.0 mm Origin head coordinates: 0.5 -10.0 40.0 mm Origin device coordinates: 0.5 -5.0 50.0 mm Fitted sphere radius: 90.0 mm Origin head coordinates: 0.5 -9.9 39.9 mm Origin device coordinates: 0.5 -4.9 49.9 mm Fitted sphere radius: 89.9 mm Origin head coordinates: 0.5 -9.9 40.0 mm Origin device coordinates: 0.5 -4.9 50.0 mm Fitted sphere radius: 120.0 mm Origin head coordinates: 0.5 -10.0 40.0 mm Origin device coordinates: 0.5 -5.0 50.0 mm Fitted sphere radius: 90.0 mm Origin head coordinates: 0.0 -30.0 0.0 mm Origin device coordinates: 0.0 -25.0 10.0 mm Fitted sphere radius: 89.8 mm Origin head coordinates: 0.5 -9.9 40.1 mm Origin device coordinates: 0.5 -4.9 50.1 mm Fitted sphere radius: 89.8 mm Origin head coordinates: 0.5 -9.9 40.1 mm Origin device coordinates: 0.5 -4.9 50.1 mm Fitted sphere radius: 154.7 mm Origin head coordinates: 0.5 109.7 228.5 mm Origin device coordinates: 0.5 114.7 238.5 mm PASSED mne/tests/test_bem.py::test_io_head_bem SKIPPED (Requires testing da...) mne/tests/test_bem.py::test_make_scalp_surfaces_topology SKIPPED (co...) mne/tests/test_bem.py::test_distance_to_bem[1-bem] SKIPPED (Requires...) mne/tests/test_bem.py::test_distance_to_bem[1-sphere] SKIPPED (Requi...) mne/tests/test_bem.py::test_distance_to_bem[10-bem] SKIPPED (Require...) mne/tests/test_bem.py::test_distance_to_bem[10-sphere] SKIPPED (Requ...) mne/tests/test_chpi.py::test_chpi_adjust SKIPPED (Requires testing d...) mne/tests/test_chpi.py::test_read_write_head_pos SKIPPED (Requires t...) mne/tests/test_chpi.py::test_hpi_info SKIPPED (Requires testing dataset) mne/tests/test_chpi.py::test_calculate_chpi_positions_preload SKIPPED mne/tests/test_chpi.py::test_calculate_chpi_positions_vv SKIPPED (Re...) mne/tests/test_chpi.py::test_calculate_chpi_snr SKIPPED (Requires te...) mne/tests/test_chpi.py::test_calculate_chpi_positions_artemis SKIPPED mne/tests/test_chpi.py::test_warn_maxwell_filtered SKIPPED (Requires...) mne/tests/test_chpi.py::test_initial_fit_redo SKIPPED (Requires test...) mne/tests/test_chpi.py::test_calculate_head_pos_chpi_on_chpi5_in_one_second_steps SKIPPED mne/tests/test_chpi.py::test_calculate_head_pos_chpi_on_chpi5_in_shorter_steps SKIPPED mne/tests/test_chpi.py::test_simulate_calculate_head_pos_chpi Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Creating RawArray with float64 data, n_channels=315, n_times=1000 Range : 0 ... 999 = 0.000 ... 9.990 secs Ready. Using 4 HPI coils: 10 15 20 25 Hz cHPI status bits enabled and stored on channel: STI201 Read 306 MEG channels from info 105 coil definitions read Coordinate transformation: MEG device -> head 0.991420 -0.039936 -0.124467 -6.13 mm 0.060661 0.984012 0.167456 0.06 mm 0.115790 -0.173570 0.977991 64.74 mm 0.000000 0.000000 0.000000 1.00 MEG coil definitions created in head coordinates. Using 4 HPI coils: 10 15 20 25 Hz Line interference frequencies: Hz Using time window: 1000.0 ms Fitting 4 HPI coil locations at up to 10 time points (10.0 s duration) 0%| | cHPI amplitudes : 0/10 [00:00 5 Estimating covariance using EMPIRICAL Done. Number of samples used : 726 [done] Reducing data rank from 5 -> 5 Estimating covariance using EMPIRICAL Done. Number of samples used : 726 [done] Reducing data rank from 5 -> 5 Estimating covariance using EMPIRICAL Done. Number of samples used : 726 [done] Reducing data rank from 5 -> 5 Estimating covariance using EMPIRICAL Done. Number of samples used : 726 [done] Reducing data rank from 5 -> 5 Estimating covariance using EMPIRICAL Done. Number of samples used : 726 [done] Reducing data rank from 5 -> 5 Estimating covariance using EMPIRICAL Done. Number of samples used : 726 [done] Reducing data rank from 5 -> 5 Estimating covariance using EMPIRICAL Done. Number of samples used : 726 [done] PASSED mne/tests/test_cov.py::test_cov_order Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 8 projection items activated MAG regularization : 0.1 Created an SSP operator for MAG (dimension = 3) GRAD regularization : 0.1 EEG regularization : 0.1 Created an SSP operator for EEG (dimension = 1) 8 projection items activated MAG regularization : 0.1 Created an SSP operator for MAG (dimension = 3) GRAD regularization : 0.1 EEG regularization : 0.1 Created an SSP operator for EEG (dimension = 1) Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 305 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 361 (4 small eigenvalues omitted) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 305 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 361 (4 small eigenvalues omitted) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 305 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 361 (4 small eigenvalues omitted) Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 306 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 362 (4 small eigenvalues omitted) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 306 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 362 (4 small eigenvalues omitted) PASSED mne/tests/test_cov.py::test_ad_hoc_cov Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied 366 x 366 diagonal covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Overwriting existing file. 366 x 366 diagonal covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active PASSED mne/tests/test_cov.py::test_io_cov 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Overwriting existing file. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Overwriting existing file. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active PASSED mne/tests/test_cov.py::test_cov_estimation_on_raw[None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 366 x 366 full covariance (kind = 1) found. Using up to 1 segment Number of samples used : 14400 [done] Using up to 119 segments Number of samples used : 14280 [done] 366 x 366 full covariance (kind = 1) found. Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Using up to 1 segment Number of samples used : 14400 [done] Using up to 119 segments Number of samples used : 14280 [done] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Using up to 5 segments Number of samples used : 600 [done] Using up to 1 segment Rejecting epoch based on EOG : ['EOG 061'] PASSED mne/tests/test_cov.py::test_cov_estimation_on_raw[empirical] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 366 x 366 full covariance (kind = 1) found. Using up to 1 segment Number of samples used : 14400 [done] Using up to 119 segments Number of samples used : 14280 [done] 366 x 366 full covariance (kind = 1) found. Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Using up to 1 segment Number of samples used : 14400 [done] Using up to 119 segments Number of samples used : 14280 [done] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Using up to 5 segments Number of samples used : 600 [done] Using up to 1 segment Rejecting epoch based on EOG : ['EOG 061'] PASSED mne/tests/test_cov.py::test_cov_estimation_on_raw[shrunk] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 366 x 366 full covariance (kind = 1) found. Using up to 1 segment Using data from preloaded Raw for 1 events and 14400 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Reducing data rank from 366 -> 366 Estimating covariance using SHRUNK Done. Number of samples used : 14400 [done] Using up to 119 segments Using data from preloaded Raw for 119 events and 120 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Reducing data rank from 366 -> 366 Estimating covariance using SHRUNK Done. Number of samples used : 14280 [done] 366 x 366 full covariance (kind = 1) found. Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Using up to 1 segment Using data from preloaded Raw for 1 events and 14400 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 1) Reducing data rank from 5 -> 5 Estimating covariance using SHRUNK Done. Number of samples used : 14400 [done] Using up to 119 segments Using data from preloaded Raw for 119 events and 120 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 1) Reducing data rank from 5 -> 5 Estimating covariance using SHRUNK Done. Number of samples used : 14280 [done] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Using up to 5 segments Loading data for 5 events and 120 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 363 Estimating covariance using SHRUNK Done. Number of samples used : 600 [done] Using up to 1 segment Rejecting epoch based on EOG : ['EOG 061'] PASSED mne/tests/test_cov.py::test_cov_estimation_on_raw_reg Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Creating RawArray with float64 data, n_channels=376, n_times=1440 Range : 0 ... 1439 = 0.000 ... 23.959 secs Ready. 366 x 366 full covariance (kind = 1) found. Using up to 4 segments Using data from preloaded Raw for 4 events and 300 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 363 Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Done. Number of samples used : 1200 [done] PASSED mne/tests/test_cov.py::test_cov_estimation_with_triggers[full] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Not setting metadata 29 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Using data from preloaded Raw for 29 events and 121 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 006', 'EEG 007', 'EEG 053'] 1 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3388 [done] 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3052 [done] Not setting metadata 7 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 6 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Using data from preloaded Raw for 7 events and 121 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 8 events and 121 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 006', 'EEG 007', 'EEG 053'] 1 bad epochs dropped Using data from preloaded Raw for 8 events and 121 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 6 events and 121 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3388 [done] Using data from preloaded Raw for 7 events and 121 original time points ... Using data from preloaded Raw for 7 events and 121 original time points ... Using data from preloaded Raw for 8 events and 121 original time points ... Using data from preloaded Raw for 6 events and 121 original time points ... Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3388 [done] 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Not setting metadata 1 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 1 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) Using data from preloaded Raw for 1 events and 121 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 1 events and 121 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 179 Estimating covariance using EMPIRICAL Done. Number of samples used : 242 [done] Using data from preloaded Raw for 1 events and 121 original time points ... Using data from preloaded Raw for 1 events and 121 original time points ... Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 179 Estimating covariance using EMPIRICAL Done. Number of samples used : 242 [done] Not setting metadata 29 matching events found Setting baseline interval to [-0.009989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Using data from preloaded Raw for 29 events and 7 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 233 Estimating covariance using EMPIRICAL Done. Number of samples used : 203 [done] Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 233 Estimating covariance using EMPIRICAL Done. Number of samples used : 203 [done] PASSED mne/tests/test_cov.py::test_cov_estimation_with_triggers[None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Not setting metadata 29 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Using data from preloaded Raw for 29 events and 121 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 006', 'EEG 007', 'EEG 053'] 1 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3388 [done] 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3052 [done] Not setting metadata 7 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 6 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Using data from preloaded Raw for 7 events and 121 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 8 events and 121 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 006', 'EEG 007', 'EEG 053'] 1 bad epochs dropped Using data from preloaded Raw for 8 events and 121 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 6 events and 121 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3388 [done] Using data from preloaded Raw for 7 events and 121 original time points ... Using data from preloaded Raw for 7 events and 121 original time points ... Using data from preloaded Raw for 8 events and 121 original time points ... Using data from preloaded Raw for 6 events and 121 original time points ... Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 362 Estimating covariance using EMPIRICAL Done. Number of samples used : 3388 [done] 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Not setting metadata 1 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Not setting metadata 1 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) Using data from preloaded Raw for 1 events and 121 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 1 events and 121 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 179 Estimating covariance using EMPIRICAL Done. Number of samples used : 242 [done] Using data from preloaded Raw for 1 events and 121 original time points ... Using data from preloaded Raw for 1 events and 121 original time points ... Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 179 Estimating covariance using EMPIRICAL Done. Number of samples used : 242 [done] Not setting metadata 29 matching events found Setting baseline interval to [-0.009989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated Using data from preloaded Raw for 29 events and 7 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 233 Estimating covariance using EMPIRICAL Done. Number of samples used : 203 [done] Created an SSP operator (subspace dimension = 4) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 233 Estimating covariance using EMPIRICAL Done. Number of samples used : 203 [done] PASSED mne/tests/test_cov.py::test_arithmetic_cov 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active PASSED mne/tests/test_cov.py::test_regularize_cov Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 7 projection items activated MAG regularization : 0.1 Created an SSP operator for MAG (dimension = 3) GRAD regularization : 0.1 EEG regularization : 0.1 Created an SSP operator for EEG (dimension = 1) PASSED mne/tests/test_cov.py::test_whiten_evoked Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 8 projection items activated MAG regularization : 0.1 Created an SSP operator for MAG (dimension = 3) GRAD regularization : 0.1 EEG regularization : 0.1 Created an SSP operator for EEG (dimension = 1) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 306 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 1.3e-14 (2.2e-16 eps * 60 dim * 0.97 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 362 (4 small eigenvalues omitted) PASSED mne/tests/test_cov.py::test_regularized_covariance Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Estimating covariance using EMPIRICAL Done. PASSED mne/tests/test_cov.py::test_auto_low_rank ... rank: 4 - loglik: -189.749 ... rank: 5 - loglik: -187.060 ... rank: 6 - loglik: -187.067 ... best model at rank = 5 ... rank: 15 - loglik: -9.393 ... best model at rank = 15 PASSED mne/tests/test_cov.py::test_compute_covariance_auto_reg[full] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Not setting metadata 29 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 21 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Reducing data rank from 10 -> 10 Estimating covariance using SHRUNK Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 Done. Estimating covariance using EMPIRICAL Done. Estimating covariance using FACTOR_ANALYSIS ... rank: 3 - loglik: -53.394 ... best model at rank = 3 Done. Using cross-validation to select the best estimator. MAG regularization : 0.1 MAG regularization : 0.1 MAG regularization : 0.1 Number of samples used : 105 log-likelihood on unseen data (descending order): shrunk: -41.844 diagonal_fixed: -51.895 factor_analysis: -53.394 empirical: -102.380 [done] Created an SSP operator (subspace dimension = 3) Reducing data rank from 10 -> 10 Estimating covariance using EMPIRICAL Done. Estimating covariance using LEDOIT_WOLF Done. Estimating covariance using OAS Done. Estimating covariance using SHRUNK Done. Estimating covariance using SHRINKAGE Done. Estimating covariance using FACTOR_ANALYSIS ... rank: 3 - loglik: -53.394 ... best model at rank = 3 Done. Estimating covariance using PCA ... rank: 3 - loglik: -54.218 ... best model at rank = 3 Done. Using cross-validation to select the best estimator. Number of samples used : 105 log-likelihood on unseen data (descending order): shrunk: -41.844 oas: -50.466 ledoit_wolf: -50.861 shrinkage: -51.908 factor_analysis: -53.394 pca: -54.218 empirical: -102.380 selecting best estimator: shrunk [done] PASSED mne/tests/test_cov.py::test_compute_covariance_auto_reg[None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Not setting metadata 29 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 21 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Setting small MAG eigenvalues to zero (without PCA) Reducing data rank from 10 -> 7 Estimating covariance using SHRUNK Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 Done. Estimating covariance using EMPIRICAL Done. Using cross-validation to select the best estimator. MAG regularization : 0.1 MAG regularization : 0.1 MAG regularization : 0.1 Number of samples used : 105 log-likelihood on unseen data (descending order): diagonal_fixed: -39.410 shrunk: -39.682 empirical: -39.683 [done] Created an SSP operator (subspace dimension = 3) Setting small MAG eigenvalues to zero (without PCA) Reducing data rank from 10 -> 7 Estimating covariance using EMPIRICAL Done. Estimating covariance using LEDOIT_WOLF Done. Estimating covariance using OAS Done. Estimating covariance using SHRUNK Done. Estimating covariance using SHRINKAGE Done. Using cross-validation to select the best estimator. Number of samples used : 105 log-likelihood on unseen data (descending order): shrinkage: -39.404 ledoit_wolf: -39.409 oas: -39.439 shrunk: -39.682 empirical: -39.683 selecting best estimator: shrinkage [done] PASSED mne/tests/test_cov.py::test_compute_covariance_auto_reg[info] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] Not setting metadata 29 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 29 events and 21 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Setting small MAG eigenvalues to zero (without PCA) Reducing data rank from 10 -> 7 Estimating covariance using SHRUNK Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 Done. Estimating covariance using EMPIRICAL Done. Using cross-validation to select the best estimator. MAG regularization : 0.1 MAG regularization : 0.1 MAG regularization : 0.1 Number of samples used : 105 log-likelihood on unseen data (descending order): diagonal_fixed: -39.410 shrunk: -39.682 empirical: -39.683 [done] Created an SSP operator (subspace dimension = 3) Setting small MAG eigenvalues to zero (without PCA) Reducing data rank from 10 -> 7 Estimating covariance using EMPIRICAL Done. Estimating covariance using LEDOIT_WOLF Done. Estimating covariance using OAS Done. Estimating covariance using SHRUNK Done. Estimating covariance using SHRINKAGE Done. Using cross-validation to select the best estimator. Number of samples used : 105 log-likelihood on unseen data (descending order): shrinkage: -39.404 ledoit_wolf: -39.409 oas: -39.439 shrunk: -39.682 empirical: -39.683 selecting best estimator: shrinkage [done] PASSED mne/tests/test_cov.py::test_low_rank_methods[None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Maxwell filtering raw data No bad MEG channels Processing 204 gradiometers and 102 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Loading raw data from disk Processing 1 data chunk [done] Not setting metadata 3 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 1) 1 projection items activated Using data from preloaded Raw for 3 events and 121 original time points ... 1 bad epochs dropped Created an SSP operator (subspace dimension = 1) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 139 Estimating covariance using EMPIRICAL Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Done. Estimating covariance using OAS Done. Using cross-validation to select the best estimator. MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Number of samples used : 242 log-likelihood on unseen data (descending order): oas: -649.968 diagonal_fixed: -662.422 empirical: -14612.610 [done] Computing rank from covariance with rank=None Using tolerance 4.5e-12 (2.2e-16 eps * 306 dim * 66 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.2e-14 (2.2e-16 eps * 60 dim * 5.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 68 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector PASSED mne/tests/test_cov.py::test_low_rank_methods[full] Created an SSP operator (subspace dimension = 1) Reducing data rank from 366 -> 366 Estimating covariance using EMPIRICAL Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Done. Estimating covariance using OAS Done. Using cross-validation to select the best estimator. MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Number of samples used : 242 log-likelihood on unseen data (descending order): oas: -1513.269 diagonal_fixed: -1697.129 empirical: -17280.513 [done] Computing rank from covariance with rank=None Using tolerance 4.5e-12 (2.2e-16 eps * 306 dim * 66 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 7.2e-14 (2.2e-16 eps * 60 dim * 5.4 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 0 projectors PASSED mne/tests/test_cov.py::test_low_rank_methods[info] Created an SSP operator (subspace dimension = 1) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 139 Estimating covariance using EMPIRICAL Done. Estimating covariance using DIAGONAL_FIXED MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Done. Estimating covariance using OAS Done. Using cross-validation to select the best estimator. MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Number of samples used : 242 log-likelihood on unseen data (descending order): oas: -649.968 diagonal_fixed: -662.422 empirical: -14612.610 [done] Computing rank from covariance with rank=None Using tolerance 4.5e-12 (2.2e-16 eps * 306 dim * 66 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.2e-14 (2.2e-16 eps * 60 dim * 5.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 68 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector PASSED mne/tests/test_cov.py::test_low_rank_cov Created an SSP operator (subspace dimension = 1) Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Reducing data rank from 366 -> 139 Estimating covariance using EMPIRICAL Done. Number of samples used : 242 [done] Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector 2 projection items activated MAG regularization : 0.1 GRAD regularization : 0.1 EEG regularization : 0.1 Created an SSP operator for EEG (dimension = 1) Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 68 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Regularizing MEG channels jointly Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 67 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector EEG regularization : 0.1 Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank={'meg': 80, 'eeg': 59} Setting small EEG eigenvalues to zero (without PCA) MEG regularization : 0.1 Computing rank from covariance with rank={'meg': 80, 'eeg': 59} Setting small MEG eigenvalues to zero (without PCA) Computing rank from covariance with rank=None Using tolerance 4.6e-12 (2.2e-16 eps * 306 dim * 68 max singular value) Estimated rank (mag + grad): 80 MEG: rank 80 computed from 306 data channels with 0 projectors Using tolerance 7.5e-14 (2.2e-16 eps * 60 dim * 5.6 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.5e-12 (2.2e-16 eps * 306 dim * 66 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Reducing data rank from 306 -> 306 Estimating covariance using OAS Done. Number of samples used : 242 [done] Computing rank from covariance with rank=None Using tolerance 4.5e-12 (2.2e-16 eps * 306 dim * 66 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 4.5e-12 (2.2e-16 eps * 306 dim * 66 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/3 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-3.002-PCA-01 (exp var=97.6%) Adding projection: eeg-Raw-0.000-3.002-PCA-02 (exp var=1.5%) 2 projection items deactivated Not setting metadata 3 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 121 original time points ... 1 bad epochs dropped Computing rank from covariance with rank=None Using tolerance 4e-14 (2.2e-16 eps * 60 dim * 3 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 60 data channels with 3 projectors 6 projection items activated EEG regularization : 0.1 Created an SSP operator for EEG (dimension = 3) Computing rank from covariance with rank=None Using tolerance 4e-14 (2.2e-16 eps * 60 dim * 3 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 60 data channels with 3 projectors Computing rank from covariance with rank=None Using tolerance 4e-14 (2.2e-16 eps * 60 dim * 3 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 60 data channels with 3 projectors EEG regularization : 0.1 Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank={'eeg': 57} Setting small EEG eigenvalues to zero (without PCA) Computing rank from covariance with rank=None Using tolerance 4e-14 (2.2e-16 eps * 60 dim * 3 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 60 data channels with 3 projectors Computing rank from covariance with rank=None Using tolerance 4e-14 (2.2e-16 eps * 60 dim * 3 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 60 data channels with 3 projectors PASSED mne/tests/test_cov.py::test_cov_ctf SKIPPED (Requires testing dataset) mne/tests/test_cov.py::test_equalize_channels Identifying common channels ... Dropped the following channels: ['CH3', 'CH5', 'CH4'] PASSED mne/tests/test_cov.py::test_compute_whitener_rank Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Computing rank from covariance with rank=None Using tolerance 1.7e-16 (2.2e-16 eps * 306 dim * 0.0025 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 306 (0 small eigenvalues omitted) Computing rank from covariance with rank=None Using tolerance 1.7e-16 (2.2e-16 eps * 306 dim * 0.0025 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.7e-16 (2.2e-16 eps * 306 dim * 0.0025 max singular value) Estimated rank (mag + grad): 305 MEG: rank 305 computed from 306 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 305 (1 small eigenvalues omitted) Computing rank from covariance with rank=None Using tolerance 1.7e-16 (2.2e-16 eps * 306 dim * 0.0025 max singular value) Estimated rank (mag + grad): 305 MEG: rank 305 computed from 306 data channels with 0 projectors Computing rank from covariance with rank={'meg': 306} Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 306 (0 small eigenvalues omitted) PASSED mne/tests/test_cov.py::test_reg_rank Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Computing rank from covariance with rank=None Using tolerance 4.8e-13 (2.2e-16 eps * 305 dim * 7.1 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 4.8e-13 (2.2e-16 eps * 305 dim * 7.1 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector 8 projection items activated MAG regularization : 0.1 Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank={'meg': 302, 'eeg': 58} Using tolerance 3.7e-14 (2.2e-16 eps * 102 dim * 1.6 max singular value) Estimated rank (mag): 99 MAG: rank 99 computed from 102 data channels with 3 projectors Setting small MAG eigenvalues to zero (without PCA) GRAD regularization : 0.1 Computing rank from covariance with rank={'meg': 302, 'eeg': 58, 'mag': 99} Using tolerance 2.6e-13 (2.2e-16 eps * 203 dim * 5.8 max singular value) Estimated rank (grad): 203 GRAD: rank 203 computed from 203 data channels with 0 projectors Setting small GRAD eigenvalues to zero (without PCA) EEG regularization : 0.1 Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank={'meg': 302, 'eeg': 58, 'mag': 99, 'grad': 203} Setting small EEG eigenvalues to zero (without PCA) Computing rank from covariance with rank=None Using tolerance 4.8e-13 (2.2e-16 eps * 305 dim * 7.1 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 5.8e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector PASSED mne/tests/test_defaults.py::test_handle_default PASSED mne/tests/test_defaults.py::test_si_units PASSED mne/tests/test_defaults.py::test_consistency[si_units] PASSED mne/tests/test_defaults.py::test_consistency[color] PASSED mne/tests/test_defaults.py::test_consistency[scalings] PASSED mne/tests/test_defaults.py::test_consistency[scalings_plot_raw] PASSED mne/tests/test_dipole.py::test_io_dipoles SKIPPED (Requires testing ...) mne/tests/test_dipole.py::test_dipole_fitting_ctf SKIPPED (Requires ...) mne/tests/test_dipole.py::test_dipole_fitting SKIPPED (Requires MNE-C) mne/tests/test_dipole.py::test_dipole_fitting_fixed SKIPPED (Require...) mne/tests/test_dipole.py::test_len_index_dipoles SKIPPED (Requires t...) mne/tests/test_dipole.py::test_min_distance_fit_dipole SKIPPED (Requ...) mne/tests/test_dipole.py::test_accuracy SKIPPED (Requires testing da...) mne/tests/test_dipole.py::test_dipole_fixed SKIPPED (Requires testin...) mne/tests/test_dipole.py::test_get_phantom_dipoles[vectorview-32] PASSED mne/tests/test_dipole.py::test_get_phantom_dipoles[otaniemi-32] PASSED mne/tests/test_dipole.py::test_get_phantom_dipoles[oyama-50] PASSED mne/tests/test_dipole.py::test_confidence SKIPPED (Requires testing ...) mne/tests/test_dipole.py::test_bdip[fname_dip_0-fname_bdip_0] SKIPPED mne/tests/test_dipole.py::test_bdip[fname_dip_1-fname_bdip_1] SKIPPED mne/tests/test_docstring_parameters.py::test_docstring_parameters SKIPPED mne/tests/test_docstring_parameters.py::test_tabs SKIPPED (could not...) mne/tests/test_docstring_parameters.py::test_documented PASSED mne/tests/test_docstring_parameters.py::test_docdict_order PASSED mne/tests/test_epochs.py::test_event_repeated Creating RawArray with float64 data, n_channels=2, n_times=100 Range : 0 ... 99 = 0.000 ... 0.099 secs Ready. Multiple event values for single event times found. Keeping the first occurrence and dropping all others. Not setting metadata 1 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Multiple event values for single event times found. Creating new event value to reflect simultaneous events. Not setting metadata 1 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated PASSED mne/tests/test_epochs.py::test_handle_event_repeated Multiple event values for single event times found. Keeping the first occurrence and dropping all others. Multiple event values for single event times found. Creating new event value to reflect simultaneous events. Multiple event values for single event times found. Creating new event value to reflect simultaneous events. Multiple event values for single event times found. Creating new event value to reflect simultaneous events. Multiple event values for single event times found. Creating new event value to reflect simultaneous events. Multiple event values for single event times found. Creating new event value to reflect simultaneous events. Multiple event values for single event times found. Creating new event value to reflect simultaneous events. Multiple event values for single event times found. Creating new event value to reflect simultaneous events. PASSED mne/tests/test_epochs.py::test_get_data_copy Not setting metadata 29 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 29 events and 421 original time points ... 1 bad epochs dropped Data is self data: True PASSED mne/tests/test_epochs.py::test_hierarchical Not setting metadata 29 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 29 events and 421 original time points ... 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_average_movements SKIPPED (Requires t...) mne/tests/test_epochs.py::test_reject Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 7 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 7 events and 421 original time points ... Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped Using data from preloaded Raw for 6 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 7 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 7 events and 421 original time points ... Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped Using data from preloaded Raw for 6 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 7 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 7 events and 421 original time points ... Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped Using data from preloaded Raw for 6 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] Rejecting flat epoch based on MAG : ['MEG 0121', 'MEG 0231', 'MEG 0341', 'MEG 0511', 'MEG 0621', 'MEG 0731', 'MEG 0921', 'MEG 1031', 'MEG 1141', 'MEG 1311', 'MEG 1421', 'MEG 1531', 'MEG 1641', 'MEG 1811', 'MEG 1921', 'MEG 2031', 'MEG 2141', 'MEG 2311', 'MEG 2421', 'MEG 2531', 'MEG 2641'] 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 3 bad epochs dropped Using data from preloaded Raw for 3 events and 421 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 3 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated PASSED mne/tests/test_epochs.py::test_reject_by_annotations_reject_tmin_reject_tmax Creating RawArray with float64 data, n_channels=1, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-1.0, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 1 events and 2001 original time points ... 1 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-1.0, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 1 events and 2001 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-1.0, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 1 events and 2001 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_own_data Not setting metadata 10 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 10 events and 421 original time points ... 0 bad epochs dropped Rejecting flat epoch based on EEG : ['EEG 053'] Rejecting flat epoch based on EEG : ['EEG 053'] Rejecting flat epoch based on EEG : ['EEG 053'] Rejecting flat epoch based on EEG : ['EEG 053'] 4 bad epochs dropped Created an SSP operator (subspace dimension = 3) 3 projection items activated Rejecting flat epoch based on EEG : ['EEG 053'] Rejecting flat epoch based on EEG : ['EEG 053'] Rejecting flat epoch based on EEG : ['EEG 053'] 3 bad epochs dropped PASSED mne/tests/test_epochs.py::test_decim Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 301 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 301 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 301 original time points ... Using data from preloaded Raw for 2 events and 301 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 301 original time points ... Using data from preloaded Raw for 2 events and 301 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 301 original time points ... Using data from preloaded Raw for 2 events and 301 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 301 original time points ... Using data from preloaded Raw for 2 events and 301 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 301 original time points ... Using data from preloaded Raw for 2 events and 301 original time points (prior to decimation) ... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 302 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 302 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 302 original time points ... Using data from preloaded Raw for 2 events and 302 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 302 original time points ... Using data from preloaded Raw for 2 events and 302 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 302 original time points ... Using data from preloaded Raw for 2 events and 302 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 302 original time points ... Using data from preloaded Raw for 2 events and 302 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 302 original time points ... Using data from preloaded Raw for 2 events and 302 original time points (prior to decimation) ... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 303 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 303 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 303 original time points ... Using data from preloaded Raw for 2 events and 303 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 303 original time points ... Using data from preloaded Raw for 2 events and 303 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 303 original time points ... Using data from preloaded Raw for 2 events and 303 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 303 original time points ... Using data from preloaded Raw for 2 events and 303 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 303 original time points ... Using data from preloaded Raw for 2 events and 303 original time points (prior to decimation) ... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 304 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 304 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 304 original time points ... Using data from preloaded Raw for 2 events and 304 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 304 original time points ... Using data from preloaded Raw for 2 events and 304 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 304 original time points ... Using data from preloaded Raw for 2 events and 304 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 304 original time points ... Using data from preloaded Raw for 2 events and 304 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 304 original time points ... Using data from preloaded Raw for 2 events and 304 original time points (prior to decimation) ... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 305 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 305 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 305 original time points ... Using data from preloaded Raw for 2 events and 305 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 305 original time points ... Using data from preloaded Raw for 2 events and 305 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 305 original time points ... Using data from preloaded Raw for 2 events and 305 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 305 original time points ... Using data from preloaded Raw for 2 events and 305 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 305 original time points ... Using data from preloaded Raw for 2 events and 305 original time points (prior to decimation) ... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 306 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 306 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 306 original time points ... Using data from preloaded Raw for 2 events and 306 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 306 original time points ... Using data from preloaded Raw for 2 events and 306 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 306 original time points ... Using data from preloaded Raw for 2 events and 306 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 306 original time points ... Using data from preloaded Raw for 2 events and 306 original time points (prior to decimation) ... Using data from preloaded Raw for 2 events and 306 original time points ... Using data from preloaded Raw for 2 events and 306 original time points (prior to decimation) ... Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_base_epochs Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_savgol_filter Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Using savgol length 31 PASSED mne/tests/test_epochs.py::test_filter Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 199 samples (0.331 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.4s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.6s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 0.7s Reading /tmp/pytest-of-pbuilder1/pytest-0/test_filter0/test-epo.fif ... Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied 0 projection items activated Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 199 samples (0.331 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 647 tasks | elapsed: 0.4s [Parallel(n_jobs=1)]: Done 881 tasks | elapsed: 0.6s [Parallel(n_jobs=1)]: Done 1151 tasks | elapsed: 0.7s PASSED mne/tests/test_epochs.py::test_epochs_from_annotations Used Annotations descriptions: ['1', '2', '3', '32', '4', '5'] Used Annotations descriptions: ['1', '2', '3', '32', '4', '5'] Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_hash Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_event_ordering Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_events_type PASSED mne/tests/test_epochs.py::test_rescale Applying baseline correction (mode: mean) Applying baseline correction (mode: ratio) Applying baseline correction (mode: logratio) Applying baseline correction (mode: percent) Applying baseline correction (mode: zscore) Applying baseline correction (mode: zlogratio) PASSED mne/tests/test_epochs.py::test_epochs_baseline_basic[True] Creating RawArray with float64 data, n_channels=2, n_times=2 Range : 0 ... 1 = 0.000 ... 0.001 secs Ready. Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 2 original time points ... 0 bad epochs dropped Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Tru0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Applying baseline correction (mode: mean) Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Tru0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Tru0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Applying baseline correction (mode: mean) Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Tru0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_baseline_basic[False] Creating RawArray with float64 data, n_channels=2, n_times=2 Range : 0 ... 1 = 0.000 ... 0.001 secs Ready. Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 1 events and 2 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 1 events and 2 original time points ... Applying baseline correction (mode: mean) Using data from preloaded Raw for 1 events and 2 original time points ... Applying baseline correction (mode: mean) Using data from preloaded Raw for 1 events and 2 original time points ... Applying baseline correction (mode: mean) No baseline correction applied Applying baseline correction (mode: mean) Using data from preloaded Raw for 1 events and 2 original time points ... Using data from preloaded Raw for 1 events and 2 original time points ... Using data from preloaded Raw for 1 events and 2 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Fal0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Applying baseline correction (mode: mean) Loading data for 1 events and 2 original time points ... Overwriting existing file. Loading data for 1 events and 2 original time points ... Overwriting existing file. Loading data for 1 events and 2 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Fal0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Loading data for 1 events and 2 original time points ... Overwriting existing file. Using data from preloaded Raw for 1 events and 2 original time points ... Overwriting existing file. Using data from preloaded Raw for 1 events and 2 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Fal0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Applying baseline correction (mode: mean) Loading data for 1 events and 2 original time points ... Overwriting existing file. Loading data for 1 events and 2 original time points ... Overwriting existing file. Loading data for 1 events and 2 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_basic_Fal0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Loading data for 1 events and 2 original time points ... PASSED mne/tests/test_epochs.py::test_epochs_bad_baseline Not setting metadata 31 matching events found Not setting metadata 31 matching events found Not setting metadata 31 matching events found Not setting metadata 31 matching events found Not setting metadata 31 matching events found Not setting metadata 31 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 31 events and 121 original time points ... 0 bad epochs dropped Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) PASSED mne/tests/test_epochs.py::test_epoch_combine_ids Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_epoch_multi_ids Not setting metadata 30 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 15 events and 421 original time points ... 0 bad epochs dropped Loading data for 15 events and 421 original time points ... 0 bad epochs dropped Loading data for 15 events and 421 original time points ... 0 bad epochs dropped Loading data for 15 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_read_epochs_bad_events Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_io_epochs_basic Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_epochs_io_proj[True] Not setting metadata 24 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 24 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 015'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Projections have already been applied. Setting proj attribute to True. Loading data for 16 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Loading data for 16 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_proj_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... Loading data for 16 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Loading data for 13 events and 421 original time points ... Loading data for 13 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_proj_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_epochs_io_proj[delayed] Not setting metadata 24 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Loading data for 24 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 015'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Leaving delayed SSP mode. Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... SSP projectors applied... Loading data for 1 events and 421 original time points ... Loading data for 16 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_proj_delayed_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... Loading data for 16 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Loading data for 13 events and 421 original time points ... Loading data for 13 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_proj_delayed_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_epochs_io_proj[False] Not setting metadata 24 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 24 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on MAG : ['MEG 0111', 'MEG 1411', 'MEG 1421'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 015'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... SSP projectors applied... Loading data for 1 events and 421 original time points ... Loading data for 16 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_proj_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... Loading data for 16 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Loading data for 13 events and 421 original time points ... Loading data for 13 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_proj_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 16 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_epochs_io_preload[False] Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test_no_bl-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/foo-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Applying baseline correction (mode: mean) Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/foo-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = 1.66 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 300 original time points ... Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Loading data for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Loading data for 1 events and 421 original time points ... Overwriting existing file. Loading data for 3 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 3 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Dropped 0 epochs: Dropped 2 epochs: 1, 2 Overwriting existing file. Loading data for 1 events and 421 original time points ... Overwriting existing file. Loading data for 1 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-bad-name.fif.gz ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... Overwriting existing file. Loading data for 7 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Overwriting existing file. Loading data for 1 events and 421 original time points ... Splitting into 2 parts Overwriting existing file. Loading data for 4 events and 421 original time points ... Loading data for 3 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo-1.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_False_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = 0.00 ... 0.00 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_io_preload[True] Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test_no_bl-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/foo-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Applying baseline correction (mode: mean) Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/foo-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = 1.66 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Using data from preloaded Raw for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Using data from preloaded Raw for 1 events and 421 original time points ... Overwriting existing file. Using data from preloaded Raw for 3 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 421 original time points ... Dropped 0 epochs: Dropped 2 epochs: 1, 2 Overwriting existing file. Using data from preloaded Raw for 1 events and 421 original time points ... Overwriting existing file. Using data from preloaded Raw for 1 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... Using data from preloaded Raw for 1 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-bad-name.fif.gz ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Using data from preloaded Raw for 7 events and 421 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 1 events and 421 original time points ... Overwriting existing file. Using data from preloaded Raw for 7 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Using data from preloaded Raw for 7 events and 421 original time points ... Overwriting existing file. Using data from preloaded Raw for 1 events and 421 original time points ... Splitting into 2 parts Overwriting existing file. Using data from preloaded Raw for 4 events and 421 original time points ... Using data from preloaded Raw for 3 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo-1.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 7 events and 421 original time points ... Using data from preloaded Raw for 7 events and 421 original time points ... Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_io_preload_True_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = 0.00 ... 0.00 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split0-preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split0-no_preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split1-preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split1-no_preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split2-preload] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Splitting into 5 parts Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_4/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_4/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_4/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_4/test-epo-3.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_4/test-epo-4.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 8 matching events found No baseline correction applied 0 projection items activated FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split2-no_preload] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Splitting into 5 parts Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_5/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_5/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_5/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_5/test-epo-3.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_5/test-epo-4.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 8 matching events found No baseline correction applied 0 projection items activated FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split3-preload] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 8 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 5 parts Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_6/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_6/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_6/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_6/test-epo-3.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_6/test-epo-4.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 8 matching events found No baseline correction applied 0 projection items activated FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split3-no_preload] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 8 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 5 parts Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_7/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_7/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_7/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_7/test-epo-3.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_7/test-epo-4.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 8 matching events found No baseline correction applied 0 projection items activated FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split4-preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split4-no_preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split5-preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split5-no_preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split6-preload] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Splitting into 2 parts Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_12/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_12/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 14 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split6-no_preload] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Splitting into 2 parts Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_13/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_13/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 14 matching events found No baseline correction applied 0 projection items activated Loading data for 14 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split7-preload] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 14 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 2 parts Using data from preloaded Raw for 7 events and 701 original time points ... Using data from preloaded Raw for 7 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_14/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_14/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 14 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split7-no_preload] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 14 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 2 parts Using data from preloaded Raw for 7 events and 701 original time points ... Using data from preloaded Raw for 7 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_15/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_15/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 14 matching events found No baseline correction applied 0 projection items activated Loading data for 14 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split8-preload] Creating RawArray with float64 data, n_channels=100, n_times=16000 Range : 0 ... 15999 = 0.000 ... 15.999 secs Ready. Not setting metadata 16 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 16 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 15 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_16/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_16/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_16/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 15 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split8-no_preload] Creating RawArray with float64 data, n_channels=100, n_times=16000 Range : 0 ... 15999 = 0.000 ... 15.999 secs Ready. Not setting metadata 16 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 16 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 15 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_17/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_17/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_17/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 15 matching events found No baseline correction applied 0 projection items activated Loading data for 15 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split9-preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split9-no_preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split10-preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split10-no_preload] SKIPPED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split11-preload] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 18 matching events found Applying baseline correction (mode: mean) Splitting into 3 parts Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_22/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_22/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_22/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 18 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split11-no_preload] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 18 matching events found Applying baseline correction (mode: mean) Splitting into 3 parts Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_23/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_23/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_23/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 18 matching events found No baseline correction applied 0 projection items activated Loading data for 18 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split12-preload] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 18 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_24/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_24/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_24/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 18 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split12-no_preload] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 18 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_25/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_25/test-epo-1.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_25/test-epo-2.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 500.00 ms 0 CTF compensation matrices available Not setting metadata 18 matching events found No baseline correction applied 0 projection items activated Loading data for 18 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split0-neuromag] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split0-bids] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split0-mix] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split1-neuromag] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split1-bids] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split1-mix] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split2-neuromag] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Splitting into 5 parts FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split2-bids] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Splitting into 5 parts FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split2-mix] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 8 matching events found Applying baseline correction (mode: mean) Splitting into 5 parts FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split3-neuromag] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 5 parts Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split3-bids] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 5 parts Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split3-mix] Creating RawArray with float64 data, n_channels=100, n_times=9000 Range : 0 ... 8999 = 0.000 ... 8.999 secs Ready. Not setting metadata 9 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 9 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 5 parts Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 2 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split4-neuromag] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split4-bids] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split4-mix] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split5-neuromag] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split5-bids] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split5-mix] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split6-neuromag] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Splitting into 2 parts PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split6-bids] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Splitting into 2 parts SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split6-mix] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Splitting into 2 parts PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split7-neuromag] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 2 parts Using data from preloaded Raw for 7 events and 701 original time points ... Using data from preloaded Raw for 7 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split7-bids] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 2 parts Using data from preloaded Raw for 7 events and 701 original time points ... Using data from preloaded Raw for 7 events and 701 original time points ... SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split7-mix] Creating RawArray with float64 data, n_channels=100, n_times=15000 Range : 0 ... 14999 = 0.000 ... 14.999 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 15 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 2 parts Using data from preloaded Raw for 7 events and 701 original time points ... Using data from preloaded Raw for 7 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split8-neuromag] Creating RawArray with float64 data, n_channels=100, n_times=16000 Range : 0 ... 15999 = 0.000 ... 15.999 secs Ready. Not setting metadata 16 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 16 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split8-bids] Creating RawArray with float64 data, n_channels=100, n_times=16000 Range : 0 ... 15999 = 0.000 ... 15.999 secs Ready. Not setting metadata 16 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 16 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split8-mix] Creating RawArray with float64 data, n_channels=100, n_times=16000 Range : 0 ... 15999 = 0.000 ... 15.999 secs Ready. Not setting metadata 16 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 16 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... Using data from preloaded Raw for 5 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split9-neuromag] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split9-bids] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split9-mix] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split10-neuromag] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split10-bids] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split10-mix] SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split11-neuromag] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 18 matching events found Applying baseline correction (mode: mean) Splitting into 3 parts PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split11-bids] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 18 matching events found Applying baseline correction (mode: mean) Splitting into 3 parts SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split11-mix] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Not setting metadata 18 matching events found Applying baseline correction (mode: mean) Splitting into 3 parts PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split12-neuromag] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_naming[epochs_to_split12-bids] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... SKIPPED mne/tests/test_epochs.py::test_split_naming[epochs_to_split12-mix] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_saved_fname_no_splitting[test_epo.fif-neuromag-test_epo-1.fif] Creating RawArray with float64 data, n_channels=100, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 10 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 9 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_saved_fname_no_splitting[test_epo.fif-bids-test_split-01_epo.fif] Creating RawArray with float64 data, n_channels=100, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 10 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 9 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_bids_splits_fail_for_bad_fname_ending[test-epo.fif-epochs_to_split0] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts PASSED mne/tests/test_epochs.py::test_bids_splits_fail_for_bad_fname_ending[test-epo.fif-epochs_to_split1] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 18 events and 701 original time points ... XFAIL mne/tests/test_epochs.py::test_bids_splits_fail_for_bad_fname_ending[a_b_c-epo.fif-epochs_to_split0] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... XFAIL mne/tests/test_epochs.py::test_bids_splits_fail_for_bad_fname_ending[a_b_c-epo.fif-epochs_to_split1] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 18 events and 701 original time points ... XFAIL mne/tests/test_epochs.py::test_splits_overwrite[neuromag-test-epo.fif-test-epo.fif-epochs_to_split0] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_splits_overwrite[neuromag-test-epo.fif-test-epo-1.fif-epochs_to_split0] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_splits_overwrite[bids-test_epo.fif-test_epo.fif-epochs_to_split0] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_splits_overwrite[bids-test_epo.fif-test_split-01_epo.fif-epochs_to_split0] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts PASSED mne/tests/test_epochs.py::test_splits_overwrite[bids-test_epo.fif-test_split-02_epo.fif-epochs_to_split0] Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_split_many_reset Not setting metadata 1000 matching events found No baseline correction applied 0 projection items activated Data is self data: True Overhead size: 2364748 Splittable size: 4112000 Split size: 524288 Data is self data: True Overhead size: 2364748 Splittable size: 4112000 Split size: 1048576 Data is self data: True Overhead size: 2364748 Splittable size: 4112000 Split size: 2097152 Data is self data: True Overhead size: 2364748 Splittable size: 4112000 Split size: 2380267 Splitting into 265 parts Data is self data: True Overhead size: 2364748 Splittable size: 4112000 Split size: 3145728 Splitting into 6 parts Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-epo-1.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-epo-2.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-epo-3.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-epo-4.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-epo-5.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Not setting metadata 1000 matching events found No baseline correction applied 0 projection items activated Data is self data: True Overhead size: 1064748 Splittable size: 4112000 Split size: 2097152 Splitting into 4 parts Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Writing using normal I/O Data is self data: True Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-reset-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-reset-epo-1.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-reset-epo-2.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Reading /tmp/pytest-of-pbuilder1/pytest-0/test_split_many_reset0/temp-reset-epo-3.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 1023.00 ms 0 CTF compensation matrices available Not setting metadata 1000 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_proj Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped SSP projectors applied... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items deactivated Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped SSP projectors applied... Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_proj0/temp-epo.fif ... Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated 3 projection items deactivated Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_proj0/test-epo.fif ... Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_proj0/test-epo.fif ... Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_proj0/test-epo.fif ... Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated Loading data for 7 events and 421 original time points ... SSP projectors applied... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_proj0/test-epo.fif ... Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Loading data for 7 events and 421 original time points ... Created an SSP operator (subspace dimension = 1) 1 projection items activated Loading data for 7 events and 421 original time points ... SSP projectors applied... PASSED mne/tests/test_epochs.py::test_evoked_arithmetic Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_evoked_io_from_epochs Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_io_from_epochs0/evoked-ave.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -183.15 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [-0.183146, 0] s) Not setting metadata 1 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_io_from_epochs0/evoked-ave.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = 99.90 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [0.1, 0.2] s) Not setting metadata 1 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_io_from_epochs0/evoked-ave.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [0.1, 0.2] s) Loading data for 1 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Loading data for 1 events and 421 original time points (prior to decimation) ... Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_io_from_epochs0/evoked-ave.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [0.1, 0.2] s) Loading data for 1 events and 421 original time points (prior to decimation) ... Loading data for 1 events and 421 original time points (prior to decimation) ... Overwriting existing file. PASSED mne/tests/test_epochs.py::test_evoked_standard_error Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_standard_error0/evoked-ave.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [-0.199795, 0] s) Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 101 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [-0.199795, 0] s) Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_standard_error0/evoked-ave.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [-0.199795, 0] s) Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_standard_error0/evoked-ave.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms (1) 0 CTF compensation matrices available nave = 1 - aspect type = 101 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [-0.199795, 0] s) PASSED mne/tests/test_epochs.py::test_reject_epochs Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132', 'MEG 0133', 'MEG 0143', 'MEG 0142', 'MEG 0213', 'MEG 0212', 'MEG 0222', 'MEG 0223', 'MEG 0232', 'MEG 0233', 'MEG 0243', 'MEG 0242', 'MEG 0313', 'MEG 0312', 'MEG 0322', 'MEG 0323', 'MEG 0333', 'MEG 0332', 'MEG 0343', 'MEG 0342', 'MEG 0413', 'MEG 0412', 'MEG 0422', 'MEG 0423', 'MEG 0432', 'MEG 0433', 'MEG 0443', 'MEG 0442', 'MEG 0513', 'MEG 0512', 'MEG 0523', 'MEG 0522', 'MEG 0532', 'MEG 0533', 'MEG 0542', 'MEG 0543', 'MEG 0613', 'MEG 0612', 'MEG 0622', 'MEG 0623', 'MEG 0633', 'MEG 0632', 'MEG 0642', 'MEG 0643', 'MEG 0713', 'MEG 0712', 'MEG 0723', 'MEG 0722', 'MEG 0733', 'MEG 0732', 'MEG 0743', 'MEG 0742', 'MEG 0813', 'MEG 0812', 'MEG 0822', 'MEG 0823', 'MEG 0913', 'MEG 0912', 'MEG 0923', 'MEG 0922', 'MEG 0932', 'MEG 0933', 'MEG 0942', 'MEG 0943', 'MEG 1013', 'MEG 1012', 'MEG 1023', 'MEG 1022', 'MEG 1032', 'MEG 1033', 'MEG 1043', 'MEG 1042', 'MEG 1112', 'MEG 1113', 'MEG 1123', 'MEG 1122', 'MEG 1133', 'MEG 1132', 'MEG 1142', 'MEG 1143', 'MEG 1213', 'MEG 1212', 'MEG 1223', 'MEG 1222', 'MEG 1232', 'MEG 1233', 'MEG 1243', 'MEG 1242', 'MEG 1312', 'MEG 1313', 'MEG 1323', 'MEG 1322', 'MEG 1333', 'MEG 1332', 'MEG 1342', 'MEG 1343', 'MEG 1412', 'MEG 1413', 'MEG 1423', 'MEG 1422', 'MEG 1433', 'MEG 1432', 'MEG 1442', 'MEG 1443', 'MEG 1512', 'MEG 1513', 'MEG 1522', 'MEG 1523', 'MEG 1533', 'MEG 1532', 'MEG 1543', 'MEG 1542', 'MEG 1613', 'MEG 1612', 'MEG 1622', 'MEG 1623', 'MEG 1632', 'MEG 1633', 'MEG 1643', 'MEG 1642', 'MEG 1713', 'MEG 1712', 'MEG 1722', 'MEG 1723', 'MEG 1732', 'MEG 1733', 'MEG 1743', 'MEG 1742', 'MEG 1813', 'MEG 1812', 'MEG 1822', 'MEG 1823', 'MEG 1832', 'MEG 1833', 'MEG 1843', 'MEG 1842', 'MEG 1912', 'MEG 1913', 'MEG 1923', 'MEG 1922', 'MEG 1932', 'MEG 1933', 'MEG 1943', 'MEG 1942', 'MEG 2013', 'MEG 2012', 'MEG 2023', 'MEG 2022', 'MEG 2032', 'MEG 2033', 'MEG 2042', 'MEG 2043', 'MEG 2113', 'MEG 2112', 'MEG 2122', 'MEG 2123', 'MEG 2133', 'MEG 2132', 'MEG 2143', 'MEG 2142', 'MEG 2212', 'MEG 2213', 'MEG 2223', 'MEG 2222', 'MEG 2233', 'MEG 2232', 'MEG 2242', 'MEG 2243', 'MEG 2312', 'MEG 2313', 'MEG 2323', 'MEG 2322', 'MEG 2332', 'MEG 2333', 'MEG 2343', 'MEG 2342', 'MEG 2412', 'MEG 2413', 'MEG 2423', 'MEG 2422', 'MEG 2433', 'MEG 2432', 'MEG 2442', 'MEG 2512', 'MEG 2513', 'MEG 2522', 'MEG 2523', 'MEG 2533', 'MEG 2532', 'MEG 2543', 'MEG 2542', 'MEG 2612', 'MEG 2613', 'MEG 2623', 'MEG 2622', 'MEG 2633', 'MEG 2632', 'MEG 2642', 'MEG 2643'] Rejecting epoch based on GRAD : ['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132', 'MEG 0133', 'MEG 0143', 'MEG 0142', 'MEG 0213', 'MEG 0212', 'MEG 0222', 'MEG 0223', 'MEG 0232', 'MEG 0233', 'MEG 0243', 'MEG 0242', 'MEG 0313', 'MEG 0312', 'MEG 0322', 'MEG 0323', 'MEG 0333', 'MEG 0332', 'MEG 0343', 'MEG 0342', 'MEG 0413', 'MEG 0412', 'MEG 0422', 'MEG 0423', 'MEG 0432', 'MEG 0433', 'MEG 0443', 'MEG 0442', 'MEG 0513', 'MEG 0512', 'MEG 0523', 'MEG 0522', 'MEG 0532', 'MEG 0533', 'MEG 0542', 'MEG 0543', 'MEG 0613', 'MEG 0612', 'MEG 0622', 'MEG 0623', 'MEG 0633', 'MEG 0632', 'MEG 0642', 'MEG 0643', 'MEG 0713', 'MEG 0712', 'MEG 0723', 'MEG 0722', 'MEG 0733', 'MEG 0732', 'MEG 0743', 'MEG 0742', 'MEG 0813', 'MEG 0812', 'MEG 0822', 'MEG 0823', 'MEG 0913', 'MEG 0912', 'MEG 0923', 'MEG 0922', 'MEG 0932', 'MEG 0933', 'MEG 0942', 'MEG 0943', 'MEG 1013', 'MEG 1012', 'MEG 1023', 'MEG 1022', 'MEG 1032', 'MEG 1033', 'MEG 1043', 'MEG 1042', 'MEG 1112', 'MEG 1113', 'MEG 1123', 'MEG 1122', 'MEG 1133', 'MEG 1132', 'MEG 1142', 'MEG 1143', 'MEG 1213', 'MEG 1212', 'MEG 1223', 'MEG 1222', 'MEG 1232', 'MEG 1233', 'MEG 1243', 'MEG 1242', 'MEG 1312', 'MEG 1313', 'MEG 1323', 'MEG 1322', 'MEG 1333', 'MEG 1332', 'MEG 1342', 'MEG 1343', 'MEG 1412', 'MEG 1413', 'MEG 1423', 'MEG 1422', 'MEG 1433', 'MEG 1432', 'MEG 1442', 'MEG 1443', 'MEG 1512', 'MEG 1513', 'MEG 1522', 'MEG 1523', 'MEG 1533', 'MEG 1532', 'MEG 1543', 'MEG 1542', 'MEG 1613', 'MEG 1612', 'MEG 1622', 'MEG 1623', 'MEG 1632', 'MEG 1633', 'MEG 1643', 'MEG 1642', 'MEG 1713', 'MEG 1712', 'MEG 1722', 'MEG 1723', 'MEG 1732', 'MEG 1733', 'MEG 1743', 'MEG 1742', 'MEG 1813', 'MEG 1812', 'MEG 1822', 'MEG 1823', 'MEG 1832', 'MEG 1833', 'MEG 1843', 'MEG 1842', 'MEG 1912', 'MEG 1913', 'MEG 1923', 'MEG 1922', 'MEG 1932', 'MEG 1933', 'MEG 1943', 'MEG 1942', 'MEG 2013', 'MEG 2012', 'MEG 2023', 'MEG 2022', 'MEG 2032', 'MEG 2033', 'MEG 2042', 'MEG 2043', 'MEG 2113', 'MEG 2112', 'MEG 2122', 'MEG 2123', 'MEG 2133', 'MEG 2132', 'MEG 2143', 'MEG 2142', 'MEG 2212', 'MEG 2213', 'MEG 2223', 'MEG 2222', 'MEG 2233', 'MEG 2232', 'MEG 2242', 'MEG 2243', 'MEG 2312', 'MEG 2313', 'MEG 2323', 'MEG 2322', 'MEG 2332', 'MEG 2333', 'MEG 2343', 'MEG 2342', 'MEG 2412', 'MEG 2413', 'MEG 2423', 'MEG 2422', 'MEG 2433', 'MEG 2432', 'MEG 2442', 'MEG 2512', 'MEG 2513', 'MEG 2522', 'MEG 2523', 'MEG 2533', 'MEG 2532', 'MEG 2543', 'MEG 2542', 'MEG 2612', 'MEG 2613', 'MEG 2623', 'MEG 2622', 'MEG 2633', 'MEG 2632', 'MEG 2642', 'MEG 2643'] Rejecting epoch based on GRAD : ['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132', 'MEG 0133', 'MEG 0143', 'MEG 0142', 'MEG 0213', 'MEG 0212', 'MEG 0222', 'MEG 0223', 'MEG 0232', 'MEG 0233', 'MEG 0243', 'MEG 0242', 'MEG 0313', 'MEG 0312', 'MEG 0322', 'MEG 0323', 'MEG 0333', 'MEG 0332', 'MEG 0343', 'MEG 0342', 'MEG 0413', 'MEG 0412', 'MEG 0422', 'MEG 0423', 'MEG 0432', 'MEG 0433', 'MEG 0443', 'MEG 0442', 'MEG 0513', 'MEG 0512', 'MEG 0523', 'MEG 0522', 'MEG 0532', 'MEG 0533', 'MEG 0542', 'MEG 0543', 'MEG 0613', 'MEG 0612', 'MEG 0622', 'MEG 0623', 'MEG 0633', 'MEG 0632', 'MEG 0642', 'MEG 0643', 'MEG 0713', 'MEG 0712', 'MEG 0723', 'MEG 0722', 'MEG 0733', 'MEG 0732', 'MEG 0743', 'MEG 0742', 'MEG 0813', 'MEG 0812', 'MEG 0822', 'MEG 0823', 'MEG 0913', 'MEG 0912', 'MEG 0923', 'MEG 0922', 'MEG 0932', 'MEG 0933', 'MEG 0942', 'MEG 0943', 'MEG 1013', 'MEG 1012', 'MEG 1023', 'MEG 1022', 'MEG 1032', 'MEG 1033', 'MEG 1043', 'MEG 1042', 'MEG 1112', 'MEG 1113', 'MEG 1123', 'MEG 1122', 'MEG 1133', 'MEG 1132', 'MEG 1142', 'MEG 1143', 'MEG 1213', 'MEG 1212', 'MEG 1223', 'MEG 1222', 'MEG 1232', 'MEG 1233', 'MEG 1243', 'MEG 1242', 'MEG 1312', 'MEG 1313', 'MEG 1323', 'MEG 1322', 'MEG 1333', 'MEG 1332', 'MEG 1342', 'MEG 1343', 'MEG 1412', 'MEG 1413', 'MEG 1423', 'MEG 1422', 'MEG 1433', 'MEG 1432', 'MEG 1442', 'MEG 1443', 'MEG 1512', 'MEG 1513', 'MEG 1522', 'MEG 1523', 'MEG 1533', 'MEG 1532', 'MEG 1543', 'MEG 1542', 'MEG 1613', 'MEG 1612', 'MEG 1622', 'MEG 1623', 'MEG 1632', 'MEG 1633', 'MEG 1643', 'MEG 1642', 'MEG 1713', 'MEG 1712', 'MEG 1722', 'MEG 1723', 'MEG 1732', 'MEG 1733', 'MEG 1743', 'MEG 1742', 'MEG 1813', 'MEG 1812', 'MEG 1822', 'MEG 1823', 'MEG 1832', 'MEG 1833', 'MEG 1843', 'MEG 1842', 'MEG 1912', 'MEG 1913', 'MEG 1923', 'MEG 1922', 'MEG 1932', 'MEG 1933', 'MEG 1943', 'MEG 1942', 'MEG 2013', 'MEG 2012', 'MEG 2023', 'MEG 2022', 'MEG 2032', 'MEG 2033', 'MEG 2042', 'MEG 2043', 'MEG 2113', 'MEG 2112', 'MEG 2122', 'MEG 2123', 'MEG 2133', 'MEG 2132', 'MEG 2143', 'MEG 2142', 'MEG 2212', 'MEG 2213', 'MEG 2223', 'MEG 2222', 'MEG 2233', 'MEG 2232', 'MEG 2242', 'MEG 2243', 'MEG 2312', 'MEG 2313', 'MEG 2323', 'MEG 2322', 'MEG 2332', 'MEG 2333', 'MEG 2343', 'MEG 2342', 'MEG 2412', 'MEG 2413', 'MEG 2423', 'MEG 2422', 'MEG 2433', 'MEG 2432', 'MEG 2442', 'MEG 2512', 'MEG 2513', 'MEG 2522', 'MEG 2523', 'MEG 2533', 'MEG 2532', 'MEG 2543', 'MEG 2542', 'MEG 2612', 'MEG 2613', 'MEG 2623', 'MEG 2622', 'MEG 2633', 'MEG 2632', 'MEG 2642', 'MEG 2643'] Rejecting epoch based on GRAD : ['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132', 'MEG 0133', 'MEG 0143', 'MEG 0142', 'MEG 0213', 'MEG 0212', 'MEG 0222', 'MEG 0223', 'MEG 0232', 'MEG 0233', 'MEG 0243', 'MEG 0242', 'MEG 0313', 'MEG 0312', 'MEG 0322', 'MEG 0323', 'MEG 0333', 'MEG 0332', 'MEG 0343', 'MEG 0342', 'MEG 0413', 'MEG 0412', 'MEG 0422', 'MEG 0423', 'MEG 0432', 'MEG 0433', 'MEG 0443', 'MEG 0442', 'MEG 0513', 'MEG 0512', 'MEG 0523', 'MEG 0522', 'MEG 0532', 'MEG 0533', 'MEG 0542', 'MEG 0543', 'MEG 0613', 'MEG 0612', 'MEG 0622', 'MEG 0623', 'MEG 0633', 'MEG 0632', 'MEG 0642', 'MEG 0643', 'MEG 0713', 'MEG 0712', 'MEG 0723', 'MEG 0722', 'MEG 0733', 'MEG 0732', 'MEG 0743', 'MEG 0742', 'MEG 0813', 'MEG 0812', 'MEG 0822', 'MEG 0823', 'MEG 0913', 'MEG 0912', 'MEG 0923', 'MEG 0922', 'MEG 0932', 'MEG 0933', 'MEG 0942', 'MEG 0943', 'MEG 1013', 'MEG 1012', 'MEG 1023', 'MEG 1022', 'MEG 1032', 'MEG 1033', 'MEG 1043', 'MEG 1042', 'MEG 1112', 'MEG 1113', 'MEG 1123', 'MEG 1122', 'MEG 1133', 'MEG 1132', 'MEG 1142', 'MEG 1143', 'MEG 1213', 'MEG 1212', 'MEG 1223', 'MEG 1222', 'MEG 1232', 'MEG 1233', 'MEG 1243', 'MEG 1242', 'MEG 1312', 'MEG 1313', 'MEG 1323', 'MEG 1322', 'MEG 1333', 'MEG 1332', 'MEG 1342', 'MEG 1343', 'MEG 1412', 'MEG 1413', 'MEG 1423', 'MEG 1422', 'MEG 1433', 'MEG 1432', 'MEG 1442', 'MEG 1443', 'MEG 1512', 'MEG 1513', 'MEG 1522', 'MEG 1523', 'MEG 1533', 'MEG 1532', 'MEG 1543', 'MEG 1542', 'MEG 1613', 'MEG 1612', 'MEG 1622', 'MEG 1623', 'MEG 1632', 'MEG 1633', 'MEG 1643', 'MEG 1642', 'MEG 1713', 'MEG 1712', 'MEG 1722', 'MEG 1723', 'MEG 1732', 'MEG 1733', 'MEG 1743', 'MEG 1742', 'MEG 1813', 'MEG 1812', 'MEG 1822', 'MEG 1823', 'MEG 1832', 'MEG 1833', 'MEG 1843', 'MEG 1842', 'MEG 1912', 'MEG 1913', 'MEG 1923', 'MEG 1922', 'MEG 1932', 'MEG 1933', 'MEG 1943', 'MEG 1942', 'MEG 2013', 'MEG 2012', 'MEG 2023', 'MEG 2022', 'MEG 2032', 'MEG 2033', 'MEG 2042', 'MEG 2043', 'MEG 2113', 'MEG 2112', 'MEG 2122', 'MEG 2123', 'MEG 2133', 'MEG 2132', 'MEG 2143', 'MEG 2142', 'MEG 2212', 'MEG 2213', 'MEG 2223', 'MEG 2222', 'MEG 2233', 'MEG 2232', 'MEG 2242', 'MEG 2243', 'MEG 2312', 'MEG 2313', 'MEG 2323', 'MEG 2322', 'MEG 2332', 'MEG 2333', 'MEG 2343', 'MEG 2342', 'MEG 2412', 'MEG 2413', 'MEG 2423', 'MEG 2422', 'MEG 2433', 'MEG 2432', 'MEG 2442', 'MEG 2512', 'MEG 2513', 'MEG 2522', 'MEG 2523', 'MEG 2533', 'MEG 2532', 'MEG 2543', 'MEG 2542', 'MEG 2612', 'MEG 2613', 'MEG 2623', 'MEG 2622', 'MEG 2633', 'MEG 2632', 'MEG 2642', 'MEG 2643'] Rejecting epoch based on GRAD : ['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132', 'MEG 0133', 'MEG 0143', 'MEG 0142', 'MEG 0213', 'MEG 0212', 'MEG 0222', 'MEG 0223', 'MEG 0232', 'MEG 0233', 'MEG 0243', 'MEG 0242', 'MEG 0313', 'MEG 0312', 'MEG 0322', 'MEG 0323', 'MEG 0333', 'MEG 0332', 'MEG 0343', 'MEG 0342', 'MEG 0413', 'MEG 0412', 'MEG 0422', 'MEG 0423', 'MEG 0432', 'MEG 0433', 'MEG 0443', 'MEG 0442', 'MEG 0513', 'MEG 0512', 'MEG 0523', 'MEG 0522', 'MEG 0532', 'MEG 0533', 'MEG 0542', 'MEG 0543', 'MEG 0613', 'MEG 0612', 'MEG 0622', 'MEG 0623', 'MEG 0633', 'MEG 0632', 'MEG 0642', 'MEG 0643', 'MEG 0713', 'MEG 0712', 'MEG 0723', 'MEG 0722', 'MEG 0733', 'MEG 0732', 'MEG 0743', 'MEG 0742', 'MEG 0813', 'MEG 0812', 'MEG 0822', 'MEG 0823', 'MEG 0913', 'MEG 0912', 'MEG 0923', 'MEG 0922', 'MEG 0932', 'MEG 0933', 'MEG 0942', 'MEG 0943', 'MEG 1013', 'MEG 1012', 'MEG 1023', 'MEG 1022', 'MEG 1032', 'MEG 1033', 'MEG 1043', 'MEG 1042', 'MEG 1112', 'MEG 1113', 'MEG 1123', 'MEG 1122', 'MEG 1133', 'MEG 1132', 'MEG 1142', 'MEG 1143', 'MEG 1213', 'MEG 1212', 'MEG 1223', 'MEG 1222', 'MEG 1232', 'MEG 1233', 'MEG 1243', 'MEG 1242', 'MEG 1312', 'MEG 1313', 'MEG 1323', 'MEG 1322', 'MEG 1333', 'MEG 1332', 'MEG 1342', 'MEG 1343', 'MEG 1412', 'MEG 1413', 'MEG 1423', 'MEG 1422', 'MEG 1433', 'MEG 1432', 'MEG 1442', 'MEG 1443', 'MEG 1512', 'MEG 1513', 'MEG 1522', 'MEG 1523', 'MEG 1533', 'MEG 1532', 'MEG 1543', 'MEG 1542', 'MEG 1613', 'MEG 1612', 'MEG 1622', 'MEG 1623', 'MEG 1632', 'MEG 1633', 'MEG 1643', 'MEG 1642', 'MEG 1713', 'MEG 1712', 'MEG 1722', 'MEG 1723', 'MEG 1732', 'MEG 1733', 'MEG 1743', 'MEG 1742', 'MEG 1813', 'MEG 1812', 'MEG 1822', 'MEG 1823', 'MEG 1832', 'MEG 1833', 'MEG 1843', 'MEG 1842', 'MEG 1912', 'MEG 1913', 'MEG 1923', 'MEG 1922', 'MEG 1932', 'MEG 1933', 'MEG 1943', 'MEG 1942', 'MEG 2013', 'MEG 2012', 'MEG 2023', 'MEG 2022', 'MEG 2032', 'MEG 2033', 'MEG 2042', 'MEG 2043', 'MEG 2113', 'MEG 2112', 'MEG 2122', 'MEG 2123', 'MEG 2133', 'MEG 2132', 'MEG 2143', 'MEG 2142', 'MEG 2212', 'MEG 2213', 'MEG 2223', 'MEG 2222', 'MEG 2233', 'MEG 2232', 'MEG 2242', 'MEG 2243', 'MEG 2312', 'MEG 2313', 'MEG 2323', 'MEG 2322', 'MEG 2332', 'MEG 2333', 'MEG 2343', 'MEG 2342', 'MEG 2412', 'MEG 2413', 'MEG 2423', 'MEG 2422', 'MEG 2433', 'MEG 2432', 'MEG 2442', 'MEG 2512', 'MEG 2513', 'MEG 2522', 'MEG 2523', 'MEG 2533', 'MEG 2532', 'MEG 2543', 'MEG 2542', 'MEG 2612', 'MEG 2613', 'MEG 2623', 'MEG 2622', 'MEG 2633', 'MEG 2632', 'MEG 2642', 'MEG 2643'] Rejecting epoch based on GRAD : ['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132', 'MEG 0133', 'MEG 0143', 'MEG 0142', 'MEG 0213', 'MEG 0212', 'MEG 0222', 'MEG 0223', 'MEG 0232', 'MEG 0233', 'MEG 0243', 'MEG 0242', 'MEG 0313', 'MEG 0312', 'MEG 0322', 'MEG 0323', 'MEG 0333', 'MEG 0332', 'MEG 0343', 'MEG 0342', 'MEG 0413', 'MEG 0412', 'MEG 0422', 'MEG 0423', 'MEG 0432', 'MEG 0433', 'MEG 0443', 'MEG 0442', 'MEG 0513', 'MEG 0512', 'MEG 0523', 'MEG 0522', 'MEG 0532', 'MEG 0533', 'MEG 0542', 'MEG 0543', 'MEG 0613', 'MEG 0612', 'MEG 0622', 'MEG 0623', 'MEG 0633', 'MEG 0632', 'MEG 0642', 'MEG 0643', 'MEG 0713', 'MEG 0712', 'MEG 0723', 'MEG 0722', 'MEG 0733', 'MEG 0732', 'MEG 0743', 'MEG 0742', 'MEG 0813', 'MEG 0812', 'MEG 0822', 'MEG 0823', 'MEG 0913', 'MEG 0912', 'MEG 0923', 'MEG 0922', 'MEG 0932', 'MEG 0933', 'MEG 0942', 'MEG 0943', 'MEG 1013', 'MEG 1012', 'MEG 1023', 'MEG 1022', 'MEG 1032', 'MEG 1033', 'MEG 1043', 'MEG 1042', 'MEG 1112', 'MEG 1113', 'MEG 1123', 'MEG 1122', 'MEG 1133', 'MEG 1132', 'MEG 1142', 'MEG 1143', 'MEG 1213', 'MEG 1212', 'MEG 1223', 'MEG 1222', 'MEG 1232', 'MEG 1233', 'MEG 1243', 'MEG 1242', 'MEG 1312', 'MEG 1313', 'MEG 1323', 'MEG 1322', 'MEG 1333', 'MEG 1332', 'MEG 1342', 'MEG 1343', 'MEG 1412', 'MEG 1413', 'MEG 1423', 'MEG 1422', 'MEG 1433', 'MEG 1432', 'MEG 1442', 'MEG 1443', 'MEG 1512', 'MEG 1513', 'MEG 1522', 'MEG 1523', 'MEG 1533', 'MEG 1532', 'MEG 1543', 'MEG 1542', 'MEG 1613', 'MEG 1612', 'MEG 1622', 'MEG 1623', 'MEG 1632', 'MEG 1633', 'MEG 1643', 'MEG 1642', 'MEG 1713', 'MEG 1712', 'MEG 1722', 'MEG 1723', 'MEG 1732', 'MEG 1733', 'MEG 1743', 'MEG 1742', 'MEG 1813', 'MEG 1812', 'MEG 1822', 'MEG 1823', 'MEG 1832', 'MEG 1833', 'MEG 1843', 'MEG 1842', 'MEG 1912', 'MEG 1913', 'MEG 1923', 'MEG 1922', 'MEG 1932', 'MEG 1933', 'MEG 1943', 'MEG 1942', 'MEG 2013', 'MEG 2012', 'MEG 2023', 'MEG 2022', 'MEG 2032', 'MEG 2033', 'MEG 2042', 'MEG 2043', 'MEG 2113', 'MEG 2112', 'MEG 2122', 'MEG 2123', 'MEG 2133', 'MEG 2132', 'MEG 2143', 'MEG 2142', 'MEG 2212', 'MEG 2213', 'MEG 2223', 'MEG 2222', 'MEG 2233', 'MEG 2232', 'MEG 2242', 'MEG 2243', 'MEG 2312', 'MEG 2313', 'MEG 2323', 'MEG 2322', 'MEG 2332', 'MEG 2333', 'MEG 2343', 'MEG 2342', 'MEG 2412', 'MEG 2413', 'MEG 2423', 'MEG 2422', 'MEG 2433', 'MEG 2432', 'MEG 2442', 'MEG 2512', 'MEG 2513', 'MEG 2522', 'MEG 2523', 'MEG 2533', 'MEG 2532', 'MEG 2543', 'MEG 2542', 'MEG 2612', 'MEG 2613', 'MEG 2623', 'MEG 2622', 'MEG 2633', 'MEG 2632', 'MEG 2642', 'MEG 2643'] Rejecting epoch based on GRAD : ['MEG 0113', 'MEG 0112', 'MEG 0122', 'MEG 0123', 'MEG 0132', 'MEG 0133', 'MEG 0143', 'MEG 0142', 'MEG 0213', 'MEG 0212', 'MEG 0222', 'MEG 0223', 'MEG 0232', 'MEG 0233', 'MEG 0243', 'MEG 0242', 'MEG 0313', 'MEG 0312', 'MEG 0322', 'MEG 0323', 'MEG 0333', 'MEG 0332', 'MEG 0343', 'MEG 0342', 'MEG 0413', 'MEG 0412', 'MEG 0422', 'MEG 0423', 'MEG 0432', 'MEG 0433', 'MEG 0443', 'MEG 0442', 'MEG 0513', 'MEG 0512', 'MEG 0523', 'MEG 0522', 'MEG 0532', 'MEG 0533', 'MEG 0542', 'MEG 0543', 'MEG 0613', 'MEG 0612', 'MEG 0622', 'MEG 0623', 'MEG 0633', 'MEG 0632', 'MEG 0642', 'MEG 0643', 'MEG 0713', 'MEG 0712', 'MEG 0723', 'MEG 0722', 'MEG 0733', 'MEG 0732', 'MEG 0743', 'MEG 0742', 'MEG 0813', 'MEG 0812', 'MEG 0822', 'MEG 0823', 'MEG 0913', 'MEG 0912', 'MEG 0923', 'MEG 0922', 'MEG 0932', 'MEG 0933', 'MEG 0942', 'MEG 0943', 'MEG 1013', 'MEG 1012', 'MEG 1023', 'MEG 1022', 'MEG 1032', 'MEG 1033', 'MEG 1043', 'MEG 1042', 'MEG 1112', 'MEG 1113', 'MEG 1123', 'MEG 1122', 'MEG 1133', 'MEG 1132', 'MEG 1142', 'MEG 1143', 'MEG 1213', 'MEG 1212', 'MEG 1223', 'MEG 1222', 'MEG 1232', 'MEG 1233', 'MEG 1243', 'MEG 1242', 'MEG 1312', 'MEG 1313', 'MEG 1323', 'MEG 1322', 'MEG 1333', 'MEG 1332', 'MEG 1342', 'MEG 1343', 'MEG 1412', 'MEG 1413', 'MEG 1423', 'MEG 1422', 'MEG 1433', 'MEG 1432', 'MEG 1442', 'MEG 1443', 'MEG 1512', 'MEG 1513', 'MEG 1522', 'MEG 1523', 'MEG 1533', 'MEG 1532', 'MEG 1543', 'MEG 1542', 'MEG 1613', 'MEG 1612', 'MEG 1622', 'MEG 1623', 'MEG 1632', 'MEG 1633', 'MEG 1643', 'MEG 1642', 'MEG 1713', 'MEG 1712', 'MEG 1722', 'MEG 1723', 'MEG 1732', 'MEG 1733', 'MEG 1743', 'MEG 1742', 'MEG 1813', 'MEG 1812', 'MEG 1822', 'MEG 1823', 'MEG 1832', 'MEG 1833', 'MEG 1843', 'MEG 1842', 'MEG 1912', 'MEG 1913', 'MEG 1923', 'MEG 1922', 'MEG 1932', 'MEG 1933', 'MEG 1943', 'MEG 1942', 'MEG 2013', 'MEG 2012', 'MEG 2023', 'MEG 2022', 'MEG 2032', 'MEG 2033', 'MEG 2042', 'MEG 2043', 'MEG 2113', 'MEG 2112', 'MEG 2122', 'MEG 2123', 'MEG 2133', 'MEG 2132', 'MEG 2143', 'MEG 2142', 'MEG 2212', 'MEG 2213', 'MEG 2223', 'MEG 2222', 'MEG 2233', 'MEG 2232', 'MEG 2242', 'MEG 2243', 'MEG 2312', 'MEG 2313', 'MEG 2323', 'MEG 2322', 'MEG 2332', 'MEG 2333', 'MEG 2343', 'MEG 2342', 'MEG 2412', 'MEG 2413', 'MEG 2423', 'MEG 2422', 'MEG 2433', 'MEG 2432', 'MEG 2442', 'MEG 2512', 'MEG 2513', 'MEG 2522', 'MEG 2523', 'MEG 2533', 'MEG 2532', 'MEG 2543', 'MEG 2542', 'MEG 2612', 'MEG 2613', 'MEG 2623', 'MEG 2622', 'MEG 2633', 'MEG 2632', 'MEG 2642', 'MEG 2643'] 7 bad epochs dropped Not setting metadata 7 matching events found Not setting metadata 7 matching events found Not setting metadata 7 matching events found Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_reject_epochs0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped Loading data for 7 events and 421 original time points ... Rejecting epoch based on MAG : ['MEG 0111', 'MEG 0141', 'MEG 0521', 'MEG 0811', 'MEG 1411', 'MEG 1421', 'MEG 1431', 'MEG 1441', 'MEG 1531', 'MEG 1541', 'MEG 2621'] Rejecting epoch based on MAG : ['MEG 0111', 'MEG 0141', 'MEG 0521', 'MEG 0811', 'MEG 1211', 'MEG 1221', 'MEG 1321', 'MEG 1411', 'MEG 1421', 'MEG 1431', 'MEG 1441', 'MEG 1531', 'MEG 1541', 'MEG 1741', 'MEG 2621'] Rejecting epoch based on MAG : ['MEG 0111', 'MEG 0141', 'MEG 1421', 'MEG 1431', 'MEG 1541', 'MEG 2621'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on MAG : ['MEG 0111', 'MEG 0141', 'MEG 0811', 'MEG 0911', 'MEG 1411', 'MEG 1421', 'MEG 1431', 'MEG 1531', 'MEG 1541', 'MEG 1711', 'MEG 1741', 'MEG 2621'] Rejecting epoch based on MAG : ['MEG 0111', 'MEG 0141', 'MEG 0521', 'MEG 1411', 'MEG 1421', 'MEG 1431', 'MEG 1441', 'MEG 1531', 'MEG 1541', 'MEG 1741', 'MEG 2611', 'MEG 2621', 'MEG 2631'] 7 bad epochs dropped PASSED mne/tests/test_epochs.py::test_callable_reject SKIPPED (Requires tes...) mne/tests/test_epochs.py::test_preload_epochs Not setting metadata 4 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 4 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped Not setting metadata 4 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 4 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_indexing_slicing Not setting metadata 5 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 5 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 2 bad epochs dropped Not setting metadata 5 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 5 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 2 bad epochs dropped Not setting metadata 5 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 5 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 2 bad epochs dropped Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Loading data for 10 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Loading data for 2 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_comparision_with_c Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-nf-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 7 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... 0 bad epochs dropped EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... PASSED mne/tests/test_epochs.py::test_crop Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-1.0006410259015925, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 1203 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped reject_tmin is not in epochs time interval. Setting reject_tmin to epochs.tmin (0.0 s) reject_tmax is not in epochs time interval. Setting reject_tmax to epochs.tmax (0.09989760657919393 s) reject_tmax is not in epochs time interval. Setting reject_tmax to epochs.tmax (0.3979254662071225 s) reject_tmin is not in epochs time interval. Setting reject_tmin to epochs.tmin (0.0 s) reject_tmax is not in epochs time interval. Setting reject_tmax to epochs.tmax (0.09989760657919393 s) Reading /tmp/pytest-of-pbuilder1/pytest-0/test_crop1/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = 0.00 ... 99.90 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_resample Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 2 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied Not setting metadata 2 matching events found No baseline correction applied Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_detrend Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.49948803289596966] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.49948803289596966] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Not setting metadata 1 matching events found Not setting metadata 1 matching events found Not setting metadata 1 matching events found PASSED mne/tests/test_epochs.py::test_bootstrap Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_epochs_copy Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_iter_evoked Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_iter_epochs[True] Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_iter_epochs[False] Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_subtract_evoked Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Subtracting Evoked from Epochs Subtracting Evoked from Epochs The following channels are not included in the subtraction: STI 005, STI 001, STI 006, STI 002, STI 014, STI 015, STI 004, STI 003, STI 016, EOG 061 [done] Projections have already been applied. Setting proj attribute to True. Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 2 events and 421 original time points ... 0 bad epochs dropped Subtracting Evoked from Epochs The following channels are not included in the subtraction: STI 005, STI 001, STI 006, STI 002, STI 014, STI 015, STI 004, STI 003, STI 016, EOG 061 [done] Loading data for 2 events and 421 original time points ... 0 bad epochs dropped Subtracting Evoked from Epochs The following channels are not included in the subtraction: STI 005, STI 001, STI 006, STI 002, STI 014, STI 015, STI 004, STI 003, STI 016, EOG 061 [done] Loading data for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_epoch_eq Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 8 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 8 events and 421 original time points ... 0 bad epochs dropped Dropped 0 epochs: Dropped 1 epoch: 7 Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 8 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 8 events and 421 original time points ... 0 bad epochs dropped Dropped 0 epochs: Dropped 1 epoch: 7 Not setting metadata 29 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 29 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Dropped 3 epochs: 14, 17, 20 Dropped 0 epochs: Dropped 6 epochs: 2, 13, 14, 15, 16, 17 Not setting metadata 29 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 29 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Dropped 3 epochs: 16, 19, 20 Loading data for 29 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Dropped 3 epochs: 16, 19, 20 Loading data for 29 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Dropped 1 epoch: 2 Dropped 10 epochs: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 Dropped 3 epochs: 2, 5, 8 Not setting metadata 29 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 29 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Dropped 9 epochs: 12, 13, 14, 15, 16, 17, 18, 19, 20 Loading data for 29 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 8 bad epochs dropped Dropped 9 epochs: 12, 13, 14, 15, 16, 17, 18, 19, 20 PASSED mne/tests/test_epochs.py::test_equalize_epoch_counts_random Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 8 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 8 events and 421 original time points ... 0 bad epochs dropped Dropped 0 epochs: Dropped 1 epoch: 0 PASSED mne/tests/test_epochs.py::test_access_by_name Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 15 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_access_by_name0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 15 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 29 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 29 events and 421 original time points ... 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_to_data_frame SKIPPED (could not impo...) mne/tests/test_epochs.py::test_to_data_frame_index[time] SKIPPED (co...) mne/tests/test_epochs.py::test_to_data_frame_index[index1] SKIPPED (...) mne/tests/test_epochs.py::test_to_data_frame_index[index2] SKIPPED (...) mne/tests/test_epochs.py::test_to_data_frame_index[index3] SKIPPED (...) mne/tests/test_epochs.py::test_to_data_frame_index[None] SKIPPED (co...) mne/tests/test_epochs.py::test_to_data_frame_time_format[None] SKIPPED mne/tests/test_epochs.py::test_to_data_frame_time_format[ms] SKIPPED mne/tests/test_epochs.py::test_to_data_frame_time_format[timedelta] SKIPPED mne/tests/test_epochs.py::test_epochs_proj_mixin Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped 1 projection items deactivated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items deactivated 2 projection items deactivated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 421 original time points ... 0 bad epochs dropped EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 421 original time points ... 0 bad epochs dropped EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Leaving delayed SSP mode. Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped SSP projectors applied... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Leaving delayed SSP mode. Created an SSP operator (subspace dimension = 4) 4 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped SSP projectors applied... Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... Leaving delayed SSP mode. Created an SSP operator (subspace dimension = 4) 4 projection items activated Loading data for 1 events and 421 original time points ... SSP projectors applied... Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 4) 4 projection items activated Loading data for 1 events and 421 original time points ... SSP projectors applied... PASSED mne/tests/test_epochs.py::test_delayed_epochs Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 True True 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped Projections have already been applied. Setting proj attribute to True. 1 True True 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped Projections have already been applied. Setting proj attribute to True. 1 True True 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Projections have already been applied. Setting proj attribute to True. 1 True False 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 True False 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 True False 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 1 True delayed 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 True delayed 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 True delayed 2 Not setting metadata 2 matching events found No baseline correction applied Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) 1 False True 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Projections have already been applied. Setting proj attribute to True. Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 False True 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped Projections have already been applied. Setting proj attribute to True. 1 False True 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Projections have already been applied. Setting proj attribute to True. 1 False False 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 False False 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 False False 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 1 False delayed 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 False delayed 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points ... 0 bad epochs dropped 1 False delayed 2 Not setting metadata 2 matching events found No baseline correction applied Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 True True 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Projections have already been applied. Setting proj attribute to True. 3 True True 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Projections have already been applied. Setting proj attribute to True. 3 True True 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Projections have already been applied. Setting proj attribute to True. 3 True False 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 True False 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 True False 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 True delayed 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 True delayed 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 True delayed 2 Not setting metadata 2 matching events found No baseline correction applied Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) 3 False True 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Projections have already been applied. Setting proj attribute to True. Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 False True 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped Projections have already been applied. Setting proj attribute to True. 3 False True 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Projections have already been applied. Setting proj attribute to True. 3 False False 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 False False 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 False False 2 Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 False delayed 0 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 False delayed 1 Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 2 events and 421 original time points (prior to decimation) ... 0 bad epochs dropped 3 False delayed 2 Not setting metadata 2 matching events found No baseline correction applied Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_drop_epochs Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Dropped 2 epochs: 2, 4 Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Dropped 3 epochs: 2, 3, 4 PASSED mne/tests/test_epochs.py::test_drop_epochs_mult[True] Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 15 events and 421 original time points ... Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 046', 'EEG 047', 'EEG 049', 'EEG 054', 'EEG 055'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059', 'EEG 060'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 6 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped PASSED mne/tests/test_epochs.py::test_drop_epochs_mult[False] Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_contains Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 0 ... 14399 = 0.000 ... 23.974 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=14400 Range : 0 ... 14399 = 0.000 ... 23.974 secs Ready. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated PASSED mne/tests/test_epochs.py::test_drop_channels_mixin Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_pick_channels_mixin Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found PASSED mne/tests/test_epochs.py::test_equalize_channels Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Identifying common channels ... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Dropped the following channels: ['MEG 0113', 'MEG 0112'] PASSED mne/tests/test_epochs.py::test_illegal_event_id PASSED mne/tests/test_epochs.py::test_add_channels_epochs Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) 3 projection items activated Created an SSP operator (subspace dimension = 3) 3 projection items activated Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_array_epochs[matplotlib] Not setting metadata 10 matching events found No baseline correction applied 0 projection items activated Not setting metadata 10 matching events found Not setting metadata 5 matching events found Not setting metadata 10 matching events found No baseline correction applied 0 projection items activated Reading /tmp/pytest-of-pbuilder1/pytest-0/test_array_epochs_matplotlib_0/test-epo.fif ... Isotrak not found Found the data of interest: t = -200.00 ... 99.00 ms 0 CTF compensation matrices available Not setting metadata 10 matching events found No baseline correction applied 0 projection items activated Not setting metadata 10 matching events found No baseline correction applied 0 projection items activated Rejecting epoch based on EEG : ['EEG 006'] Rejecting flat epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020'] 2 bad epochs dropped Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Not setting metadata 10 matching events found No baseline correction applied 0 projection items activated Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_array_epochs_matplotlib_0/test-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 0.00 ms 0 CTF compensation matrices available Not setting metadata 10 matching events found No baseline correction applied 0 projection items activated Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_array_epochs[qt] SKIPPED (Qt API None...) mne/tests/test_epochs.py::test_concatenate_epochs Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Applying baseline correction (mode: mean) Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Loading data for 0 events and 421 original time points ... Not setting metadata 7 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_concatenate_epochs_large Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 21 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Not setting metadata 420 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_add_channels Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) 3 projection items activated Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_seeg_ecog Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_default_values Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_metadata SKIPPED (could not import 'p...) mne/tests/test_epochs.py::test_make_metadata[all_event_id0-None--0.5-1.5-None-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata[all_event_id1-None--0.5-1.5-None-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata[all_event_id2-None--0.5-1.5-None-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata[all_event_id3-row_events3--0.5-1.5-keep_first3-c] SKIPPED mne/tests/test_epochs.py::test_make_metadata[all_event_id4-None-None-1.5-None-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata[all_event_id5-None--0.5-None-None-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata[all_event_id6-None-None-None-None-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata_bounded_by_row_or_tmin_tmax_event_names[None-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata_bounded_by_row_or_tmin_tmax_event_names[cue-resp] SKIPPED mne/tests/test_epochs.py::test_make_metadata_bounded_by_row_or_tmin_tmax_event_names[tmin2-tmax2] SKIPPED mne/tests/test_epochs.py::test_make_metadata_bounded_by_row_or_tmin_tmax_event_names[None-resp] SKIPPED mne/tests/test_epochs.py::test_make_metadata_bounded_by_row_or_tmin_tmax_event_names[cue-None] SKIPPED mne/tests/test_epochs.py::test_make_metadata_bounded_by_row_or_tmin_tmax_event_names[tmin5-tmax5] SKIPPED mne/tests/test_epochs.py::test_events_list Creating RawArray with float64 data, n_channels=10, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated PASSED mne/tests/test_epochs.py::test_save_overwrite Creating RawArray with float64 data, n_channels=100, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 10 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 9 events and 701 original time points ... Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 9 events and 701 original time points ... Overwriting existing file. Using data from preloaded Raw for 1 events and 701 original time points ... Overwriting existing file. Using data from preloaded Raw for 9 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_save_complex_data[single-2e-06-True-True] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_single_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_save_complex_data[single-2e-06-True-False] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_single_1/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_save_complex_data[single-2e-06-False-True] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_single_2/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_save_complex_data[single-2e-06-False-False] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_single_3/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_save_complex_data[double-1e-10-True-True] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_double_0/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_save_complex_data[double-1e-10-True-False] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_double_1/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_save_complex_data[double-1e-10-False-True] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_double_2/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) PASSED mne/tests/test_epochs.py::test_save_complex_data[double-1e-10-False-False] Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 1 events and 421 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_save_complex_data_double_3/test-epo.fif ... Read a total of 3 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Found the data of interest: t = -199.80 ... 499.49 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_no_epochs Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 31 events and 421 original time points ... Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] 4 bad epochs dropped Loading data for 1 events and 421 original time points ... Loading data for 27 events and 421 original time points ... Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Loading data for 31 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 31 bad epochs dropped Overwriting existing file. Loading data for 0 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_readonly_times Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_channel_types_mixin Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_average_methods Not setting metadata 5 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_shift_time[True] Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_shift_time[False] Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_shift_time_raises_when_not_loaded[True] Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_shift_time_raises_when_not_loaded[False] Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_drop_selection[fname0-True] SKIPPED mne/tests/test_epochs.py::test_epochs_drop_selection[fname0-False] SKIPPED mne/tests/test_epochs.py::test_epochs_drop_selection[fname1-True] SKIPPED mne/tests/test_epochs.py::test_epochs_drop_selection[fname1-False] SKIPPED mne/tests/test_epochs.py::test_file_like[True-file] Creating RawArray with float64 data, n_channels=100, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 10 events and 701 original time points ... 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_file_like[True-bytes] Creating RawArray with float64 data, n_channels=100, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 10 events and 701 original time points ... 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_file_like[False-file] Creating RawArray with float64 data, n_channels=100, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 10 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 9 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_file_like[False-bytes] Creating RawArray with float64 data, n_channels=100, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. Not setting metadata 10 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 10 events and 701 original time points ... 1 bad epochs dropped Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 9 events and 701 original time points ... PASSED mne/tests/test_epochs.py::test_epochs_get_data_item[True] Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 2 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_epochs.py::test_epochs_get_data_item[False] Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 2 events and 421 original time points ... 0 bad epochs dropped Loading data for 1 events and 421 original time points ... Loading data for 1 events and 421 original time points ... PASSED mne/tests/test_epochs.py::test_pick_types_reject_flat_keys Not setting metadata 29 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 29 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 6 bad epochs dropped PASSED mne/tests/test_epochs.py::test_make_fixed_length_epochs SKIPPED (Req...) mne/tests/test_epochs.py::test_epochs_huge_events Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_concat_overflow Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 4 matching events found No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_concat_overflow0/temp-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 999.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Overwriting existing file. Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_concat_overflow0/temp-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 999.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_baseline_after_cropping Creating RawArray with float64 data, n_channels=1, n_times=2001 Range : 0 ... 2000 = 0.000 ... 2.000 secs Ready. Not setting metadata 1 matching events found Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 1 events and 401 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 1 events and 401 original time points ... Using data from preloaded Raw for 1 events and 401 original time points ... Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_baseline_after_cro0/temp-cropped-epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 200.00 ms 0 CTF compensation matrices available Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_empty_constructor Not setting metadata No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_apply_function Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_apply_function_epo_ch_access Creating RawArray with float64 data, n_channels=2, n_times=100 Range : 0 ... 99 = 0.000 ... 99.000 secs Ready. Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 46 original time points ... 0 bad epochs dropped apply_function requested to access ch_idx apply_function requested to access ch_idx ... MNE_FORCE_SERIAL set. Processing in forced serial mode. apply_function requested to access ch_name apply_function requested to access ch_name ... MNE_FORCE_SERIAL set. Processing in forced serial mode. PASSED mne/tests/test_epochs.py::test_add_channels_picks SKIPPED (Requires ...) mne/tests/test_epochs.py::test_epoch_annotations[None-None-0] SKIPPED mne/tests/test_epochs.py::test_epoch_annotations[None-None-10] SKIPPED mne/tests/test_epochs.py::test_epoch_annotations[3.141592653589793-None-0] SKIPPED mne/tests/test_epochs.py::test_epoch_annotations[3.141592653589793-None-10] SKIPPED mne/tests/test_epochs.py::test_epoch_annotations[3.141592653589793-orig_date2-0] SKIPPED mne/tests/test_epochs.py::test_epoch_annotations[3.141592653589793-orig_date2-10] SKIPPED mne/tests/test_epochs.py::test_epoch_annotations_cases Creating RawArray with float64 data, n_channels=1, n_times=600 Range : 0 ... 599 = 0.000 ... 5.990 secs Ready. Not setting metadata 3 matching events found No baseline correction applied 0 projection items activated Not setting metadata 3 matching events found No baseline correction applied 0 projection items activated Dropped 1 epoch: 0 Using data from preloaded Raw for 2 events and 101 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 101 original time points ... 0 bad epochs dropped Using data from preloaded Raw for 2 events and 101 original time points ... Using data from preloaded Raw for 2 events and 101 original time points ... Not setting metadata 4 matching events found No baseline correction applied PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[1-0-None] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 0 ... 39 = 0.000 ... 3.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa0/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 900.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[1-0-meas_date1] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 0 ... 39 = 0.000 ... 3.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa1/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 900.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[1-10000-None] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 10000 ... 10039 = 1000.000 ... 1003.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa2/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 900.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[1-10000-meas_date1] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 10000 ... 10039 = 1000.000 ... 1003.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa3/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 900.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[2-0-None] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 0 ... 39 = 0.000 ... 3.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points (prior to decimation) ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa4/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 800.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[2-0-meas_date1] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 0 ... 39 = 0.000 ... 3.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points (prior to decimation) ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa5/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 800.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[2-10000-None] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 10000 ... 10039 = 1000.000 ... 1003.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points (prior to decimation) ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa6/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 800.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_annotations_backwards_compat[2-10000-meas_date1] Creating RawArray with float64 data, n_channels=1, n_times=40 Range : 10000 ... 10039 = 1000.000 ... 1003.900 secs Ready. Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 4 events and 10 original time points (prior to decimation) ... 0 bad epochs dropped Reading /tmp/pytest-of-pbuilder1/pytest-0/test_epochs_annotations_backwa7/test_epo.fif ... Isotrak not found Found the data of interest: t = 0.00 ... 800.00 ms 0 CTF compensation matrices available Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_epochs.py::test_epochs_saving_with_annotations SKIPPED mne/tests/test_epochs.py::test_empty_error[add_reference_channels] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[apply_function] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[apply_hilbert] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[as_type] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[average] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[compute_psd] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[drop_channels] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[filter] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[interpolate_bads] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[pick] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[pick_channels] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). PASSED mne/tests/test_epochs.py::test_empty_error[pick_types] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped NOTE: pick_types() is a legacy function. New code should use inst.pick(...). PASSED mne/tests/test_epochs.py::test_empty_error[plot] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[plot_image] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[plot_psd] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped NOTE: plot_psd() is a legacy function. New code should use .compute_psd().plot(). PASSED mne/tests/test_epochs.py::test_empty_error[plot_psd_topo] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped NOTE: plot_psd_topo() is a legacy function. New code should use .compute_psd().plot_topo(). PASSED mne/tests/test_epochs.py::test_empty_error[plot_psd_topomap] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped NOTE: plot_psd_topomap() is a legacy function. New code should use .compute_psd().plot_topomap(). PASSED mne/tests/test_epochs.py::test_empty_error[plot_topo_image] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[resample] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[reorder_channels] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[savgol_filter] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[set_eeg_reference] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. PASSED mne/tests/test_epochs.py::test_empty_error[shift_time] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[standard_error] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped PASSED mne/tests/test_epochs.py::test_empty_error[to_data_frame] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Rejecting epoch based on MAG : ['MEG 0721', 'MEG 1111', 'MEG 1421', 'MEG 1911', 'MEG 2221', 'MEG 2531'] 1 bad epochs dropped SKIPPED (c...) mne/tests/test_event.py::test_fix_stim Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 32 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32 32765] 32 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32 32771] PASSED mne/tests/test_event.py::test_add_events Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 32 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 1 event found on stim channel STI 014 Event IDs: [1] PASSED mne/tests/test_event.py::test_merge_events PASSED mne/tests/test_event.py::test_io_events Overwriting existing file. Overwriting existing file. Overwriting existing file. Overwriting existing file. PASSED mne/tests/test_event.py::test_io_c_annot Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/tests/test_event.py::test_find_events Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 31 events found on stim channel STI 014 Event IDs: [ 1 2 3 4 5 32] 8 events found on stim channel STI 014 Event IDs: [ 4 32] 2 events found on stim channel STI 014 Event IDs: [2 4] 3 events found on stim channel STI 014 Event IDs: [1 4] 1 event found on stim channel STI 014 Event IDs: [4] 3 events found on stim channel STI 014 Event IDs: [1 2 3] 2 events found on stim channel STI 014 Event IDs: [1] 1 event found on stim channel STI 014 Event IDs: [2] 3 events found on stim channel STI 014 Event IDs: [1 2 3] 1 event found on stim channel STI 014 Event IDs: [4] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 1 event found on stim channel STI 014 Event IDs: [9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 3 events found on stim channel STI 014 Event IDs: [5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 5 events found on stim channel STI 014 Event IDs: [5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 4 events found on stim channel STI 014 Event IDs: [5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 3 events found on stim channel STI 014 Event IDs: [5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 5 events found on stim channel STI 014 Event IDs: [5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 8 events found on stim channel STI 014 Event IDs: [0 5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 8 events found on stim channel STI 014 Event IDs: [0 5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 4 events found on stim channel STI 014 Event IDs: [5 6 9] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 1 event found on stim channel STI 014 Event IDs: [5] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 3 events found on stim channel STI 014 Event IDs: [5 6] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 1 event found on stim channel STI 014 Event IDs: [5] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 3 events found on stim channel STI 014 Event IDs: [5 6] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 2 events found on stim channel STI 014 Event IDs: [5 6] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 2 events found on stim channel STI 014 Event IDs: [1 3] Trigger channel STI 014 has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 2 events found on stim channel STI 014 Event IDs: [1 3] 2 events found on stim channel STI 015 Event IDs: [1 3] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 7 ... 1006 = 0.007 ... 1.006 secs Ready. Trigger channel MYSTI has a non-zero initial value of {initial_value} (consider using initial_event=True to detect this event) Removing orphaned offset at the beginning of the file. 1 event found on stim channel MYSTI Event IDs: [200] 2 events found on stim channel MYSTI Event IDs: [100 200] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/tests/test_event.py::test_pick_events PASSED mne/tests/test_event.py::test_make_fixed_length_events Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=27768 Range : 0 ... 27767 = 0.000 ... 178.623 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=21216 Range : 0 ... 21215 = 0.000 ... 136.475 secs Ready. Using up to 1 segment Number of samples used : 21216 [done] PASSED mne/tests/test_event.py::test_define_events Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/tests/test_event.py::test_acqparser SKIPPED (Requires testing da...) mne/tests/test_event.py::test_acqparser_averaging SKIPPED (Requires ...) mne/tests/test_event.py::test_shift_time_events PASSED mne/tests/test_event.py::test_match_event_names PASSED mne/tests/test_event.py::test_count_events PASSED mne/tests/test_evoked.py::test_get_data Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_decim Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped PASSED mne/tests/test_evoked.py::test_savgol_filter Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Using savgol length 61 PASSED mne/tests/test_evoked.py::test_hash_evoked Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[1100 (FIFFV_ASPECT_IFII_LOW)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_1100__FIFF0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 1100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[1101 (FIFFV_ASPECT_IFII_HIGH)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_1101__FIFF0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 1101 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[1102 (FIFFV_ASPECT_GATE)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_1102__FIFF0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 1102 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[100 (FIFFV_ASPECT_AVERAGE)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_100__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[101 (FIFFV_ASPECT_STD_ERR)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_101__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 101 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[102 (FIFFV_ASPECT_SINGLE)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_102__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 102 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[103 (FIFFV_ASPECT_SUBAVERAGE)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_103__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 103 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[104 (FIFFV_ASPECT_ALTAVERAGE)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_104__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 104 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[105 (FIFFV_ASPECT_SAMPLE)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_105__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 105 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[106 (FIFFV_ASPECT_POWER_DENSITY)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_106__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 106 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_aspects[200 (FIFFV_ASPECT_DIPOLE_WAVE)] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_aspects_200__FIFFV0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 200 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_io_evoked Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory doubled nave) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif.gz ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/complex-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (🙃) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/test-bad-name.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_io_evoked0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_shift_time_evoked Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_shift_time_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -299.80 ... 399.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_shift_time_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -99.80 ... 599.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Overwriting existing file. Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_shift_time_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_shift_time_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -300.00 ... 399.28 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_shift_time_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.79 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Overwriting existing file. Reading /tmp/pytest-of-pbuilder1/pytest-0/test_shift_time_evoked0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -104.89 ... 594.39 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_tmin_tmax Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_resample Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_resample0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 500.32 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_resample0/evoked-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 500.32 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_resamp_noop Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Sampling frequency of the instance is already 600.614990234375, returning unmodified. PASSED mne/tests/test_evoked.py::test_evoked_filter Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 199 samples (0.331 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.1s PASSED mne/tests/test_evoked.py::test_evoked_detrend Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_to_data_frame SKIPPED (could not impo...) mne/tests/test_evoked.py::test_to_data_frame_time_format[None] SKIPPED mne/tests/test_evoked.py::test_to_data_frame_time_format[ms] SKIPPED mne/tests/test_evoked.py::test_to_data_frame_time_format[timedelta] SKIPPED mne/tests/test_evoked.py::test_evoked_proj Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied 0 projection items deactivated Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 No baseline correction applied 4 projection items deactivated 3 projection items deactivated Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 No baseline correction applied Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... PASSED mne/tests/test_evoked.py::test_get_peak Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_drop_channels_mixin Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_pick_channels_mixin Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_equalize_channels Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Identifying common channels ... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Dropped the following channels: ['MEG 0113', 'MEG 0112'] PASSED mne/tests/test_evoked.py::test_arithmetic Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Setting channel interpolation method to {'eeg': 'spline', 'meg': 'MNE'}. Interpolating bad channels. Automatic origin fit: head of radius 91.0 mm Computing interpolation matrix from 59 sensor positions Interpolating 1 sensors Identifying common channels ... NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Dropped the following channels: ['MEG 0113', 'MEG 0112'] Identifying common channels ... Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_array_epochs Reading /tmp/pytest-of-pbuilder1/pytest-0/test_array_epochs0/evkdary-ave.fif ... Isotrak not found Found the data of interest: t = -10.00 ... 49.00 ms (No comment) 0 CTF compensation matrices available nave = 1 - aspect type = 100 No projector specified for this dataset. Please consider the method self.add_proj. No baseline correction applied Not setting metadata 1 matching events found No baseline correction applied 0 projection items activated PASSED mne/tests/test_evoked.py::test_time_as_index_and_crop Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_add_channels Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/tests/test_evoked.py::test_evoked_baseline Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Applying baseline correction (mode: mean) Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Applying baseline correction (mode: mean) Reading /tmp/pytest-of-pbuilder1/pytest-0/test_evoked_baseline0/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Projections have already been applied. Setting proj attribute to True. Loaded Evoked data is baseline-corrected (baseline: [-0.2, -0.1] s) Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Applying baseline correction (mode: mean) PASSED mne/tests/test_evoked.py::test_hilbert Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 31 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated PASSED mne/tests/test_evoked.py::test_apply_function_evk PASSED mne/tests/test_evoked.py::test_apply_function_evk_ch_access apply_function requested to access ch_idx apply_function requested to access ch_idx ... MNE_FORCE_SERIAL set. Processing in forced serial mode. apply_function requested to access ch_name apply_function requested to access ch_name ... MNE_FORCE_SERIAL set. Processing in forced serial mode. PASSED mne/tests/test_filter.py::test_filter_array Setting up band-pass filter from 8 - 12 Hz IIR filter parameters --------------------- butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 8 (effective, after forward-backward) - Cutoffs at 8.00, 12.00 Hz: -6.02, -6.02 dB Setting up band-pass filter from 8 - 12 Hz IIR filter parameters --------------------- butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 8 (effective, after forward-backward) - Cutoffs at 8.00, 12.00 Hz: -6.02, -6.02 dB PASSED mne/tests/test_filter.py::test_mne_c_design SKIPPED (Requires MNE-C) mne/tests/test_filter.py::test_estimate_ringing PASSED mne/tests/test_filter.py::test_1d_filter PASSED mne/tests/test_filter.py::test_iir_stability Setting up high-pass filter at 0.6 Hz IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 16 (effective, after forward-backward) Setting up high-pass filter at 0.6 Hz IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 16 (effective, after forward-backward) - Cutoff at 0.60 Hz: -6.02 dB Setting up high-pass filter at 0.6 Hz IIR filter parameters --------------------- Butterworth highpass non-linear phase (one-pass forward) causal filter: - Filter order 8 (forward) - Cutoff at 0.60 Hz: -3.01 dB Setting up high-pass filter at 0.6 Hz Setting up high-pass filter at 0.6 Hz Setting up high-pass filter at 0.6 Hz Setting up high-pass filter at 0.6 Hz Setting up high-pass filter at 0.1 Hz Setting up high-pass filter at 0.5 Hz IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 4 (effective, after forward-backward) - Cutoff at 0.50 Hz: -6.02 dB IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 4 (effective, after forward-backward) - Cutoff at 0.50 Hz: -6.02 dB Setting up high-pass filter at 0.5 Hz Setting up high-pass filter at 0.5 Hz IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 4 (effective, after forward-backward) - Cutoff at 0.50 Hz: -6.02 dB PASSED mne/tests/test_filter.py::test_iir_phase Setting up high-pass filter at 0.6 Hz IIR filter parameters --------------------- Butterworth highpass non-linear phase (one-pass forward) causal filter: - Filter order 2 (forward) - Cutoff at 0.60 Hz: -3.01 dB Setting up high-pass filter at 0.6 Hz IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 4 (effective, after forward-backward) - Cutoff at 0.60 Hz: -6.02 dB PASSED mne/tests/test_filter.py::test_notch_filters[spectrum_fit-auto-None-2] PASSED mne/tests/test_filter.py::test_notch_filters[spectrum_fit-None-None-2] PASSED mne/tests/test_filter.py::test_notch_filters[spectrum_fit-10s-None-2] PASSED mne/tests/test_filter.py::test_notch_filters[spectrum_fit-auto-line_freq3-1] PASSED mne/tests/test_filter.py::test_notch_filters[fft-auto-line_freq4-1] PASSED mne/tests/test_filter.py::test_notch_filters[fft-8192-line_freq5-1] PASSED mne/tests/test_filter.py::test_resample[fft] PASSED mne/tests/test_filter.py::test_resample[polyphase] Polyphase resampling neighborhood: ±2 input samples Polyphase resampling neighborhood: ±2 input samples PASSED mne/tests/test_filter.py::test_resample_scipy CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None PASSED mne/tests/test_filter.py::test_n_jobs[2] ... MNE_FORCE_SERIAL set. Processing in forced serial mode. Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 33 samples (0.330 s) Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 33 samples (0.330 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 4 out of 4 | elapsed: 2.1s finished PASSED mne/tests/test_filter.py::test_n_jobs[cuda] CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 33 samples (0.330 s) Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 33 samples (0.330 s) CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None PASSED mne/tests/test_filter.py::test_resamp_stim_channel PASSED mne/tests/test_filter.py::test_resample_raw[fft] Creating RawArray with float64 data, n_channels=1, n_times=1001 Range : 0 ... 1000 = 0.000 ... 0.488 secs Ready. PASSED mne/tests/test_filter.py::test_resample_raw[polyphase] Creating RawArray with float64 data, n_channels=1, n_times=1001 Range : 0 ... 1000 = 0.000 ... 0.488 secs Ready. Polyphase resampling neighborhood: ±2 input samples PASSED mne/tests/test_filter.py::test_resample_below_1_sample[fft] Creating RawArray with float64 data, n_channels=1, n_times=100 Range : 0 ... 99 = 0.000 ... 0.099 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. PASSED mne/tests/test_filter.py::test_resample_below_1_sample[polyphase] Creating RawArray with float64 data, n_channels=1, n_times=100 Range : 0 ... 99 = 0.000 ... 0.099 secs Ready. Polyphase resampling neighborhood: ±2 input samples Creating RawArray with float64 data, n_channels=1, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. PASSED mne/tests/test_filter.py::test_filters Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1001 samples (10.010 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1001 samples (10.010 s) Setting up band-pass filter from 4 - 8 Hz Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 11 samples (0.110 s) Setting up band-pass filter from 4 - 50 Hz Setting up high-pass filter at -1 Hz No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped. FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal allpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Filter length: 1 samples (0.010 s) FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal allpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Filter length: 1 samples (0.010 s) Setting up band-pass filter from 1 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Lower passband edge: 1.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 9.00 Hz) - Filter length: 257 samples (2.570 s) Setting up band-pass filter from 1 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Lower passband edge: 1.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 9.00 Hz) - Filter length: 51 samples (0.510 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 331 samples (3.310 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 3.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 2.50 Hz) - Upper passband edge: 9.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 9.50 Hz) - Filter length: 331 samples (3.310 s) Setting up low-pass filter at 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 331 samples (3.310 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished Setting up high-pass filter at 4 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Filter length: 331 samples (3.310 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 331 samples (3.310 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 331 samples (3.310 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 331 samples (3.310 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 331 samples (3.310 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1001 samples (10.010 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 3.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 2.50 Hz) - Upper passband edge: 9.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 9.50 Hz) - Filter length: 1001 samples (10.010 s) Setting up low-pass filter at 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1001 samples (10.010 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished Setting up high-pass filter at 4 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Filter length: 1001 samples (10.010 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1001 samples (10.010 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1001 samples (10.010 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1001 samples (10.010 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1001 samples (10.010 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 501 samples (5.010 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 3.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 2.50 Hz) - Upper passband edge: 9.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 9.50 Hz) - Filter length: 501 samples (5.010 s) Setting up low-pass filter at 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 501 samples (5.010 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished Setting up high-pass filter at 4 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Filter length: 501 samples (5.010 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 501 samples (5.010 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 501 samples (5.010 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 501 samples (5.010 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 501 samples (5.010 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1025 samples (10.250 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 3.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 2.50 Hz) - Upper passband edge: 9.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 9.50 Hz) - Filter length: 1025 samples (10.250 s) Setting up low-pass filter at 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1025 samples (10.250 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished Setting up high-pass filter at 4 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Filter length: 1025 samples (10.250 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1025 samples (10.250 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1025 samples (10.250 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1025 samples (10.250 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1025 samples (10.250 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1023 samples (10.230 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 3.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 2.50 Hz) - Upper passband edge: 9.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 9.50 Hz) - Filter length: 1023 samples (10.230 s) Setting up low-pass filter at 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 1023 samples (10.230 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished Setting up high-pass filter at 4 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Filter length: 1023 samples (10.230 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1023 samples (10.230 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1023 samples (10.230 s) Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1023 samples (10.230 s) Setting up band-stop filter from 3 - 9 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal bandstop filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 1.00 Hz - Upper transition bandwidth: 1.00 Hz - Filter length: 1023 samples (10.230 s) ... MNE_FORCE_SERIAL set. Processing in forced serial mode. ... MNE_FORCE_SERIAL set. Processing in forced serial mode. CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None IIR filter parameters --------------------- Chebyshev I low zero-phase (two-pass forward and reverse) non-causal filter: - Cutoff at 40.00 Hz: -2.00 dB IIR filter parameters --------------------- Butterworth low zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 8 (effective, after forward-backward) - Cutoff at 40.00 Hz: -6.02 dB IIR filter parameters --------------------- Chebyshev I low zero-phase (two-pass forward and reverse) non-causal filter: - Cutoff at 40.00 Hz: -2.00 dB IIR filter parameters --------------------- Butterworth low zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 8 (effective, after forward-backward) - Cutoff at 40.00 Hz: -6.02 dB Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 3.00 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 9.00 Hz) - Filter length: 401 samples (4.010 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.3s Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 3.00 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 9.00 Hz) - Filter length: 401 samples (4.010 s) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.2s [Parallel(n_jobs=1)]: Done 449 tasks | elapsed: 0.3s Setting up band-pass filter from 4 - 8 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 4.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 3.50 Hz) - Upper passband edge: 8.00 Hz - Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz) - Filter length: 101 samples (1.010 s) Setting up low-pass filter at 55 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 55.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 55.25 Hz) - Filter length: 6601 samples (6.601 s) Setting up low-pass filter at 55 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 55.00 Hz - Upper transition bandwidth: 0.50 Hz (-6 dB cutoff frequency: 55.25 Hz) - Filter length: 7001 samples (7.001 s) Setting up low-pass filter at 55 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 55.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 55.25 Hz) - Filter length: 7001 samples (7.001 s) PASSED mne/tests/test_filter.py::test_filter_auto Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 265 samples (2.650 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Setting up low-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz) - Filter length: 133 samples (1.330 s) Creating RawArray with float64 data, n_channels=1, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready. PASSED mne/tests/test_filter.py::test_cuda_fir SKIPPED (CUDA not enabled) mne/tests/test_filter.py::test_cuda_resampling CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None CUDA not enabled in config, skipping initialization CUDA not used, CUDA could not be initialized, falling back to n_jobs=None PASSED mne/tests/test_filter.py::test_detrend PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-butter-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-butter-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-butter-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-butter-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-butter-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-butter-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-bessel-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-bessel-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-bessel-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-bessel-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-bessel-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-bessel-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-ellip-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-ellip-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-ellip-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-ellip-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-ellip-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-lowpass-ellip-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-butter-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-butter-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-butter-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-butter-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-butter-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-butter-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-bessel-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-bessel-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-bessel-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-bessel-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-bessel-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-bessel-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-ellip-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-ellip-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-ellip-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-ellip-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-ellip-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[1-bandpass-ellip-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-butter-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-butter-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-butter-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-butter-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-butter-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-butter-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-bessel-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-bessel-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-bessel-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-bessel-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-bessel-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-bessel-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-ellip-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-ellip-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-ellip-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-ellip-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-ellip-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-lowpass-ellip-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-butter-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-butter-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-butter-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-butter-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-butter-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-butter-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-bessel-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-bessel-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-bessel-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-bessel-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-bessel-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-bessel-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-ellip-ba-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-ellip-ba-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-ellip-ba-forward] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-ellip-sos-zero] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-ellip-sos-zero-double] PASSED mne/tests/test_filter.py::test_reporting_iir[4-bandpass-ellip-sos-forward] PASSED mne/tests/test_filter.py::test_reporting_fir[lowpass-hamming-zero] PASSED mne/tests/test_filter.py::test_reporting_fir[lowpass-hamming-zero-double] PASSED mne/tests/test_filter.py::test_reporting_fir[lowpass-hamming-minimum] PASSED mne/tests/test_filter.py::test_reporting_fir[lowpass-blackman-zero] PASSED mne/tests/test_filter.py::test_reporting_fir[lowpass-blackman-zero-double] PASSED mne/tests/test_filter.py::test_reporting_fir[lowpass-blackman-minimum] PASSED mne/tests/test_filter.py::test_reporting_fir[bandpass-hamming-zero] PASSED mne/tests/test_filter.py::test_reporting_fir[bandpass-hamming-zero-double] PASSED mne/tests/test_filter.py::test_reporting_fir[bandpass-hamming-minimum] PASSED mne/tests/test_filter.py::test_reporting_fir[bandpass-blackman-zero] PASSED mne/tests/test_filter.py::test_reporting_fir[bandpass-blackman-zero-double] PASSED mne/tests/test_filter.py::test_reporting_fir[bandpass-blackman-minimum] PASSED mne/tests/test_filter.py::test_filter_picks Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. No data channels found. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. No data channels found. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. No data channels found. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. No data channels found. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. No data channels found. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. No data channels found. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Filtering raw data in 1 contiguous segment Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 331 samples (0.331 s) PASSED mne/tests/test_filter.py::test_filter_minimum_phase_bug No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped. Setting up high-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 10.00 - Lower transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 7.50 Hz) - Filter length: 1001 samples (1.001 s) No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped. Setting up high-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, non-linear phase, causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 5.00 Hz - Filter length: 1001 samples (1.001 s) No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped. Setting up high-pass filter at 10 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower transition bandwidth: 5.00 Hz - Filter length: 1001 samples (1.001 s) PASSED mne/tests/test_freesurfer.py::test_check_subject_dir SKIPPED (Requir...) mne/tests/test_freesurfer.py::test_mgz_header SKIPPED (Requires test...) mne/tests/test_freesurfer.py::test_vertex_to_mni SKIPPED (Requires t...) mne/tests/test_freesurfer.py::test_head_to_mni SKIPPED (Requires tes...) mne/tests/test_freesurfer.py::test_vertex_to_mni_fs_nibabel SKIPPED mne/tests/test_freesurfer.py::test_read_lta PASSED mne/tests/test_freesurfer.py::test_read_freesurfer_lut[None] SKIPPED mne/tests/test_freesurfer.py::test_read_freesurfer_lut[fname1] SKIPPED mne/tests/test_freesurfer.py::test_talxfm_rigid SKIPPED (Requires te...) mne/tests/test_import_nesting.py::test_import_nesting_hierarchy PASSED mne/tests/test_import_nesting.py::test_lazy_loading Running subprocess: /usr/bin/python3 -c import sys import mne out = set() # check scipy (Numba imports it to check the version) ok_scipy_submodules = {'version'} scipy_submodules = set(x.split('.')[1] for x in sys.modules.keys() if x.startswith('scipy.') and '__' not in x and not x.split('.')[1].startswith('_') and sys.modules[x] is not None) bad = scipy_submodules - ok_scipy_submodules if len(bad) > 0: out |= {'scipy submodules: %s' % list(bad)} # check sklearn and others for x in sys.modules.keys(): for key in ('sklearn', 'pandas', 'pyvista', 'matplotlib', 'dipy', 'nibabel', 'cupy', 'picard', 'pyvistaqt', 'pooch', 'tqdm', 'jinja2'): if x.startswith(key): x = '.'.join(x.split('.')[:2]) out |= {x} if len(out) > 0: print('\nFound un-nested import(s) for %s' % (sorted(out),), end='') exit(len(out)) # but this should still work mne.io.read_raw_fif assert "scipy.signal" in sys.modules, "scipy.signal not in sys.modules" PASSED mne/tests/test_label.py::test_copy PASSED mne/tests/test_label.py::test_label_subject PASSED mne/tests/test_label.py::test_label_addition PASSED mne/tests/test_label.py::test_label_fill_restrict[fname0] SKIPPED (R...) mne/tests/test_label.py::test_label_fill_restrict[fname1] SKIPPED (R...) mne/tests/test_label.py::test_label_io_and_time_course_estimates SKIPPED mne/tests/test_label.py::test_label_io SKIPPED (Requires testing dat...) mne/tests/test_label.py::test_annot_io SKIPPED (Requires testing dat...) mne/tests/test_label.py::test_morph_labels SKIPPED (Requires testing...) mne/tests/test_label.py::test_labels_to_stc SKIPPED (Requires testin...) mne/tests/test_label.py::test_read_labels_from_annot SKIPPED (Requir...) mne/tests/test_label.py::test_read_labels_from_annot_annot2labels SKIPPED mne/tests/test_label.py::test_write_labels_to_annot SKIPPED (Require...) mne/tests/test_label.py::test_split_label SKIPPED (Requires testing ...) mne/tests/test_label.py::test_stc_to_label SKIPPED (Requires testing...) mne/tests/test_label.py::test_morph SKIPPED (Requires testing dataset) mne/tests/test_label.py::test_grow_labels SKIPPED (Requires testing ...) mne/tests/test_label.py::test_random_parcellation SKIPPED (Requires ...) mne/tests/test_label.py::test_label_sign_flip SKIPPED (Requires test...) mne/tests/test_label.py::test_label_center_of_mass SKIPPED (Requires...) mne/tests/test_label.py::test_select_sources SKIPPED (Requires testi...) mne/tests/test_label.py::test_label_geometry[fname0-0.000657] SKIPPED mne/tests/test_label.py::test_label_geometry[fname1-0.003245] SKIPPED mne/tests/test_line_endings.py::test_line_endings PASSED mne/tests/test_misc.py::test_parse_ave PASSED mne/tests/test_morph.py::test_sourcemorph_consistency PASSED mne/tests/test_morph.py::test_sparse_morph SKIPPED (Requires testing...) mne/tests/test_morph.py::test_xhemi_morph SKIPPED (Requires testing ...) mne/tests/test_morph.py::test_surface_source_morph_round_trip[None-0.959-0.963-0-float] SKIPPED mne/tests/test_morph.py::test_surface_source_morph_round_trip[3-0.968-0.971-2-complex] SKIPPED mne/tests/test_morph.py::test_surface_source_morph_round_trip[nearest-0.98-0.99-0-float] SKIPPED mne/tests/test_morph.py::test_surface_source_morph_shortcut SKIPPED mne/tests/test_morph.py::test_surface_vector_source_morph SKIPPED (R...) mne/tests/test_morph.py::test_volume_source_morph_basic SKIPPED (Req...) mne/tests/test_morph.py::test_volume_source_morph_round_trip[sample-fsaverage-5.9-6.1-float-False] SKIPPED mne/tests/test_morph.py::test_volume_source_morph_round_trip[fsaverage-fsaverage-0.0-0.1-float-False] SKIPPED mne/tests/test_morph.py::test_volume_source_morph_round_trip[sample-sample-0.0-0.1-complex-False] SKIPPED mne/tests/test_morph.py::test_volume_source_morph_round_trip[sample-sample-0.0-0.1-float-True] SKIPPED mne/tests/test_morph.py::test_volume_source_morph_round_trip[sample-fsaverage-10-12-float-True] SKIPPED mne/tests/test_morph.py::test_morph_stc_dense SKIPPED (Requires test...) mne/tests/test_morph.py::test_morph_stc_sparse SKIPPED (Requires tes...) mne/tests/test_morph.py::test_volume_labels_morph[sl0-37-138-8] SKIPPED mne/tests/test_morph.py::test_volume_labels_morph[sl1-51-204-12] SKIPPED mne/tests/test_morph.py::test_volume_labels_morph[sl2-88-324-20] SKIPPED mne/tests/test_morph.py::test_mixed_source_morph[testing_data-False] SKIPPED mne/tests/test_morph.py::test_mixed_source_morph[testing_data-True] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine0-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine0-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine0-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine1-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape0-rand-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine0-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine0-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine0-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine1-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape1-rand-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine0-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine0-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine0-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine1-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine0-to_shape2-rand-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine0-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine0-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine0-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine1-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape0-rand-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine0-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine0-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine0-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine1-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape1-rand-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape2-from_affine0-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape2-from_affine0-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape2-from_affine0-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape2-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape2-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine1-to_shape2-from_affine1-from_shape2] SKIPPED 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mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-from_affine1-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[0-0-to_affine2-to_shape0-rand-from_shape2] SKIPPED 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mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape1-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape1-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape1-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape1-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape1-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape1-rand-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine0-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine0-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine0-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine1-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine1-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine1-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine2-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine2-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-from_affine2-from_shape2] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-rand-from_shape0] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-rand-from_shape1] SKIPPED mne/tests/test_morph.py::test_resample_equiv[1-1-rand-to_shape2-rand-from_shape2] SKIPPED mne/tests/test_morph_map.py::test_make_morph_maps SKIPPED (Requires ...) mne/tests/test_ola.py::test_interp_2pt PASSED mne/tests/test_ola.py::test_cola[1] Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.018 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.009 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.009 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.049 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.099 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.099 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.048 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.09 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.09 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.03 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.081 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.081 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.03 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.498 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.498 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.019 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.01 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.01 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.091 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.091 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.031 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.082 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.082 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.031 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.249 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and triang windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and triang windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.02 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.011 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.011 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.092 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.092 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.032 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.083 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.083 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.032 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and boxcar windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and boxcar windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and triang windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and triang windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing PASSED mne/tests/test_ola.py::test_cola[2] Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.018 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.009 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.009 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.049 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.099 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.099 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.048 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.09 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.09 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.03 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.081 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.081 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.03 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.498 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.498 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.019 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.01 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.01 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.091 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.091 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.031 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.082 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.082 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.031 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.249 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and triang windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and triang windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.02 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.011 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.011 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.092 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.092 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.032 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.083 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.083 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.032 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and boxcar windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and boxcar windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and triang windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and triang windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing PASSED mne/tests/test_ola.py::test_cola[3] Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.018 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.018 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.009 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.009 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.049 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.099 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.099 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.048 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.048 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.09 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.09 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.03 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.081 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.081 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.03 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.03 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.498 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.498 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.019 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.019 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.01 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.01 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.049 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.091 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.091 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.031 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.082 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.082 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.031 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.031 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.249 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.249 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and triang windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and triang windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.02 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.02 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.011 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.011 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 10 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.001 s will be lumped into the final window Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 19 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.092 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.092 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and hann windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and bartlett windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and boxcar windowing The final 0.032 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.083 s will be lumped into the final window Processing 9 data chunks of (at least) 0.1 s with 0.0 s overlap and boxcar windowing The final 0.083 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.032 s will be lumped into the final window Processing 18 data chunks of (at least) 0.1 s with 0.1 s overlap and triang windowing The final 0.032 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and hann windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and bartlett windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and boxcar windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and boxcar windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 1 data chunk of (at least) 0.5 s with 0.0 s overlap and boxcar windowing The final 0.499 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and triang windowing The final 0.248 s will be lumped into the final window Processing 2 data chunks of (at least) 0.5 s with 0.3 s overlap and triang windowing The final 0.248 s will be lumped into the final window Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and hann windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.5 s overlap and bartlett windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing Processing 1 data chunk of (at least) 1.0 s with 0.0 s overlap and boxcar windowing PASSED mne/tests/test_parallel.py::test_parallel_func[None] SKIPPED (MNE_FO...) mne/tests/test_parallel.py::test_parallel_func[1] SKIPPED (MNE_FORCE...) mne/tests/test_parallel.py::test_parallel_func[-1] SKIPPED (MNE_FORC...) mne/tests/test_parallel.py::test_parallel_func[loky 2] SKIPPED (MNE_...) mne/tests/test_parallel.py::test_parallel_func[threading 3] SKIPPED mne/tests/test_parallel.py::test_parallel_func[multiprocessing 4] SKIPPED mne/tests/test_proj.py::test_bad_proj Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 15 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 15 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 15 events and 421 original time points ... 1 bad epochs dropped Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 1) 1 projection items activated Using data from preloaded Raw for 15 events and 421 original time points ... 1 bad epochs dropped Not setting metadata 15 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 1) 1 projection items activated Using data from preloaded Raw for 15 events and 421 original time points ... 1 bad epochs dropped Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Computing rank from covariance with rank=None Using tolerance 1.5e-11 (2.2e-16 eps * 305 dim * 2.2e+02 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 5.3e-13 (2.2e-16 eps * 60 dim * 40 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector 8 projection items activated MAG regularization : 0.1 Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank={'meg': 302, 'eeg': 59} Using tolerance 3e-14 (2.2e-16 eps * 101 dim * 1.4 max singular value) Estimated rank (mag): 98 MAG: rank 98 computed from 101 data channels with 3 projectors Setting small MAG eigenvalues to zero (without PCA) GRAD regularization : 0.1 Computing rank from covariance with rank={'meg': 302, 'eeg': 59, 'mag': 98} Using tolerance 1e-11 (2.2e-16 eps * 204 dim * 2.2e+02 max singular value) Estimated rank (grad): 204 GRAD: rank 204 computed from 204 data channels with 0 projectors Setting small GRAD eigenvalues to zero (without PCA) EEG regularization : 0.1 Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank={'meg': 302, 'eeg': 59, 'mag': 98, 'grad': 204} Setting small EEG eigenvalues to zero (without PCA) PASSED mne/tests/test_proj.py::test_sensitivity_maps SKIPPED (Requires test...) mne/tests/test_proj.py::test_compute_proj_epochs Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 7 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Adding projection: planar-1--0.200-0.300-PCA-01 (exp var=74.6%) Adding projection: axial-1--0.200-0.300-PCA-01 (exp var=43.9%) No channels 'eeg' found. Skipping. Read a total of 2 projection items: planar-1--0.200-0.300-PCA-01 (1 x 204) idle axial-1--0.200-0.300-PCA-01 (1 x 102) idle Read a total of 2 projection items: planar-1--0.200-0.300-PCA-01 (1 x 204) idle axial-1--0.200-0.300-PCA-01 (1 x 102) idle Read a total of 2 projection items: planar-1--0.200-0.300-PCA-01 (1 x 204) idle axial-1--0.200-0.300-PCA-01 (1 x 102) idle 2 projection items activated Read a total of 2 projection items: planar-1--0.200-0.300-PCA-01 (1 x 204) idle axial-1--0.200-0.300-PCA-01 (1 x 102) idle Adding projection: planar--0.200-0.300-PCA-01 (exp var=69.4%) Adding projection: axial--0.200-0.300-PCA-01 (exp var=49.1%) No channels 'eeg' found. Skipping. Adding projection: planar-foobar-PCA-01 (exp var=74.6%) Adding projection: axial-foobar-PCA-01 (exp var=43.9%) No channels 'eeg' found. Skipping. 2 projection items activated Read a total of 2 projection items: planar-foobar-PCA-01 (1 x 204) active axial-foobar-PCA-01 (1 x 102) active Overwriting existing file. PASSED mne/tests/test_proj.py::test_compute_proj_raw Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 1502 = 0.000 ... 2.501 secs... Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Dropped 0/16 epochs Adding projection: planar-Raw-0.000-2.500-PCA-01 (exp var=71.4%) Adding projection: axial-Raw-0.000-2.500-PCA-01 (exp var=49.8%) 2 projection items activated Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_0.25_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_0.25_raw.fif [done] Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 5) Dropped 0/6 epochs Adding projection: planar-Raw-0.000-2.500-PCA-01 (exp var=71.4%) Adding projection: axial-Raw-0.000-2.500-PCA-01 (exp var=49.8%) 2 projection items activated Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_0.5_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_0.5_raw.fif [done] Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 5) Dropped 0/2 epochs Adding projection: planar-Raw-0.000-2.500-PCA-01 (exp var=72.3%) Adding projection: axial-Raw-0.000-2.500-PCA-01 (exp var=53.5%) 2 projection items activated Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_1_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_1_raw.fif [done] Not setting metadata 1 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 6) Dropped 0/1 epochs Adding projection: planar-Raw-0.000-2.500-PCA-01 (exp var=71.7%) Adding projection: axial-Raw-0.000-2.500-PCA-01 (exp var=51.6%) 2 projection items activated Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_2_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_2_raw.fif [done] Adding projection: planar-Raw-0.000-2.499-PCA-01 (exp var=71.5%) Adding projection: axial-Raw-0.000-2.499-PCA-01 (exp var=49.7%) 2 projection items activated Writing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_rawproj_continuous_raw.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_compute_proj_raw0/foo_rawproj_continuous_raw.fif [done] ... MNE_FORCE_SERIAL set. Processing in forced serial mode. Adding projection: planar-Raw-0.000-2.500-PCA-01 (exp var=71.5%) Adding projection: axial-Raw-0.000-2.500-PCA-01 (exp var=49.7%) 2 projection items activated Updating bad channels: [] -> ['MEG 0422', 'MEG 0433'] Not setting metadata 2 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 6) Dropped 0/2 epochs Adding projection: eeg-Raw-0.000-2.502-PCA-01 (exp var=97.6%) PASSED mne/tests/test_proj.py::test_proj_raw_duration[600.614990234375-1] Creating RawArray with float64 data, n_channels=30, n_times=10000 Range : 0 ... 9999 = 0.000 ... 16.648 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/16 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-16.010-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-16.010-PCA-02 (exp var=14.0%) Adding projection: eeg-Raw-0.000-16.010-PCA-03 (exp var=6.2%) Not setting metadata 16 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/16 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-16.010-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-16.010-PCA-02 (exp var=14.0%) Adding projection: eeg-Raw-0.000-16.010-PCA-03 (exp var=6.2%) No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-16.010-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-16.010-PCA-02 (exp var=14.0%) Adding projection: eeg-Raw-0.000-16.010-PCA-03 (exp var=6.2%) PASSED mne/tests/test_proj.py::test_proj_raw_duration[600.614990234375-1.5707963267948966] Creating RawArray with float64 data, n_channels=30, n_times=10000 Range : 0 ... 9999 = 0.000 ... 16.648 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 15 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/15 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-15.701-PCA-01 (exp var=79.9%) Adding projection: eeg-Raw-0.000-15.701-PCA-02 (exp var=13.9%) Adding projection: eeg-Raw-0.000-15.701-PCA-03 (exp var=6.2%) Not setting metadata 10 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/10 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-15.701-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-15.701-PCA-02 (exp var=14.0%) Adding projection: eeg-Raw-0.000-15.701-PCA-03 (exp var=6.2%) No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-15.701-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-15.701-PCA-02 (exp var=14.0%) Adding projection: eeg-Raw-0.000-15.701-PCA-03 (exp var=6.2%) PASSED mne/tests/test_proj.py::test_proj_raw_duration[1000.0-1] Creating RawArray with float64 data, n_channels=30, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 10 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/10 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-10.000-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-10.000-PCA-02 (exp var=13.9%) Adding projection: eeg-Raw-0.000-10.000-PCA-03 (exp var=6.2%) Not setting metadata 10 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/10 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-10.000-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-10.000-PCA-02 (exp var=13.9%) Adding projection: eeg-Raw-0.000-10.000-PCA-03 (exp var=6.2%) No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-10.000-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-10.000-PCA-02 (exp var=13.9%) Adding projection: eeg-Raw-0.000-10.000-PCA-03 (exp var=6.2%) PASSED mne/tests/test_proj.py::test_proj_raw_duration[1000.0-1.5707963267948966] Creating RawArray with float64 data, n_channels=30, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Not setting metadata 9 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/9 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-9.426-PCA-01 (exp var=79.9%) Adding projection: eeg-Raw-0.000-9.426-PCA-02 (exp var=13.9%) Adding projection: eeg-Raw-0.000-9.426-PCA-03 (exp var=6.2%) Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 1) Dropped 0/6 epochs No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-9.426-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-9.426-PCA-02 (exp var=14.0%) Adding projection: eeg-Raw-0.000-9.426-PCA-03 (exp var=6.2%) No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg-Raw-0.000-9.426-PCA-01 (exp var=79.8%) Adding projection: eeg-Raw-0.000-9.426-PCA-02 (exp var=14.0%) Adding projection: eeg-Raw-0.000-9.426-PCA-03 (exp var=6.2%) PASSED mne/tests/test_proj.py::test_make_eeg_average_ref_proj Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... PASSED mne/tests/test_proj.py::test_has_eeg_average_ref_proj[eeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... PASSED mne/tests/test_proj.py::test_has_eeg_average_ref_proj[ecog] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... ECoG channel type selected for re-referencing Adding average ECOG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. ECoG channel type selected for re-referencing Adding average ECOG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... PASSED mne/tests/test_proj.py::test_has_eeg_average_ref_proj[seeg] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... sEEG channel type selected for re-referencing Adding average SEEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. sEEG channel type selected for re-referencing Adding average SEEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... PASSED mne/tests/test_proj.py::test_has_eeg_average_ref_proj[dbs] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... DBS channel type selected for re-referencing Adding average DBS reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. DBS channel type selected for re-referencing Adding average DBS reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... PASSED mne/tests/test_proj.py::test_has_eeg_average_ref_proj[all] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Adding average EEG reference projection. 1 projection items deactivated Adding average ECOG reference projection. 1 projection items deactivated Adding average SEEG reference projection. 1 projection items deactivated Adding average DBS reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Adding average EEG/ECOG/SEEG/DBS reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Adding average ECOG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Adding average SEEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Adding average DBS reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Adding average EEG/ECOG/SEEG/DBS reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... PASSED mne/tests/test_proj.py::test_needs_eeg_average_ref_proj Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. PASSED mne/tests/test_proj.py::test_sss_proj Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 601 = 0.000 ... 1.001 secs... Maxwell filtering raw data No bad MEG channels Processing 34 gradiometers and 17 magnetometers Automatic origin fit: head of radius 91.0 mm Using origin -4.1, 16.0, 51.7 mm in the head frame Using 29/43 harmonic components for 0.000 (21/35 in, 8/8 out) Using loaded raw data Processing 1 data chunk [done] 6 projection items deactivated Created an SSP operator (subspace dimension = 6) 6 projection items activated SSP projectors applied... Computing rank from data with rank=None Using tolerance 3.6e-13 (2.2e-16 eps * 51 dim * 32 max singular value) Estimated rank (mag + grad): 21 MEG: rank 21 computed from 51 data channels with 6 projectors Created an SSP operator (subspace dimension = 6) Computing rank from covariance with rank=None Using tolerance 1.4e-14 (2.2e-16 eps * 51 dim * 1.2 max singular value) Estimated rank (mag + grad): 21 MEG: rank 21 computed from 51 data channels with 6 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 21 (30 small eigenvalues omitted) 3 projection items deactivated Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Computing rank from data with rank=None Using tolerance 4.7e-13 (2.2e-16 eps * 51 dim * 41 max singular value) Estimated rank (mag + grad): 18 MEG: rank 18 computed from 51 data channels with 3 projectors Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 2.7e-14 (2.2e-16 eps * 51 dim * 2.4 max singular value) Estimated rank (mag + grad): 18 MEG: rank 18 computed from 51 data channels with 3 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 18 (33 small eigenvalues omitted) PASSED mne/tests/test_proj.py::test_eq_ne Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated PASSED mne/tests/test_proj.py::test_setup_proj Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Adding average EEG reference projection. Created an SSP operator (subspace dimension = 1) 1 projection items activated PASSED mne/tests/test_proj.py::test_compute_proj_explained_variance SKIPPED mne/tests/test_rank.py::test_estimate_rank Using tolerance 2.2e-15 (2.2e-16 eps * 10 dim * 1 max singular value) Using tolerance 2.2e-15 (2.2e-16 eps * 10 dim * 1 max singular value) PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-norm-fname0-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 2402 = 0.000 ... 3.999 secs... Estimated rank (mag + grad + eeg): 366 Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 363 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-norm-fname1-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Reading 0 ... 4000 = 0.000 ... 4.000 secs... Found multiple SSS records. Using the first. Estimated rank (mag + grad + eeg): 180 Not setting metadata 4 matching events found No baseline correction applied Dropped 0/4 epochs Adding projection: planar-Raw-0.000-4.001-PCA-01 (exp var=77.2%) Adding projection: planar-Raw-0.000-4.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-4.001-PCA-01 (exp var=53.8%) Adding projection: axial-Raw-0.000-4.001-PCA-02 (exp var=26.9%) 4 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 180 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-norm-fname2-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 No projector specified for this dataset. Please consider the method self.add_proj. Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-norm-fname3-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Estimated rank (mag): 304 No projector specified for this dataset. Please consider the method self.add_proj. Estimated rank (mag): 304 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-scalings1-fname0-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 2402 = 0.000 ... 3.999 secs... Estimated rank (mag + grad + eeg): 366 Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 363 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-scalings1-fname1-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Reading 0 ... 4000 = 0.000 ... 4.000 secs... Found multiple SSS records. Using the first. Estimated rank (mag + grad + eeg): 180 Not setting metadata 4 matching events found No baseline correction applied Dropped 0/4 epochs Adding projection: planar-Raw-0.000-4.001-PCA-01 (exp var=77.2%) Adding projection: planar-Raw-0.000-4.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-4.001-PCA-01 (exp var=53.8%) Adding projection: axial-Raw-0.000-4.001-PCA-02 (exp var=26.9%) 4 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 180 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-scalings1-fname2-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 No projector specified for this dataset. Please consider the method self.add_proj. Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[absolute-0.0001-scalings1-fname3-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Estimated rank (mag): 304 No projector specified for this dataset. Please consider the method self.add_proj. Estimated rank (mag): 304 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-norm-fname0-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 2402 = 0.000 ... 3.999 secs... Estimated rank (mag + grad + eeg): 366 Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 363 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-norm-fname1-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Reading 0 ... 4000 = 0.000 ... 4.000 secs... Found multiple SSS records. Using the first. Estimated rank (mag + grad + eeg): 180 Not setting metadata 4 matching events found No baseline correction applied Dropped 0/4 epochs Adding projection: planar-Raw-0.000-4.001-PCA-01 (exp var=77.2%) Adding projection: planar-Raw-0.000-4.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-4.001-PCA-01 (exp var=53.8%) Adding projection: axial-Raw-0.000-4.001-PCA-02 (exp var=26.9%) 4 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 180 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-norm-fname2-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 No projector specified for this dataset. Please consider the method self.add_proj. Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-norm-fname3-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Estimated rank (mag): 304 No projector specified for this dataset. Please consider the method self.add_proj. Estimated rank (mag): 304 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-scalings1-fname0-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 2402 = 0.000 ... 3.999 secs... Estimated rank (mag + grad + eeg): 366 Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 363 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-scalings1-fname1-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Reading 0 ... 4000 = 0.000 ... 4.000 secs... Found multiple SSS records. Using the first. Estimated rank (mag + grad + eeg): 180 Not setting metadata 4 matching events found No baseline correction applied Dropped 0/4 epochs Adding projection: planar-Raw-0.000-4.001-PCA-01 (exp var=77.2%) Adding projection: planar-Raw-0.000-4.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-4.001-PCA-01 (exp var=53.8%) Adding projection: axial-Raw-0.000-4.001-PCA-02 (exp var=26.9%) 4 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Estimated rank (mag + grad + eeg): 180 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-scalings1-fname2-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 No projector specified for this dataset. Please consider the method self.add_proj. Removing 5 compensators from info because not all compensation channels were picked. Estimated rank (mag): 275 PASSED mne/tests/test_rank.py::test_raw_rank_estimation[relative-1e-06-scalings1-fname3-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Reading 0 ... 7200 = 0.000 ... 3.000 secs... Estimated rank (mag): 304 No projector specified for this dataset. Please consider the method self.add_proj. Estimated rank (mag): 304 PASSED mne/tests/test_rank.py::test_cov_rank_estimation[info-True-separate] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: planar-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: planar-Raw-0.000-5.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-5.001-PCA-01 (exp var=52.2%) Adding projection: axial-Raw-0.000-5.001-PCA-02 (exp var=26.1%) 4 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank='info' MEG: rank 303 after 3 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' MEG: rank 303 after 3 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 116 after 4 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 116 after 4 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_cov_rank_estimation[info-True-combined] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: meg-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: meg-Raw-0.000-5.001-PCA-02 (exp var=10.0%) 2 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank='info' MEG: rank 303 after 3 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' MEG: rank 303 after 3 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 118 after 2 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 118 after 2 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_cov_rank_estimation[info-False-separate] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: planar-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: planar-Raw-0.000-5.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-5.001-PCA-01 (exp var=52.2%) Adding projection: axial-Raw-0.000-5.001-PCA-02 (exp var=26.1%) 4 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank='info' MEG: rank 306 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' MEG: rank 306 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 120 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 120 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_cov_rank_estimation[info-False-combined] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: meg-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: meg-Raw-0.000-5.001-PCA-02 (exp var=10.0%) 2 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank='info' MEG: rank 306 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' MEG: rank 306 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 120 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank='info' Found multiple SSS records. Using the first. MEG: rank 120 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_cov_rank_estimation[None-True-separate] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: planar-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: planar-Raw-0.000-5.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-5.001-PCA-01 (exp var=52.2%) Adding projection: axial-Raw-0.000-5.001-PCA-02 (exp var=26.1%) 4 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank=None Using tolerance 1.7e-07 (2.2e-16 eps * 306 dim * 2.5e+06 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.7e-07 (2.2e-16 eps * 306 dim * 2.5e+06 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.5e-05 (2.2e-16 eps * 306 dim * 2.3e+08 max singular value) Estimated rank (mag + grad): 120 Found multiple SSS records. Using the first. MEG: rank 120 computed from 306 data channels with 4 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank=None Using tolerance 1.5e-05 (2.2e-16 eps * 306 dim * 2.3e+08 max singular value) Estimated rank (mag + grad): 120 Found multiple SSS records. Using the first. MEG: rank 120 computed from 306 data channels with 4 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_cov_rank_estimation[None-True-combined] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: meg-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: meg-Raw-0.000-5.001-PCA-02 (exp var=10.0%) 2 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank=None Using tolerance 1.7e-07 (2.2e-16 eps * 306 dim * 2.5e+06 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.7e-07 (2.2e-16 eps * 306 dim * 2.5e+06 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.5e-05 (2.2e-16 eps * 306 dim * 2.3e+08 max singular value) Estimated rank (mag + grad): 118 Found multiple SSS records. Using the first. MEG: rank 118 computed from 306 data channels with 2 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank=None Using tolerance 1.5e-05 (2.2e-16 eps * 306 dim * 2.3e+08 max singular value) Estimated rank (mag + grad): 118 Found multiple SSS records. Using the first. MEG: rank 118 computed from 306 data channels with 2 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_cov_rank_estimation[None-False-separate] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: planar-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: planar-Raw-0.000-5.001-PCA-02 (exp var=10.0%) Adding projection: axial-Raw-0.000-5.001-PCA-01 (exp var=52.2%) Adding projection: axial-Raw-0.000-5.001-PCA-02 (exp var=26.1%) 4 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank=None Using tolerance 3.7e-07 (2.2e-16 eps * 306 dim * 5.5e+06 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.7e-07 (2.2e-16 eps * 306 dim * 2.5e+06 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 0 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 0.00012 (2.2e-16 eps * 306 dim * 1.7e+09 max singular value) Estimated rank (mag + grad): 120 Found multiple SSS records. Using the first. MEG: rank 120 computed from 306 data channels with 0 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank=None Using tolerance 1.5e-05 (2.2e-16 eps * 306 dim * 2.3e+08 max singular value) Estimated rank (mag + grad): 120 Found multiple SSS records. Using the first. MEG: rank 120 computed from 306 data channels with 0 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_cov_rank_estimation[None-False-combined] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank=None Using tolerance 5.7e-14 (2.2e-16 eps * 59 dim * 4.4 max singular value) Estimated rank (eeg): 58 EEG: rank 58 computed from 59 data channels with 1 projector Setting small EEG eigenvalues to zero (without PCA) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found No baseline correction applied Dropped 0/5 epochs Adding projection: meg-Raw-0.000-5.001-PCA-01 (exp var=76.3%) Adding projection: meg-Raw-0.000-5.001-PCA-02 (exp var=10.0%) 2 projection items deactivated Using up to 119 segments Number of samples used : 14280 [done] Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Using up to 119 segments Number of samples used : 14280 [done] Using up to 25 segments Number of samples used : 5000 [done] Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Using up to 25 segments Number of samples used : 5000 [done] Computing rank from covariance with rank=None Using tolerance 3.7e-07 (2.2e-16 eps * 306 dim * 5.5e+06 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.7e-07 (2.2e-16 eps * 306 dim * 2.5e+06 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 0 projectors Using tolerance 1.1e-10 (2.2e-16 eps * 60 dim * 8.4e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 0.00012 (2.2e-16 eps * 306 dim * 1.7e+09 max singular value) Estimated rank (mag + grad): 120 Found multiple SSS records. Using the first. MEG: rank 120 computed from 306 data channels with 0 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. Computing rank from covariance with rank=None Using tolerance 1.5e-05 (2.2e-16 eps * 306 dim * 2.3e+08 max singular value) Estimated rank (mag + grad): 118 Found multiple SSS records. Using the first. MEG: rank 118 computed from 306 data channels with 0 projectors Using tolerance 1.1e-09 (2.2e-16 eps * 60 dim * 8.1e+04 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Found multiple SSS records. Using the first. Found multiple SSS records. Using the first. PASSED mne/tests/test_rank.py::test_rank_epochs[info-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 23 events and 601 original time points ... 0 bad epochs dropped Computing rank from data with rank='info' MEG: rank 303 after 3 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels PASSED mne/tests/test_rank.py::test_rank_epochs[info-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 23 events and 601 original time points ... 0 bad epochs dropped Computing rank from data with rank='info' MEG: rank 306 after 0 projectors applied to 306 channels EEG: rank 60 after 0 projectors applied to 60 channels PASSED mne/tests/test_rank.py::test_rank_epochs[None-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 23 events and 601 original time points ... 0 bad epochs dropped Computing rank from data with rank=None Using tolerance 1.3e-10 (2.2e-16 eps * 306 dim * 1.9e+03 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 7.9e-11 (2.2e-16 eps * 60 dim * 5.9e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors PASSED mne/tests/test_rank.py::test_rank_epochs[None-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 23 events and 601 original time points ... 0 bad epochs dropped Computing rank from data with rank=None Using tolerance 1.3e-10 (2.2e-16 eps * 306 dim * 1.9e+03 max singular value) Estimated rank (mag + grad): 306 MEG: rank 306 computed from 306 data channels with 0 projectors Using tolerance 7.9e-11 (2.2e-16 eps * 60 dim * 5.9e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors PASSED mne/tests/test_rank.py::test_maxfilter_get_rank[absolute-float32-0-combined-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[absolute-float32-0-combined-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[absolute-float32-10-combined-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[absolute-float32-10-combined-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[absolute-float32-10-separate-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[absolute-float32-10-separate-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-float32-0-combined-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-float32-0-combined-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-float32-10-combined-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-float32-10-combined-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-float32-10-separate-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-float32-10-separate-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-1e-05-0-combined-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-1e-05-0-combined-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-1e-05-10-combined-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-1e-05-10-combined-fname1-67] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-1e-05-10-separate-fname0-120] SKIPPED mne/tests/test_rank.py::test_maxfilter_get_rank[relative-1e-05-10-separate-fname1-67] SKIPPED mne/tests/test_rank.py::test_explicit_bads_pick Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Using up to 25 segments Number of samples used : 3000 [done] Computing rank from covariance with rank=None Using tolerance 1.6e-11 (2.2e-16 eps * 306 dim * 2.4e+02 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 1.4e-12 (2.2e-16 eps * 60 dim * 1.1e+02 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Using up to 25 segments Number of samples used : 3000 [done] Computing rank from covariance with rank=None Using tolerance 1.6e-11 (2.2e-16 eps * 305 dim * 2.4e+02 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 1.2e-12 (2.2e-16 eps * 57 dim * 94 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 57 data channels with 0 projectors Using up to 25 segments Number of samples used : 3000 [done] Computing rank from covariance with rank=None Using tolerance 1.6e-11 (2.2e-16 eps * 305 dim * 2.4e+02 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 1.2e-12 (2.2e-16 eps * 57 dim * 94 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 57 data channels with 0 projectors Computing rank from data with rank=None Using tolerance 5.7e-11 (2.2e-16 eps * 306 dim * 8.4e+02 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 3.6e-11 (2.2e-16 eps * 60 dim * 2.7e+03 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors Computing rank from data with rank=None Using tolerance 5.7e-11 (2.2e-16 eps * 305 dim * 8.4e+02 max singular value) Estimated rank (mag + grad): 302 MEG: rank 302 computed from 305 data channels with 3 projectors Using tolerance 3.4e-11 (2.2e-16 eps * 57 dim * 2.7e+03 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 57 data channels with 0 projectors PASSED mne/tests/test_read_vectorview_selection.py::test_read_vectorview_selection Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. PASSED mne/tests/test_source_estimate.py::test_stc_baseline_correction SKIPPED mne/tests/test_source_estimate.py::test_spatial_inter_hemi_adjacency SKIPPED mne/tests/test_source_estimate.py::test_volume_stc SKIPPED (Requires...) mne/tests/test_source_estimate.py::test_save_stc_as_gifti SKIPPED (R...) mne/tests/test_source_estimate.py::test_stc_as_volume SKIPPED (Requi...) mne/tests/test_source_estimate.py::test_save_vol_stc_as_nifti SKIPPED mne/tests/test_source_estimate.py::test_expand SKIPPED (Requires tes...) mne/tests/test_source_estimate.py::test_stc_snr SKIPPED (Requires te...) mne/tests/test_source_estimate.py::test_stc_attributes PASSED mne/tests/test_source_estimate.py::test_io_stc Writing STC to disk... [done] PASSED mne/tests/test_source_estimate.py::test_io_stc_h5[True-True] SKIPPED mne/tests/test_source_estimate.py::test_io_stc_h5[True-False] SKIPPED mne/tests/test_source_estimate.py::test_io_stc_h5[False-True] SKIPPED mne/tests/test_source_estimate.py::test_io_stc_h5[False-False] SKIPPED mne/tests/test_source_estimate.py::test_io_w Writing STC to disk (w format)... [done] Writing STC to disk (w format)... [done] PASSED mne/tests/test_source_estimate.py::test_stc_arithmetic PASSED mne/tests/test_source_estimate.py::test_stc_methods[fft-scalar] SKIPPED mne/tests/test_source_estimate.py::test_stc_methods[fft-vector] SKIPPED mne/tests/test_source_estimate.py::test_stc_methods[polyphase-scalar] SKIPPED mne/tests/test_source_estimate.py::test_stc_methods[polyphase-vector] SKIPPED mne/tests/test_source_estimate.py::test_stc_resamp_noop SKIPPED (Req...) mne/tests/test_source_estimate.py::test_center_of_mass SKIPPED (Requ...) mne/tests/test_source_estimate.py::test_extract_label_time_course[False-surface] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course[False-mixed] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course[True-surface] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course[True-mixed] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course_volume[str-False-False-False-head-meth] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course_volume[str-False-False-str-mri-func] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course_volume[str-False-True-int-mri-func] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course_volume[str-True-False-False-mri-func] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course_volume[list-True-False-False-mri-func] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course_volume[dict-True-False-False-mri-func] SKIPPED mne/tests/test_source_estimate.py::test_extract_label_time_course_equiv SKIPPED mne/tests/test_source_estimate.py::test_transform_data PASSED mne/tests/test_source_estimate.py::test_transform PASSED mne/tests/test_source_estimate.py::test_spatio_temporal_tris_adjacency -- number of adjacent vertices : 6 PASSED mne/tests/test_source_estimate.py::test_spatio_temporal_src_adjacency SKIPPED mne/tests/test_source_estimate.py::test_to_data_frame SKIPPED (could...) mne/tests/test_source_estimate.py::test_to_data_frame_index[time] SKIPPED mne/tests/test_source_estimate.py::test_to_data_frame_index[index1] SKIPPED mne/tests/test_source_estimate.py::test_to_data_frame_index[None] SKIPPED mne/tests/test_source_estimate.py::test_get_peak[5-False-surface] PASSED mne/tests/test_source_estimate.py::test_get_peak[5-False-mixed] PASSED mne/tests/test_source_estimate.py::test_get_peak[5-False-volume] PASSED mne/tests/test_source_estimate.py::test_get_peak[5-True-surface] PASSED mne/tests/test_source_estimate.py::test_get_peak[5-True-mixed] PASSED mne/tests/test_source_estimate.py::test_get_peak[5-True-volume] PASSED mne/tests/test_source_estimate.py::test_get_peak[1-False-surface] PASSED mne/tests/test_source_estimate.py::test_get_peak[1-False-mixed] PASSED mne/tests/test_source_estimate.py::test_get_peak[1-False-volume] PASSED mne/tests/test_source_estimate.py::test_get_peak[1-True-surface] PASSED mne/tests/test_source_estimate.py::test_get_peak[1-True-mixed] PASSED mne/tests/test_source_estimate.py::test_get_peak[1-True-volume] PASSED mne/tests/test_source_estimate.py::test_mixed_stc SKIPPED (Requires ...) mne/tests/test_source_estimate.py::test_vec_stc_basic[float32-VectorSourceEstimate-surf] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[float32-VolVectorSourceEstimate-vol] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[float32-VolVectorSourceEstimate-discrete] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[float32-MixedVectorSourceEstimate-mixed] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[float64-VectorSourceEstimate-surf] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[float64-VolVectorSourceEstimate-vol] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[float64-VolVectorSourceEstimate-discrete] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[float64-MixedVectorSourceEstimate-mixed] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex64-VectorSourceEstimate-surf] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex64-VolVectorSourceEstimate-vol] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex64-VolVectorSourceEstimate-discrete] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex64-MixedVectorSourceEstimate-mixed] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex128-VectorSourceEstimate-surf] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex128-VolVectorSourceEstimate-vol] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex128-VolVectorSourceEstimate-discrete] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_basic[complex128-MixedVectorSourceEstimate-mixed] SKIPPED mne/tests/test_source_estimate.py::test_source_estime_project[True] PASSED mne/tests/test_source_estimate.py::test_source_estime_project[False] PASSED mne/tests/test_source_estimate.py::test_source_estime_project_label SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_free[testing_data-None] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_free[testing_data-normal] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_free[testing_data-vector] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_free_surf[testing_data-None] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_free_surf[testing_data-normal] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_free_surf[testing_data-vector] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_fixed[testing_data-None] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_fixed[testing_data-normal] SKIPPED mne/tests/test_source_estimate.py::test_vec_stc_inv_fixed[testing_data-vector] SKIPPED mne/tests/test_source_estimate.py::test_epochs_vector_inverse SKIPPED mne/tests/test_source_estimate.py::test_vol_adjacency SKIPPED (Requi...) mne/tests/test_source_estimate.py::test_spatial_src_adjacency SKIPPED mne/tests/test_source_estimate.py::test_vol_mask SKIPPED (Requires t...) mne/tests/test_source_estimate.py::test_stc_near_sensors SKIPPED (Re...) mne/tests/test_source_estimate.py::test_stc_near_sensors_picks SKIPPED mne/tests/test_source_estimate.py::test_scale_morph_labels[scale0-volume] SKIPPED mne/tests/test_source_estimate.py::test_scale_morph_labels[scale0-surface] SKIPPED mne/tests/test_source_estimate.py::test_scale_morph_labels[1.0-volume] SKIPPED mne/tests/test_source_estimate.py::test_scale_morph_labels[1.0-surface] SKIPPED mne/tests/test_source_estimate.py::test_scale_morph_labels[0.9-volume] SKIPPED mne/tests/test_source_estimate.py::test_scale_morph_labels[0.9-surface] SKIPPED mne/tests/test_source_estimate.py::test_label_extraction_subject[surface] SKIPPED mne/tests/test_source_estimate.py::test_label_extraction_subject[volume] SKIPPED mne/tests/test_source_estimate.py::test_apply_function_stc ... MNE_FORCE_SERIAL set. Processing in forced serial mode. PASSED mne/tests/test_surface.py::test_helmet Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Read 5 compensation matrices PASSED mne/tests/test_surface.py::test_head SKIPPED (Requires testing dataset) mne/tests/test_surface.py::test_fast_cross_3d PASSED mne/tests/test_surface.py::test_compute_nearest PASSED mne/tests/test_surface.py::test_io_surface SKIPPED (Requires testing...) mne/tests/test_surface.py::test_read_curv SKIPPED (Requires testing ...) mne/tests/test_surface.py::test_decimate_surface_vtk[4] SKIPPED (cou...) mne/tests/test_surface.py::test_decimate_surface_vtk[3] SKIPPED (cou...) mne/tests/test_surface.py::test_decimate_surface_vtk[2] SKIPPED (cou...) mne/tests/test_surface.py::test_decimate_surface_sphere SKIPPED (Req...) mne/tests/test_surface.py::test_dig_mri_distances[auto-False-72-bounds0-0] SKIPPED mne/tests/test_surface.py::test_dig_mri_distances[dig_kinds1-False-146-bounds1-1] SKIPPED mne/tests/test_surface.py::test_dig_mri_distances[dig_kinds2-True-139-bounds2-0] SKIPPED mne/tests/test_surface.py::test_normal_orth PASSED mne/tests/test_surface.py::test_marching_cubes[0-1-F-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-1-F-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-1-F->i4] SKIPPED (c...) mne/tests/test_surface.py::test_marching_cubes[0-1-C-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-1-C-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-1-C->i4] SKIPPED (c...) mne/tests/test_surface.py::test_marching_cubes[0-12-F-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-12-F-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-12-F->i4] SKIPPED (...) mne/tests/test_surface.py::test_marching_cubes[0-12-C-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-12-C-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0-12-C->i4] SKIPPED (...) mne/tests/test_surface.py::test_marching_cubes[0.9-1-F-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-1-F-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-1-F->i4] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-1-C-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-1-C-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-1-C->i4] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-12-F-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-12-F-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-12-F->i4] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-12-C-float64] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-12-C-uint16] SKIPPED mne/tests/test_surface.py::test_marching_cubes[0.9-12-C->i4] SKIPPED mne/tests/test_surface.py::test_get_montage_volume_labels SKIPPED (R...) mne/tests/test_surface.py::test_voxel_neighbors PASSED mne/tests/test_surface.py::test_project_onto_surface[accurate-False] SKIPPED mne/tests/test_surface.py::test_project_onto_surface[accurate-True] SKIPPED mne/tests/test_surface.py::test_project_onto_surface[nearest-False] SKIPPED mne/tests/test_surface.py::test_project_onto_surface[nearest-True] SKIPPED mne/tests/test_transforms.py::test_tps Computing TPS warp Centering data Using centers {np.array_str(src_center, None, 3)} -> {np.array_str(dest_center, None, 3)} Converting to spherical coordinates Computing spherical harmonic approximation with order 4 Matching 1026 points (oct5) on smoothed surfaces [done] Transforming 200 points PASSED mne/tests/test_transforms.py::test_get_trans SKIPPED (Requires testi...) mne/tests/test_transforms.py::test_io_trans SKIPPED (Requires testin...) mne/tests/test_transforms.py::test_get_ras_to_neuromag_trans PASSED mne/tests/test_transforms.py::test_sph_to_cart PASSED mne/tests/test_transforms.py::test_polar_to_cartesian PASSED mne/tests/test_transforms.py::test_topo_to_sph PASSED mne/tests/test_transforms.py::test_rotation PASSED mne/tests/test_transforms.py::test_rotation3d_align_z_axis PASSED mne/tests/test_transforms.py::test_combine SKIPPED (Requires testing...) mne/tests/test_transforms.py::test_quaternions Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Read 5 compensation matrices PASSED mne/tests/test_transforms.py::test_vector_rotation PASSED mne/tests/test_transforms.py::test_average_quats PASSED mne/tests/test_transforms.py::test_fs_xfm[fsaverage] SKIPPED (Requir...) mne/tests/test_transforms.py::test_fs_xfm[sample] SKIPPED (Requires ...) mne/tests/test_transforms.py::test_fit_matched_points[True-0.25] 0 1 2 3 4 PASSED mne/tests/test_transforms.py::test_fit_matched_points[True-1] 0 1 2 3 4 PASSED mne/tests/test_transforms.py::test_fit_matched_points[False-0.25] PASSED mne/tests/test_transforms.py::test_fit_matched_points[False-1] 0 1 2 3 4 PASSED mne/tests/test_transforms.py::test_euler PASSED mne/tests/test_transforms.py::test_volume_registration SKIPPED (Requ...) mne/tests/test_transforms.py::test_displacement_field PASSED mne/time_frequency/tests/test_ar.py::test_yule_walker SKIPPED (could...) mne/time_frequency/tests/test_ar.py::test_ar_raw Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 1201 = 0.000 ... 2.000 secs... PASSED mne/time_frequency/tests/test_csd.py::test_csd PASSED mne/time_frequency/tests/test_csd.py::test_csd_repr PASSED mne/time_frequency/tests/test_csd.py::test_csd_mean PASSED mne/time_frequency/tests/test_csd.py::test_csd_get_frequency_index PASSED mne/time_frequency/tests/test_csd.py::test_csd_pick_frequency PASSED mne/time_frequency/tests/test_csd.py::test_csd_get_data PASSED mne/time_frequency/tests/test_csd.py::test_csd_save SKIPPED (could n...) mne/time_frequency/tests/test_csd.py::test_csd_pickle PASSED mne/time_frequency/tests/test_csd.py::test_pick_channels_csd PASSED mne/time_frequency/tests/test_csd.py::test_sym_mat_to_vector PASSED mne/time_frequency/tests/test_csd.py::test_csd_fourier Not setting metadata 1 matching events found Applying baseline correction (mode: mean) 0 projection items activated Computing cross-spectral density from epochs... 0%| | CSD epoch blocks : 0/1 [00:00 3 NOTE: tfr_stockwell() is a legacy function. New code should use .compute_tfr(method="stockwell", freqs="auto"). PASSED mne/time_frequency/tests/test_stockwell.py::test_stockwell_check_input The input signal is shorter (127) than "n_fft" (128). Applying zero padding. PASSED mne/time_frequency/tests/test_stockwell.py::test_stockwell_st_no_zero_pad PASSED mne/time_frequency/tests/test_stockwell.py::test_stockwell_core PASSED mne/time_frequency/tests/test_stockwell.py::test_stockwell_api Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated NOTE: tfr_stockwell() is a legacy function. New code should use .compute_tfr(method="stockwell", freqs="auto"). Loading data for 7 events and 421 original time points ... 0 bad epochs dropped The input signal is shorter (421) than "n_fft" (512). Applying zero padding. NOTE: tfr_stockwell() is a legacy function. New code should use .compute_tfr(method="stockwell", freqs="auto"). The input signal is shorter (421) than "n_fft" (512). Applying zero padding. NOTE: tfr_stockwell() is a legacy function. New code should use .compute_tfr(method="stockwell", freqs="auto"). Loading data for 7 events and 421 original time points ... The input signal is shorter (421) than "n_fft" (512). Applying zero padding. NOTE: tfr_stockwell() is a legacy function. New code should use .compute_tfr(method="stockwell", freqs="auto"). The input signal is shorter (421) than "n_fft" (512). Applying zero padding. NOTE: tfr_stockwell() is a legacy function. New code should use .compute_tfr(method="stockwell", freqs="auto"). Loading data for 7 events and 421 original time points ... The input signal is shorter (421) than "n_fft" (512). Applying zero padding. NOTE: tfr_stockwell() is a legacy function. New code should use .compute_tfr(method="stockwell", freqs="auto"). The input signal is shorter (421) than "n_fft" (512). Applying zero padding. PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_ctf Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Compensator constructed to change 0 -> 3 Not setting metadata 2 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). Removing 5 compensators from info because not all compensation channels were picked. Loading data for 2 events and 1681 original time points ... 1 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.4s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.8s NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Removing 5 compensators from info because not all compensation channels were picked. Loading data for 1 events and 1681 original time points ... [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.3s [Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.6s PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[7-10.0-1000.0] PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[7-10.0-103.1415926535898] PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[7-3.141592653589793-1000.0] PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[7-3.141592653589793-103.1415926535898] PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[2-10.0-1000.0] PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[2-10.0-103.1415926535898] PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[2-3.141592653589793-1000.0] PASSED mne/time_frequency/tests/test_tfr.py::test_morlet[2-3.141592653589793-103.1415926535898] PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_morlet Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 420 original time points ... 0 bad epochs dropped Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 1 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). inst is Evoked, setting `average=False` NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... 0 bad epochs dropped NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). inst is Evoked, setting `average=False` NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... Loading data for 7 events and 420 original time points ... ['MEG 0113', 'MEG 0112'] Applying baseline correction (mode: logratio) Identifying common channels ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 1 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 1 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... Identifying common channels ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 1 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 7 events and 420 original time points ... PASSED mne/time_frequency/tests/test_tfr.py::test_dpsswavelet PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_multitaper Not setting metadata 3 matching events found No baseline correction applied 0 projection items activated 0 bad epochs dropped NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). Not setting metadata 1 matching events found No baseline correction applied NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). NOTE: tfr_multitaper() is a legacy function. New code should use .compute_tfr(method="multitaper"). PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[4-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[4-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[4-stockwell] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Requested `method="stockwell"` so ignoring parameter `average=False`. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.2s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[decim1-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[decim1-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[decim1-stockwell] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Requested `method="stockwell"` so ignoring parameter `average=False`. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.2s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[decim2-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[decim2-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_decim_and_shift_time[decim2-stockwell] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Requested `method="stockwell"` so ignoring parameter `average=False`. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.2s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_io[raw_tfr] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s SKIPPED (...) mne/time_frequency/tests/test_tfr.py::test_tfr_io[epochs_tfr] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s SKIPPED mne/time_frequency/tests/test_tfr.py::test_tfr_io[average_tfr] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s SKIPPED mne/time_frequency/tests/test_tfr.py::test_roundtrip_from_legacy_func Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_tfr_init Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/time_frequency/tests/test_tfr.py::test_epochstfr_init_errors Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_init_errors[morlet-None-EpochsTFR got unsupported parameter value freqs=None.] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_init_errors[None-freqs1-got unsupported parameter value method=None.] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_init_errors[None-None-got unsupported parameter values method=None and freqs=None.] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped PASSED mne/time_frequency/tests/test_tfr.py::test_equalize_epochs_tfr_counts Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s Dropped 1 epoch: 0 Dropped 0 epochs: PASSED mne/time_frequency/tests/test_tfr.py::test_dB_computation No baseline correction applied No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_plot No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_add_channels PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 7 events and 420 original time points ... 0 bad epochs dropped PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_correct[1-multitaper] PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_correct[1-morlet] PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_correct[decim1-multitaper] PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_correct[decim1-morlet] PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_correct[3-multitaper] PASSED mne/time_frequency/tests/test_tfr.py::test_compute_tfr_correct[3-morlet] PASSED mne/time_frequency/tests/test_tfr.py::test_averaging_epochsTFR Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 1 events and 420 original time points ... 0 bad epochs dropped PASSED mne/time_frequency/tests/test_tfr.py::test_averaging_freqsandtimes_epochsTFR Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). Loading data for 1 events and 420 original time points ... 0 bad epochs dropped PASSED mne/time_frequency/tests/test_tfr.py::test_epochstfr_getitem[0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 662 original time points ... 0 bad epochs dropped SKIPPED mne/time_frequency/tests/test_tfr.py::test_epochstfr_getitem[2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 662 original time points ... 0 bad epochs dropped SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame SKIPPED (co...) mne/time_frequency/tests/test_tfr.py::test_to_data_frame_index[time] SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame_index[index1] SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame_index[index2] SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame_index[index3] SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame_index[None] SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame_time_format[None] SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame_time_format[ms] SKIPPED mne/time_frequency/tests/test_tfr.py::test_to_data_frame_time_format[timedelta] SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[mag-power-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[mag-power-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[mag-phase-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[mag-phase-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[mag-complex-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[mag-complex-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks1-power-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks1-power-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks1-phase-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks1-phase-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks1-complex-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks1-complex-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks2-power-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks2-power-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks2-phase-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks2-phase-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks2-complex-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_raw_compute_tfr[picks2-complex-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[average,no_itc-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[average,no_itc-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[average,itc-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[average,itc-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.2s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[no_average,agg_freqs-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[no_average,agg_freqs-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[no_average,agg_epochs-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[no_average,agg_epochs-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[no_average,agg_times-morlet] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_average_itc[no_average,agg_times-multitaper] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_vs_evoked_compute_tfr Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_method_kw[morlet-nondefaults] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_method_kw[multitaper-nondefaults] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.3s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_method_kw[stockwell-nondefaults] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 3.5s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_stockwell[False-freqauto] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Requested `method="stockwell"` so ignoring parameter `average=False`. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 3.5s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_stockwell[False-fminfmax] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Requested `method="stockwell"` so ignoring parameter `average=False`. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.2s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_stockwell[True-freqauto] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Requested `method="stockwell"` so ignoring parameter `average=False`. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 5.4s PASSED mne/time_frequency/tests/test_tfr.py::test_epochs_compute_tfr_stockwell[True-fminfmax] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Requested `method="stockwell"` so ignoring parameter `average=False`. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. The input signal is shorter (662) than "n_fft" (1024). Applying zero padding. [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.4s PASSED mne/time_frequency/tests/test_tfr.py::test_epochstfr_iter_evoked[False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_epochstfr_iter_evoked[True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_proj Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_copy Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_apply_baseline[mean] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Applying baseline correction (mode: mean) PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_apply_baseline[ratio] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Applying baseline correction (mode: ratio) PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_apply_baseline[logratio] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Applying baseline correction (mode: logratio) PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_apply_baseline[percent] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Applying baseline correction (mode: percent) PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_apply_baseline[zscore] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Applying baseline correction (mode: zscore) PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_apply_baseline[zlogratio] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Applying baseline correction (mode: zlogratio) PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_arithmetic Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_repr_html Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_combine[mean_of_mags] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_combine[rms_of_grads] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_combine[single_channel] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_combine[two_separate_channels] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_extras Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_interactivity Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topo[raw_tfr-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topo[raw_tfr-grad] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topo[epochs_tfr-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topo[average_tfr-mag] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied PASSED mne/time_frequency/tests/test_tfr.py::test_tfr_plot_topo[average_tfr-grad] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s No baseline correction applied PASSED mne/utils/_logging.py::mne.utils._logging.use_log_level PASSED mne/utils/_logging.py::mne.utils._logging.verbose PASSED mne/utils/docs.py::mne.utils.docs.copy_doc PASSED mne/utils/docs.py::mne.utils.docs.copy_function_doc_to_method_doc PASSED mne/utils/misc.py::mne.utils.misc.pformat PASSED mne/utils/mixin.py::mne.utils.mixin.GetEpochsMixin.__iter__ SKIPPED mne/utils/mixin.py::mne.utils.mixin.GetEpochsMixin.__len__ SKIPPED (...) mne/utils/tests/test_bunch.py::test_pickle PASSED mne/utils/tests/test_check.py::test_check SKIPPED (Requires testing ...) mne/utils/tests/test_check.py::test_check_fname_suffixes[_meg.fif] SKIPPED mne/utils/tests/test_check.py::test_check_fname_suffixes[_eeg.fif] SKIPPED mne/utils/tests/test_check.py::test_check_fname_suffixes[_ieeg.fif] SKIPPED mne/utils/tests/test_check.py::test_check_fname_suffixes[_meg.fif.gz] SKIPPED mne/utils/tests/test_check.py::test_check_fname_suffixes[_eeg.fif.gz] SKIPPED mne/utils/tests/test_check.py::test_check_fname_suffixes[_ieeg.fif.gz] SKIPPED mne/utils/tests/test_check.py::test_check_info_inv SKIPPED (Requires...) mne/utils/tests/test_check.py::test_check_option PASSED mne/utils/tests/test_check.py::test_path_like PASSED mne/utils/tests/test_check.py::test_validate_type PASSED mne/utils/tests/test_check.py::test_check_range PASSED mne/utils/tests/test_check.py::test_suggest SKIPPED (Requires testin...) mne/utils/tests/test_check.py::test_on_missing PASSED mne/utils/tests/test_check.py::test_safe_input PASSED mne/utils/tests/test_check.py::test_check_ch_locs SKIPPED (Requires ...) mne/utils/tests/test_check.py::test_strip_dev[1.23.0.dev0+782.g1168868df6-1.23-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[1.9.0.dev0+1485.b06254e-1.9-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[3.6.0.dev1651+g30d6161406-3.6-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[1.1.dev0-1.1-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[0.56.0dev0+39.gef1ba4c10-0.56-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[9.1.0.rc1-9.1-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[0.3dev0-0.3-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[0.2.2.dev0-0.2.2-False] PASSED mne/utils/tests/test_check.py::test_strip_dev[3.2.2+150.g1e93bd5d-3.2.2-True] PASSED mne/utils/tests/test_check.py::test_strip_dev[1.2.3-1.2.3-True] PASSED mne/utils/tests/test_check.py::test_strip_dev[1.2-1.2-True] PASSED mne/utils/tests/test_check.py::test_strip_dev[1-1-True] PASSED mne/utils/tests/test_check.py::test_check_sphere_verbose SKIPPED (Re...) mne/utils/tests/test_config.py::test_config Attempting to create new mne-python configuration file: /tmp/pytest-of-pbuilder1/pytest-0/test_config0/.mne/mne-python.json PASSED mne/utils/tests/test_config.py::test_sys_info_basic PASSED mne/utils/tests/test_config.py::test_sys_info_complete PASSED mne/utils/tests/test_config.py::test_sys_info_qt_browser SKIPPED (co...) mne/utils/tests/test_config.py::test_get_subjects_dir PASSED mne/utils/tests/test_config.py::test_sys_info_check_outdated SKIPPED mne/utils/tests/test_config.py::test_sys_info_check_other PASSED mne/utils/tests/test_docs.py::test_doc_filling[grade_to_tris] PASSED mne/utils/tests/test_docs.py::test_deprecated_alias PASSED mne/utils/tests/test_docs.py::test_deprecated_and_legacy[deprecated-deprecated_class-deprecated_func] PASSED mne/utils/tests/test_docs.py::test_deprecated_and_legacy[legacy-legacy_class-legacy_func] PASSED mne/utils/tests/test_docs.py::test_copy_doc PASSED mne/utils/tests/test_docs.py::test_copy_function_doc_to_method_doc PASSED mne/utils/tests/test_docs.py::test_open_docs PASSED mne/utils/tests/test_docs.py::test_linkcode_resolve PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-True-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-False-False-False-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-True-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-False-False-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-True-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_sym_mat_pow-True-True-False-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-True-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-False-False-False-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-True-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-False-False-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-True-4-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-3-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-3-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-3-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-3-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-3-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-3-4-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-4-2-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-4-2-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-4-3-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-4-3-complex128] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-4-4-float64] PASSED mne/utils/tests/test_linalg.py::test_pos_semidef_inv[_reg_pinv-True-True-False-4-4-complex128] PASSED mne/utils/tests/test_logging.py::test_frame_info PASSED mne/utils/tests/test_logging.py::test_how_to_deal_with_warnings PASSED mne/utils/tests/test_logging.py::test_logging_options PASSED mne/utils/tests/test_logging.py::test_verbose_method[True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated PASSED mne/utils/tests/test_logging.py::test_verbose_method[False] PASSED mne/utils/tests/test_logging.py::test_warn Creating RawArray with float64 data, n_channels=1, n_times=1 Range : 0 ... 0 = 0.000 ... 0.000 secs Ready. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_warn0/bad.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_warn0/bad.fif [done] Overwriting existing file. Writing /tmp/pytest-of-pbuilder1/pytest-0/test_warn0/bad.fif Closing /tmp/pytest-of-pbuilder1/pytest-0/test_warn0/bad.fif [done] PASSED mne/utils/tests/test_logging.py::test_get_call_line PASSED mne/utils/tests/test_logging.py::test_verbose_strictness PASSED mne/utils/tests/test_logging.py::test_verbose_threads[1] PASSED mne/utils/tests/test_logging.py::test_verbose_threads[2] PASSED mne/utils/tests/test_misc.py::test_sizeof_fmt PASSED mne/utils/tests/test_misc.py::test_html_repr PASSED mne/utils/tests/test_misc.py::test_run_subprocess[True-stdout] PASSED mne/utils/tests/test_misc.py::test_run_subprocess[True-stderr] PASSED mne/utils/tests/test_misc.py::test_run_subprocess[False-stdout] PASSED mne/utils/tests/test_misc.py::test_run_subprocess[False-stderr] PASSED mne/utils/tests/test_misc.py::test_clean_names PASSED mne/utils/tests/test_mixin.py::test_decimate SKIPPED (Requires testi...) mne/utils/tests/test_numerics.py::test_get_inst_data Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 601 = 0.000 ... 1.001 secs... Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using data from preloaded Raw for 2 events and 300 original time points ... 0 bad epochs dropped NOTE: tfr_morlet() is a legacy function. New code should use .compute_tfr(method="morlet"). inst is Evoked, setting `average=False` PASSED mne/utils/tests/test_numerics.py::test_hashfunc PASSED mne/utils/tests/test_numerics.py::test_sum_squared PASSED mne/utils/tests/test_numerics.py::test_compute_corr PASSED mne/utils/tests/test_numerics.py::test_create_slices PASSED mne/utils/tests/test_numerics.py::test_time_mask PASSED mne/utils/tests/test_numerics.py::test_freq_mask PASSED mne/utils/tests/test_numerics.py::test_random_permutation PASSED mne/utils/tests/test_numerics.py::test_cov_scaling Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active PASSED mne/utils/tests/test_numerics.py::test_reg_pinv[2] PASSED mne/utils/tests/test_numerics.py::test_reg_pinv[3] PASSED mne/utils/tests/test_numerics.py::test_object_size PASSED mne/utils/tests/test_numerics.py::test_object_diff_with_nan PASSED mne/utils/tests/test_numerics.py::test_hash PASSED mne/utils/tests/test_numerics.py::test_pca[True-None] PASSED mne/utils/tests/test_numerics.py::test_pca[True-0.9999] PASSED mne/utils/tests/test_numerics.py::test_pca[True-8] PASSED mne/utils/tests/test_numerics.py::test_pca[True-mle] PASSED mne/utils/tests/test_numerics.py::test_pca[False-None] PASSED mne/utils/tests/test_numerics.py::test_pca[False-0.9999] PASSED mne/utils/tests/test_numerics.py::test_pca[False-8] PASSED mne/utils/tests/test_numerics.py::test_pca[False-mle] PASSED mne/utils/tests/test_numerics.py::test_array_equal_nan PASSED mne/utils/tests/test_numerics.py::test_julian_conversions PASSED mne/utils/tests/test_numerics.py::test_grand_average_empty_sequence PASSED mne/utils/tests/test_numerics.py::test_grand_average_len_1 Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Identifying common channels ... PASSED mne/utils/tests/test_numerics.py::test_reuse_cycle PASSED mne/utils/tests/test_numerics.py::test_arange_div[Numba-0.0001-0] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-0.0001-1] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-0.0001-10] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-0.0001-1000] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1-0] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1-1] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1-10] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1-1000] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-2.5-0] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-2.5-1] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-2.5-10] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-2.5-1000] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1000-0] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1000-1] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1000-10] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[Numba-1000-1000] SKIPPED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-0.0001-0] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-0.0001-1] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-0.0001-10] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-0.0001-1000] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1-0] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1-1] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1-10] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1-1000] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-2.5-0] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-2.5-1] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-2.5-10] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-2.5-1000] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1000-0] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1000-1] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1000-10] PASSED mne/utils/tests/test_numerics.py::test_arange_div[NumPy-1000-1000] PASSED mne/utils/tests/test_numerics.py::test_custom_lru_cache PASSED mne/utils/tests/test_numerics.py::test_replace_md5 PASSED mne/utils/tests/test_progressbar.py::test_progressbar 0%| | : 0/10 [00:00 16 Estimating covariance using EMPIRICAL Done. Number of samples used : 2804 [done] Computing rank from covariance with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 4 dim * 1.6e+02 max singular value) Estimated rank (eeg): 4 EEG: rank 4 computed from 4 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.2e-10 (2.2e-16 eps * 12 dim * 4.4e+04 max singular value) Estimated rank (mag + grad): 12 Found multiple SSS records. Using the first. MEG: rank 12 computed from 12 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.2e-10 (2.2e-16 eps * 12 dim * 4.4e+04 max singular value) Estimated rank (mag + grad): 12 Found multiple SSS records. Using the first. MEG: rank 12 computed from 12 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 4 dim * 1.6e+02 max singular value) Estimated rank (eeg): 4 EEG: rank 4 computed from 4 data channels with 0 projectors PASSED mne/viz/tests/test_evoked.py::test_plot_evoked Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Need more than one channel to make topography for grad. Disabling interactivity. Need more than one channel to make topography for mag. Disabling interactivity. Need more than one channel to make topography for grad. Disabling interactivity. Need more than one channel to make topography for mag. Disabling interactivity. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Loading data for 1 events and 121 original time points (prior to decimation) ... 0 bad epochs dropped PASSED mne/viz/tests/test_evoked.py::test_constrained_layout Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Need more than one channel to make topography for grad. Disabling interactivity. PASSED mne/viz/tests/test_evoked.py::test_plot_evoked_reconstruct[picks0-rlims0-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated No projector specified for this dataset. Please consider the method self.add_proj. Adding projection: planar--0.100-0.000-PCA-01 (exp var=70.8%) Adding projection: planar--0.100-0.000-PCA-02 (exp var=9.2%) Adding projection: axial--0.100-0.000-PCA-01 (exp var=91.6%) Adding projection: axial--0.100-0.000-PCA-02 (exp var=8.4%) No channels 'eeg' found. Skipping. 4 projection items deactivated Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Automatic origin fit: head of radius 91.0 mm Computing dot products for 10 MEG channels... Computing cross products for 10 → 10 MEG channels... Preparing the mapping matrix... Truncating at 6/10 components to omit less than 0.0001 (0) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active PASSED mne/viz/tests/test_evoked.py::test_plot_evoked_reconstruct[picks1-rlims1-True] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Projections have already been applied. Setting proj attribute to True. Automatic origin fit: head of radius 91.0 mm Computing dot products for 18 EEG channels... Computing cross products for 18 → 18 EEG channels... Preparing the mapping matrix... Truncating at 17/18 components and regularizing with α=1.0e-01 The map has an average electrode reference (18 channels) Projections have already been applied. Setting proj attribute to True. No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg--0.100-0.000-PCA-01 (exp var=63.1%) Adding projection: eeg--0.100-0.000-PCA-02 (exp var=22.2%) 2 projection items deactivated Created an SSP operator (subspace dimension = 3) 3 projection items activated SSP projectors applied... Automatic origin fit: head of radius 91.0 mm Computing dot products for 18 EEG channels... Computing cross products for 18 → 18 EEG channels... Preparing the mapping matrix... Truncating at 15/18 components and regularizing with α=1.0e-01 The map has an average electrode reference (18 channels) 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active PASSED mne/viz/tests/test_evoked.py::test_plot_evoked_reconstruct[picks2-rlims2-False] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated No projector specified for this dataset. Please consider the method self.add_proj. No channels 'grad' found. Skipping. No channels 'mag' found. Skipping. Adding projection: eeg--0.100-0.000-PCA-01 (exp var=56.1%) Adding projection: eeg--0.100-0.000-PCA-02 (exp var=28.8%) 2 projection items deactivated Created an SSP operator (subspace dimension = 2) 2 projection items activated SSP projectors applied... Automatic origin fit: head of radius 91.0 mm Computing dot products for 18 EEG channels... Computing cross products for 18 → 18 EEG channels... Preparing the mapping matrix... Truncating at 16/18 components and regularizing with α=1.0e-01 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active PASSED mne/viz/tests/test_evoked.py::test_plot_evoked_image Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated No projector specified for this dataset. Please consider the method self.add_proj. PASSED mne/viz/tests/test_evoked.py::test_plot_white 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 10, 'meg': 19} NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank={'eeg': 2, 'grad': 8, 'mag': 1, 'meg': 9} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 11 (3 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 1, 'meg': 10} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 13 (1 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 8, 'mag': 2, 'meg': 10} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 13 (1 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) Computing rank from covariance with rank={'eeg': 3, 'grad': 9, 'mag': 2, 'meg': 11} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 14 (0 small eigenvalues omitted) Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it. Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 5.4e-14 (2.2e-16 eps * 58 dim * 4.2 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 58 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 5.4e-14 (2.2e-16 eps * 58 dim * 4.2 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 58 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 5.4e-14 (2.2e-16 eps * 58 dim * 4.2 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 58 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 5.4e-14 (2.2e-16 eps * 58 dim * 4.2 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 58 data channels with 1 projector SSS has been applied to data. Showing mag and grad whitening jointly. Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank={'eeg': 57, 'meg': 64} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 121 (243 small eigenvalues omitted) Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank={'eeg': 57, 'meg': 64} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 121 (243 small eigenvalues omitted) NOTE: pick_types() is a legacy function. New code should use inst.pick(...). Computing rank from covariance with rank=None Using tolerance 5.4e-14 (2.2e-16 eps * 58 dim * 4.2 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 58 data channels with 1 projector Computing rank from covariance with rank=None Using tolerance 5.4e-14 (2.2e-16 eps * 58 dim * 4.2 max singular value) Estimated rank (eeg): 57 EEG: rank 57 computed from 58 data channels with 1 projector SSS has been applied to data. Showing mag and grad whitening jointly. Created an SSP operator (subspace dimension = 1) Computing rank from covariance with rank={'eeg': 57, 'meg': 302} Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 359 (5 small eigenvalues omitted) PASSED mne/viz/tests/test_evoked.py::test_plot_compare_evokeds_neuromag122 Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) combining channels using RMS (grad channels) combining channels using RMS (grad channels) PASSED mne/viz/tests/test_evoked.py::test_plot_ctf SKIPPED (Requires testin...) mne/viz/tests/test_figure.py::test_browse_figure_constructor PASSED mne/viz/tests/test_ica.py::test_plot_ica_components Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Fitting ICA to data using 9 channels (please be patient, this may take a while) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 1e-15 (2.2e-16 eps * 9 dim * 0.51 max singular value) Estimated rank (mag + grad): 6 MEG: rank 6 computed from 9 data channels with 3 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 6 (3 small eigenvalues omitted) Selecting by number: 8 components Fitting ICA took 0.7s. Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 11 matching events found No baseline correction applied 0 projection items activated PASSED mne/viz/tests/test_ica.py::test_plot_ica_properties Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 0 projection items deactivated Not setting metadata 3 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 181 original time points ... 1 bad epochs dropped 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Fitting ICA to data using 6 channels (please be patient, this may take a while) Created an SSP operator (subspace dimension = 2) Computing rank from covariance with rank=None Using tolerance 6e-16 (2.2e-16 eps * 6 dim * 0.45 max singular value) Estimated rank (mag + grad): 4 MEG: rank 4 computed from 6 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 4 (2 small eigenvalues omitted) Selecting by number: 2 components Fitting ICA took 0.1s. Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Fitting ICA to data using 6 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 6 components Fitting ICA took 0.0s. Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Fitting ICA to data using 6 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 6 components Fitting ICA took 0.0s. Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Fitting ICA to data using 10 channels (please be patient, this may take a while) Omitting 600 of 4806 (12.48%) samples, retaining 4206 (87.52%) samples. Selecting by non-zero PCA components: 10 components Fitting ICA took 0.1s. Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated Not setting metadata 4 matching events found No baseline correction applied 0 projection items activated PASSED mne/viz/tests/test_ica.py::test_plot_ica_sources[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 2 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Fitting ICA to data using 9 channels (please be patient, this may take a while) Selecting by number: 2 components Fitting ICA took 0.0s. Creating RawArray with float64 data, n_channels=2, n_times=602 Range : 25800 ... 26401 = 42.956 ... 43.957 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=602 Range : 25800 ... 26401 = 42.956 ... 43.957 secs Ready. Creating RawArray with float64 data, n_channels=3, n_times=3004 Range : 25800 ... 28803 = 42.956 ... 47.956 secs Ready. Using multitaper spectrum estimation with 7 DPSS windows Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated Creating RawArray with float64 data, n_channels=2, n_times=602 Range : 25800 ... 26401 = 42.956 ... 43.957 secs Ready. Creating RawArray with float64 data, n_channels=2, n_times=602 Range : 25800 ... 26401 = 42.956 ... 43.957 secs Ready. Loading data for 2 events and 181 original time points ... 0 bad epochs dropped Loading data for 2 events and 181 original time points ... 0 bad epochs dropped Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated PASSED mne/viz/tests/test_ica.py::test_plot_ica_sources[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt API...) mne/viz/tests/test_ica.py::test_plot_ica_overlay Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Fitting ICA to data using 9 channels (please be patient, this may take a while) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 1e-15 (2.2e-16 eps * 9 dim * 0.51 max singular value) Estimated rank (mag + grad): 6 MEG: rank 6 computed from 9 data channels with 3 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 6 (3 small eigenvalues omitted) Selecting by number: 2 components Fitting ICA took 0.2s. Reconstructing ECG signal from Magnetometers Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 23 (average pulse 57.558936564127606 / min.) Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 23 events and 601 original time points ... 0 bad epochs dropped Applying ICA to Evoked instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Using EOG channel: EOG 061 EOG channel index for this subject is: [375] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Not setting metadata 4 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) Using data from preloaded Raw for 4 events and 601 original time points ... 0 bad epochs dropped Applying ICA to Evoked instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 2 PCA components Applying ICA to Raw instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 9 PCA components Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_raw.fif... Read 5 compensation matrices Range : 24000 ... 31200 = 10.000 ... 13.000 secs Ready. Current compensation grade : 0 Compensator constructed to change 0 -> 3 Fitting ICA to data using 275 channels (please be patient, this may take a while) Removing 5 compensators from info because not all compensation channels were picked. Selecting by number: 2 components Fitting ICA took 9.5s. Using channel ECG 063 to identify heart beats. Setting up band-pass filter from 8 - 16 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 8.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz) - Upper passband edge: 16.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz) - Filter length: 24000 samples (10.000 s) Number of ECG events detected : 3 (average pulse 59.991667823913346 / min.) Not setting metadata 3 matching events found No baseline correction applied Loading data for 3 events and 2401 original time points ... 1 bad epochs dropped Applying ICA to Evoked instance Transforming to ICA space (2 components) Zeroing out 0 ICA components Projecting back using 275 PCA components PASSED mne/viz/tests/test_ica.py::test_plot_ica_scores Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Fitting ICA to data using 9 channels (please be patient, this may take a while) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 1e-15 (2.2e-16 eps * 9 dim * 0.51 max singular value) Estimated rank (mag + grad): 6 MEG: rank 6 computed from 9 data channels with 3 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 6 (3 small eigenvalues omitted) Selecting by number: 2 components Fitting ICA took 0.5s. PASSED mne/viz/tests/test_ica.py::test_plot_instance_components[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Fitting ICA to data using 9 channels (please be patient, this may take a while) Created an SSP operator (subspace dimension = 3) Computing rank from covariance with rank=None Using tolerance 1e-15 (2.2e-16 eps * 9 dim * 0.51 max singular value) Estimated rank (mag + grad): 6 MEG: rank 6 computed from 9 data channels with 3 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 6 (3 small eigenvalues omitted) Selecting by number: 2 components Fitting ICA took 0.5s. Creating RawArray with float64 data, n_channels=3, n_times=14400 Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 2 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 2 events and 181 original time points ... 0 bad epochs dropped Not setting metadata 2 matching events found No baseline correction applied 0 projection items activated PASSED mne/viz/tests/test_ica.py::test_plot_instance_components[qt] SKIPPED mne/viz/tests/test_misc.py::test_plot_filter Setting up band-pass filter from 2 - 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Lower passband edge: 2.00 - Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz) - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 3301 samples (3.301 s) Setting up band-pass filter from 2 - 40 Hz IIR filter parameters --------------------- Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 16 (effective, after forward-backward) - Cutoffs at 2.00, 40.00 Hz: -6.02, -6.02 dB Setting up band-pass filter from 2 - 40 Hz IIR filter parameters --------------------- Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 16 (effective, after forward-backward) - Cutoffs at 2.00, 40.00 Hz: -6.02, -6.02 dB Setting up band-pass filter from 2 - 40 Hz IIR filter parameters --------------------- Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 16 (effective, after forward-backward) - Cutoffs at 2.00, 40.00 Hz: -6.02, -6.02 dB Setting up band-pass filter from 2 - 40 Hz IIR filter parameters --------------------- Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 16 (effective, after forward-backward) - Cutoffs at 2.00, 40.00 Hz: -6.02, -6.02 dB PASSED mne/viz/tests/test_misc.py::test_plot_cov Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Created an SSP operator (subspace dimension = 2) Computing rank from covariance with rank=None Using tolerance 4e-46 (2.2e-16 eps * 2 dim * 8.9e-31 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 4.2e-16 (2.2e-16 eps * 4 dim * 0.47 max singular value) Estimated rank (grad): 4 GRAD: rank 4 computed from 4 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 5.2e-14 (2.2e-16 eps * 102 dim * 2.3 max singular value) Estimated rank (mag): 102 MAG: rank 102 computed from 102 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.7e-11 (2.2e-16 eps * 204 dim * 3.8e+02 max singular value) Estimated rank (grad): 204 GRAD: rank 204 computed from 204 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 8.6e-14 (2.2e-16 eps * 60 dim * 6.5 max singular value) Estimated rank (eeg): 60 EEG: rank 60 computed from 60 data channels with 0 projectors PASSED mne/viz/tests/test_misc.py::test_plot_bem SKIPPED (Requires testing ...) mne/viz/tests/test_misc.py::test_event_colors PASSED mne/viz/tests/test_misc.py::test_plot_events Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_misc.py::test_plot_source_spectrogram SKIPPED (Re...) mne/viz/tests/test_misc.py::test_plot_snr SKIPPED (Requires testing ...) mne/viz/tests/test_misc.py::test_plot_dipole_amplitudes SKIPPED (Req...) mne/viz/tests/test_misc.py::test_plot_csd PASSED mne/viz/tests/test_misc.py::test_plot_chpi_snr SKIPPED (Requires tes...) mne/viz/tests/test_montage.py::test_plot_montage PASSED mne/viz/tests/test_montage.py::test_plot_defect_montage[standard_1005-342] 4 duplicate electrode labels found: T7/T3, T8/T4, P7/T5, P8/T6 Plotting 339 unique labels. PASSED mne/viz/tests/test_montage.py::test_plot_defect_montage[standard_postfixed-85] 18 duplicate electrode labels found: F7p/T3a, F5p/C5a, F3p/C3a, F1p/C1a, Fzp/Cza, F2p/C2a, F4p/C4a, F6p/C6a, F8p/T4a, T3p/T5a, C5p/P5a, C3p/P3a, C1p/P1a, Czp/Pza, C2p/P2a, C4p/P4a, C6p/P6a, T4p/T6a Plotting 82 unique labels. PASSED mne/viz/tests/test_montage.py::test_plot_defect_montage[standard_primed-85] 18 duplicate electrode labels found: F7''/T3', F5''/C5', F3''/C3', F1''/C1', Fz''/Cz', F2''/C2', F4''/C4', F6''/C6', F8''/T4', T3''/T5', C5''/P5', C3''/P3', C1''/P1', Cz''/Pz', C2''/P2', C4''/P4', C6''/P6', T4''/T6' Plotting 82 unique labels. PASSED mne/viz/tests/test_montage.py::test_plot_defect_montage[standard_1020-93] 4 duplicate electrode labels found: T7/T3, T8/T4, P7/T5, P8/T6 Plotting 90 unique labels. PASSED mne/viz/tests/test_montage.py::test_plot_digmontage PASSED mne/viz/tests/test_proj.py::test_plot_projs_joint SKIPPED (Requires ...) mne/viz/tests/test_raw.py::test_scale_bar[matplotlib] Creating RawArray with float64 data, n_channels=3, n_times=10000 Range : 0 ... 9999 = 0.000 ... 9.999 secs Ready. PASSED mne/viz/tests/test_raw.py::test_scale_bar[qt] SKIPPED (Qt API None h...) mne/viz/tests/test_raw.py::test_plot_raw_selection[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Clicking button: 'Left-temporal' PASSED mne/viz/tests/test_raw.py::test_plot_raw_selection[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt A...) mne/viz/tests/test_raw.py::test_plot_raw_ssp_interaction[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... 2 projection items deactivated PASSED mne/viz/tests/test_raw.py::test_plot_raw_ssp_interaction[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_plot_raw_child_figures[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_child_figures[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (...) mne/viz/tests/test_raw.py::test_orphaned_annot_fig[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Channels marked as bad: none PASSED mne/viz/tests/test_raw.py::test_orphaned_annot_fig[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt A...) mne/viz/tests/test_raw.py::test_plot_raw_keypresses[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_keypresses[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt ...) mne/viz/tests/test_raw.py::test_plot_raw_traces[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_traces[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt API ...) mne/viz/tests/test_raw.py::test_plot_raw_picks[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_picks[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt API N...) mne/viz/tests/test_raw.py::test_plot_raw_groupby[matplotlib-position] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_groupby[matplotlib-selection] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_groupby[qt-position] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_plot_raw_groupby[qt-selection] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_plot_raw_meas_date[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_meas_date[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt A...) mne/viz/tests/test_raw.py::test_plot_raw_nan[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_raw_nan[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt API Non...) mne/viz/tests/test_raw.py::test_plot_raw_white[matplotlib] SKIPPED (...) mne/viz/tests/test_raw.py::test_plot_raw_white[qt] SKIPPED (Requires...) mne/viz/tests/test_raw.py::test_plot_ref_meg[matplotlib] SKIPPED (Re...) mne/viz/tests/test_raw.py::test_plot_ref_meg[qt] SKIPPED (Requires t...) mne/viz/tests/test_raw.py::test_plot_misc_auto[matplotlib] Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Creating RawArray with float64 data, n_channels=1, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/viz/tests/test_raw.py::test_plot_misc_auto[qt] SKIPPED (Qt API N...) mne/viz/tests/test_raw.py::test_plot_annotations[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_plot_annotations[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt API...) mne/viz/tests/test_raw.py::test_overlapping_annotation_deletion[matplotlib-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_overlapping_annotation_deletion[matplotlib-1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_overlapping_annotation_deletion[matplotlib-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_overlapping_annotation_deletion[qt-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_overlapping_annotation_deletion[qt-1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_overlapping_annotation_deletion[qt-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_remove_annotations[matplotlib-hide_which0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_remove_annotations[matplotlib-hide_which1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_remove_annotations[matplotlib-hide_which2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_remove_annotations[matplotlib-hide_which3] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_remove_annotations[qt-hide_which0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_remove_annotations[qt-hide_which1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_remove_annotations[qt-hide_which2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_remove_annotations[qt-hide_which3] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_merge_annotations[matplotlib] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_merge_annotations[qt] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt AP...) mne/viz/tests/test_raw.py::test_plot_raw_filtered[matplotlib-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting up low-pass filter at 3e+02 Hz Setting up low-pass filter at 1 Hz IIR filter parameters --------------------- Butterworth lowpass zero-phase (two-pass forward and reverse) non-causal filter: Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 199 samples (0.331 s) Setting up high-pass filter at 1 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal highpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Lower passband edge: 1.00 - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz) - Filter length: 1983 samples (3.302 s) Setting up low-pass filter at 40 Hz FIR filter parameters --------------------- Designing a one-pass, zero-phase, non-causal lowpass filter: - Windowed time-domain design (firwin) method - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation - Upper passband edge: 40.00 Hz - Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz) - Filter length: 199 samples (0.331 s) Creating RawArray with float64 data, n_channels=1, n_times=100 Range : 0 ... 99 = 0.000 ... 4.950 secs Ready. Setting up low-pass filter at 5 Hz IIR filter parameters --------------------- Butterworth lowpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 8 (effective, after forward-backward) - Cutoff at 5.00 Hz: -6.02 dB PASSED mne/viz/tests/test_raw.py::test_plot_raw_filtered[matplotlib-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Setting up low-pass filter at 3e+02 Hz Setting up low-pass filter at 1 Hz IIR filter parameters --------------------- Butterworth lowpass zero-phase (two-pass forward and reverse) non-causal filter: Setting up low-pass filter at 40 Hz IIR filter parameters --------------------- Butterworth lowpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 4 (effective, after forward-backward) - Cutoff at 40.00 Hz: -6.02 dB Setting up high-pass filter at 1 Hz IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 4 (effective, after forward-backward) - Cutoff at 1.00 Hz: -6.02 dB Setting up low-pass filter at 40 Hz IIR filter parameters --------------------- Butterworth lowpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 4 (effective, after forward-backward) - Cutoff at 40.00 Hz: -6.02 dB Creating RawArray with float64 data, n_channels=1, n_times=100 Range : 0 ... 99 = 0.000 ... 4.950 secs Ready. Setting up low-pass filter at 5 Hz IIR filter parameters --------------------- Butterworth lowpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 8 (effective, after forward-backward) - Cutoff at 5.00 Hz: -6.02 dB PASSED mne/viz/tests/test_raw.py::test_plot_raw_filtered[qt-0] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt ...) mne/viz/tests/test_raw.py::test_plot_raw_filtered[qt-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt ...) mne/viz/tests/test_raw.py::test_plot_raw_psd Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Effective window size : 3.410 (s) Plotting power spectral density (dB=True). Plotting power spectral density (dB=True). Plotting power spectral density (dB=True). Effective window size : 3.410 (s) Plotting amplitude spectral density (dB=False). Plotting power spectral density (dB=True). Plotting power spectral density (dB=True). Plotting power spectral density (dB=True). Effective window size : 3.410 (s) Plotting power spectral density (dB=True). Effective window size : 3.410 (s) Plotting amplitude spectral density (dB=True). Plotting power spectral density (dB=True). Plotting amplitude spectral density (dB=False). Plotting power spectral density (dB=False). Effective window size : 3.410 (s) Effective window size : 1.002 (s) Plotting power spectral density (dB=True). Plotting power spectral density (dB=True). Effective window size : 1.002 (s) Plotting power spectral density (dB=True). Need more than one channel to make topography for hbo. Disabling interactivity. Need more than one channel to make topography for hbr. Disabling interactivity. Need more than one channel to make topography for fnirs_cw_amplitude. Disabling interactivity. Need more than one channel to make topography for fnirs_od. Disabling interactivity. Creating RawArray with float64 data, n_channels=2, n_times=100 Range : 0 ... 99 = 0.000 ... 0.990 secs Ready. Effective window size : 1.000 (s) Plotting power spectral density (dB=True). PASSED mne/viz/tests/test_raw.py::test_plot_sensors Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Creating RawArray with float64 data, n_channels=4, n_times=100 Range : 0 ... 99 = 0.000 ... 0.990 secs Ready. Approximating Fpz location by mirroring Oz along the X and Y axes. PASSED mne/viz/tests/test_raw.py::test_min_window_size[matplotlib-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_min_window_size[matplotlib-0.1,0.1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_min_window_size[qt-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt...) mne/viz/tests/test_raw.py::test_min_window_size[qt-0.1,0.1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_scalings_int[matplotlib] Creating RawArray with float64 data, n_channels=1, n_times=500 Range : 0 ... 499 = 0.000 ... 0.499 secs Ready. PASSED mne/viz/tests/test_raw.py::test_scalings_int[qt] SKIPPED (Qt API Non...) mne/viz/tests/test_raw.py::test_clock_xticks[matplotlib-20-1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_clock_xticks[matplotlib-1.8-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_clock_xticks[matplotlib-0.01-4] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... PASSED mne/viz/tests/test_raw.py::test_clock_xticks[qt-20-1] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt AP...) mne/viz/tests/test_raw.py::test_clock_xticks[qt-1.8-2] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt A...) mne/viz/tests/test_raw.py::test_clock_xticks[qt-0.01-4] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt ...) mne/viz/tests/test_raw.py::test_plotting_order_consistency PASSED mne/viz/tests/test_raw.py::test_plotting_temperature_gsr[matplotlib] Creating RawArray with float64 data, n_channels=2, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/viz/tests/test_raw.py::test_plotting_temperature_gsr[qt] SKIPPED mne/viz/tests/test_raw.py::test_plotting_memory_garbage_collection Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED mne/viz/tests/test_raw.py::test_plotting_scalebars[matplotlib] Detected locale "C" with character encoding "ANSI_X3.4-1968", which is not UTF-8. Qt depends on a UTF-8 locale, and has switched to "C.UTF-8" instead. If this causes problems, reconfigure your locale. See the locale(1) manual for more information. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 601 = 0.000 ... 1.001 secs... PASSED mne/viz/tests/test_raw.py::test_plotting_scalebars[qt] SKIPPED (Qt A...) mne/viz/tests/test_scraper.py::test_qt_scraper Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... SKIPPED (Qt API None ...) mne/viz/tests/test_topo.py::test_plot_joint Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated No projector specified for this dataset. Please consider the method self.add_proj. No projector specified for this dataset. Please consider the method self.add_proj. No projector specified for this dataset. Please consider the method self.add_proj. Adding projection: meg--0.200-0.200-PCA-01 (exp var=57.5%) No channels 'eeg' found. Skipping. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Projections have already been applied. Setting proj attribute to True. Automatic origin fit: head of radius 91.0 mm Computing dot products for 6 MEG channels... Computing cross products for 6 → 6 MEG channels... Preparing the mapping matrix... Truncating at 5/6 components to omit less than 0.0001 (0) No projector specified for this dataset. Please consider the method self.add_proj. Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated No projector specified for this dataset. Please consider the method self.add_proj. PASSED mne/viz/tests/test_topo.py::test_plot_topo Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Entering delayed SSP mode. Created an SSP operator (subspace dimension = 2) 3 projection items deactivated Created an SSP operator (subspace dimension = 1) 3 projection items activated SSP projectors applied... No projector specified for this dataset. Please consider the method self.add_proj. 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Computing rank from covariance with rank=None Using tolerance 6.5e-16 (2.2e-16 eps * 6 dim * 0.49 max singular value) Estimated rank (mag + grad): 6 MEG: rank 6 computed from 6 data channels with 0 projectors Setting small MEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 6 (0 small eigenvalues omitted) PASSED mne/viz/tests/test_topo.py::test_plot_topo_nirs PASSED mne/viz/tests/test_topo.py::test_plot_topo_single_ch Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated PASSED mne/viz/tests/test_topo.py::test_plot_topo_image_epochs Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 2 events and 241 original time points ... 0 bad epochs dropped PASSED mne/viz/tests/test_topo.py::test_plot_tfr_topo Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Applying baseline correction (mode: ratio) Applying baseline correction (mode: ratio) Applying baseline correction (mode: ratio) No baseline correction applied Applying baseline correction (mode: mean) No baseline correction applied PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_interactive[None] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) No channels 'grad' found. Skipping. Adding projection: axial--0.200-0.499-PCA-01 (exp var=51.3%) No channels 'eeg' found. Skipping. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... 0 projection items deactivated No projector specified for this dataset. Please consider the method self.add_proj. PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_interactive[constrained] Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) No channels 'grad' found. Skipping. Adding projection: axial--0.200-0.499-PCA-01 (exp var=51.3%) No channels 'eeg' found. Skipping. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... 0 projection items deactivated No projector specified for this dataset. Please consider the method self.add_proj. PASSED mne/viz/tests/test_topomap.py::test_plot_projs_topomap SKIPPED (Requ...) mne/viz/tests/test_topomap.py::test_plot_projs_topomap_joint[combined-joint] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Dropped 0/23 epochs Adding projection: meg-Raw-0.000-23.975-PCA-01 (exp var=42.3%) Adding projection: meg-Raw-0.000-23.975-PCA-02 (exp var=25.4%) No channels 'eeg' found. Skipping. PASSED mne/viz/tests/test_topomap.py::test_plot_projs_topomap_joint[combined-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Dropped 0/23 epochs Adding projection: meg-Raw-0.000-23.975-PCA-01 (exp var=42.3%) Adding projection: meg-Raw-0.000-23.975-PCA-02 (exp var=25.4%) No channels 'eeg' found. Skipping. PASSED mne/viz/tests/test_topomap.py::test_plot_projs_topomap_joint[separate-joint] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Dropped 0/23 epochs Adding projection: planar-Raw-0.000-23.975-PCA-01 (exp var=42.4%) Adding projection: planar-Raw-0.000-23.975-PCA-02 (exp var=25.4%) Adding projection: axial-Raw-0.000-23.975-PCA-01 (exp var=93.2%) Adding projection: axial-Raw-0.000-23.975-PCA-02 (exp var=4.5%) No channels 'eeg' found. Skipping. PASSED mne/viz/tests/test_topomap.py::test_plot_projs_topomap_joint[separate-None] Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Not setting metadata 23 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s Dropped 0/23 epochs Adding projection: planar-Raw-0.000-23.975-PCA-01 (exp var=42.4%) Adding projection: planar-Raw-0.000-23.975-PCA-02 (exp var=25.4%) Adding projection: axial-Raw-0.000-23.975-PCA-01 (exp var=93.2%) Adding projection: axial-Raw-0.000-23.975-PCA-02 (exp var=4.5%) No channels 'eeg' found. Skipping. PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_animation PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_animation_csd PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_animation_nirs PASSED mne/viz/tests/test_topomap.py::test_plot_evoked_topomap_border PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_basic Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... Applying baseline correction (mode: mean) Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Applying baseline correction (mode: mean) 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... PASSED mne/viz/tests/test_topomap.py::test_plot_tfr_topomap Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. No baseline correction applied No baseline correction applied Averaging TFR over channels ['MEG 0923', 'MEG 0922'] Averaging TFR over channels ['MEG 0111'] converting legacy list-of-tuples input to a dict for the `bands` parameter PASSED mne/viz/tests/test_topomap.py::test_ctf_plotting Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_ctf_comp_raw.fif... Read 5 compensation matrices Range : 0 ... 240 = 0.000 ... 0.500 secs Ready. Current compensation grade : 3 Reading 0 ... 240 = 0.000 ... 0.500 secs... Not setting metadata 50 matching events found No baseline correction applied 0 projection items activated PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_neuromag122 SKIPPED mne/viz/tests/test_topomap.py::test_plot_topomap_bads Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_channel_distance PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_bads_grad Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_nirs_overlap[testing_data] SKIPPED mne/viz/tests/test_topomap.py::test_plot_topomap_nirs_ica[testing_data] SKIPPED mne/viz/tests/test_topomap.py::test_plot_cov_topomap 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 306 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 362 (4 small eigenvalues omitted) Created an SSP operator (subspace dimension = 4) Computing rank from covariance with rank=None Using tolerance 1.8e-11 (2.2e-16 eps * 306 dim * 2.7e+02 max singular value) Estimated rank (mag + grad): 303 MEG: rank 303 computed from 306 data channels with 3 projectors Using tolerance 5.9e-14 (2.2e-16 eps * 60 dim * 4.4 max singular value) Estimated rank (eeg): 59 EEG: rank 59 computed from 60 data channels with 1 projector Setting small MEG eigenvalues to zero (without PCA) Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 362 (4 small eigenvalues omitted) PASSED mne/viz/tests/test_topomap.py::test_plot_topomap_cnorm PASSED mne/viz/tests/test_topomap.py::test_plot_bridged_electrodes Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... Local minimum 1.8764818581666296e-11 found Bridge detected between EEG 001 and EEG 002 PASSED mne/viz/tests/test_topomap.py::test_plot_ch_adjacency Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations. -- number of adjacent vertices : 5 PASSED mne/viz/tests/test_ui_events.py::test_get_event_channel SKIPPED (Req...) mne/viz/tests/test_ui_events.py::test_publish PASSED mne/viz/tests/test_ui_events.py::test_subscribe PASSED mne/viz/tests/test_ui_events.py::test_unsubscribe PASSED mne/viz/tests/test_ui_events.py::test_link PASSED mne/viz/tests/test_ui_events.py::test_unlink PASSED mne/viz/tests/test_ui_events.py::test_disable_ui_events PASSED mne/viz/tests/test_utils.py::test_setup_vmin_vmax_warns PASSED mne/viz/tests/test_utils.py::test_get_color_list PASSED mne/viz/tests/test_utils.py::test_mne_analyze_colormap PASSED mne/viz/tests/test_utils.py::test_compare_fiff PASSED mne/viz/tests/test_utils.py::test_clickable_image PASSED mne/viz/tests/test_utils.py::test_add_background_image PASSED mne/viz/tests/test_utils.py::test_auto_scale Opening raw data file /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 63 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 63 events and 421 original time points ... 2 bad epochs dropped Loading data for 61 events and 421 original time points ... Loading data for 1 events and 421 original time points ... PASSED mne/viz/tests/test_utils.py::test_validate_if_list_of_axes PASSED mne/viz/tests/test_utils.py::test_centers_to_edges PASSED mne/viz/tests/test_utils.py::test_event_color_dict PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-3-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-3-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-3-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-3-4-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-5-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-5-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-5-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-2-5-4-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-3-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-3-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-3-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-3-4-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-5-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-5-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-5-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[3-4-5-4-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-3-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-3-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-3-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-3-4-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-5-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-5-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-5-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-2-5-4-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-3-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-3-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-3-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-3-4-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-5-2-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-5-2-1] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-5-4-0] PASSED mne/viz/tests/test_utils.py::test_concatenate_images[5-4-5-4-1] PASSED mne/viz/tests/test_utils.py::test_draggable_colorbar Reading /build/reproducible-path/python-mne-1.8.0/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right visual) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied PASSED mne/viz/utils.py::mne.viz.utils.centers_to_edges PASSED =================================== FAILURES =================================== _______________________________ test_peak_finder _______________________________ mne/preprocessing/tests/test_peak_finder.py:18: in test_peak_finder assert_equal(peak_inds.dtype, np.dtype("int64")) E AssertionError:  E Items are not equal: E ACTUAL: dtype('int32') E DESIRED: dtype('int64') _________ test_split_saving_and_loading_back[epochs_to_split2-preload] _________ mne/tests/test_epochs.py:1651: in test_split_saving_and_loading_back _assert_splits(fname, n_files, got_size) E AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_4/test-epo-5.fif E assert False E + where False = is_file() E + where is_file = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_4/test-epo-5.fif').is_file _______ test_split_saving_and_loading_back[epochs_to_split2-no_preload] ________ mne/tests/test_epochs.py:1651: in test_split_saving_and_loading_back _assert_splits(fname, n_files, got_size) E AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_5/test-epo-5.fif E assert False E + where False = is_file() E + where is_file = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_5/test-epo-5.fif').is_file _________ test_split_saving_and_loading_back[epochs_to_split3-preload] _________ mne/tests/test_epochs.py:1651: in test_split_saving_and_loading_back _assert_splits(fname, n_files, got_size) E AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_6/test-epo-5.fif E assert False E + where False = is_file() E + where is_file = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_6/test-epo-5.fif').is_file _______ test_split_saving_and_loading_back[epochs_to_split3-no_preload] ________ mne/tests/test_epochs.py:1651: in test_split_saving_and_loading_back _assert_splits(fname, n_files, got_size) E AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_7/test-epo-5.fif E assert False E + where False = is_file() E + where is_file = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_saving_and_loading_7/test-epo-5.fif').is_file _________________ test_split_naming[epochs_to_split2-neuromag] _________________ mne/tests/test_epochs.py:1696: in test_split_naming assert len(list(dst_fpath.parent.iterdir())) == n_files E AssertionError: assert 5 == 6 E + where 5 = len([PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-3.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-2.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-4.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-1.fif')]) E + where [PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-3.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-2.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-4.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo-1.fif')] = list() E + where = iterdir() E + where iterdir = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6').iterdir E + where PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6') = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp6/test_epo.fif').parent ___________________ test_split_naming[epochs_to_split2-bids] ___________________ mne/tests/test_epochs.py:1696: in test_split_naming assert len(list(dst_fpath.parent.iterdir())) == n_files E AssertionError: assert 5 == 6 E + where 5 = len([PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-05_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-03_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-04_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-01_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-02_epo.fif')]) E + where [PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-05_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-03_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-04_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-01_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_split-02_epo.fif')] = list() E + where = iterdir() E + where iterdir = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg').iterdir E + where PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg') = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp7/sub-01/meg/sub-01_epo.fif').parent ___________________ test_split_naming[epochs_to_split2-mix] ____________________ mne/tests/test_epochs.py:1696: in test_split_naming assert len(list(dst_fpath.parent.iterdir())) == n_files E AssertionError: assert 5 == 6 E + where 5 = len([PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-05_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-04_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-02_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-03_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-01_b-epo.fif')]) E + where [PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-05_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-04_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-02_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-03_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_split-01_b-epo.fif')] = list() E + where = iterdir() E + where iterdir = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8').iterdir E + where PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8') = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp8/a_b-epo.fif').parent _________________ test_split_naming[epochs_to_split3-neuromag] _________________ mne/tests/test_epochs.py:1696: in test_split_naming assert len(list(dst_fpath.parent.iterdir())) == n_files E AssertionError: assert 5 == 6 E + where 5 = len([PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-3.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-2.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-4.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-1.fif')]) E + where [PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-3.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-2.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-4.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo-1.fif')] = list() E + where = iterdir() E + where iterdir = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9').iterdir E + where PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9') = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp9/test_epo.fif').parent ___________________ test_split_naming[epochs_to_split3-bids] ___________________ mne/tests/test_epochs.py:1696: in test_split_naming assert len(list(dst_fpath.parent.iterdir())) == n_files E AssertionError: assert 5 == 6 E + where 5 = len([PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-05_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-03_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-04_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-01_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-02_epo.fif')]) E + where [PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-05_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-03_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-04_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-01_epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_split-02_epo.fif')] = list() E + where = iterdir() E + where iterdir = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg').iterdir E + where PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg') = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp10/sub-01/meg/sub-01_epo.fif').parent ___________________ test_split_naming[epochs_to_split3-mix] ____________________ mne/tests/test_epochs.py:1696: in test_split_naming assert len(list(dst_fpath.parent.iterdir())) == n_files E AssertionError: assert 5 == 6 E + where 5 = len([PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-05_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-04_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-02_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-03_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-01_b-epo.fif')]) E + where [PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-05_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-04_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-02_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-03_b-epo.fif'), PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_split-01_b-epo.fif')] = list() E + where = iterdir() E + where iterdir = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11').iterdir E + where PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11') = PosixPath('/tmp/pytest-of-pbuilder1/pytest-0/test_split_naming_epochs_to_sp11/a_b-epo.fif').parent - generated xml file: /build/reproducible-path/python-mne-1.8.0/junit-results.xml - =========================== slowest 20 test modules ============================ 505.44s total mne/tests/test_epochs.py 461.79s total mne/tests/test_rank.py 333.81s total mne/tests/test_cov.py 190.72s total mne/viz/tests/test_raw.py 168.47s total mne/_fiff/tests/ 165.07s total mne/time_frequency/tests/ 143.14s total mne/decoding/tests/ 136.13s total mne/preprocessing/tests/test_ica.py 132.45s total mne/channels/tests/ 131.63s total mne/viz/tests/test_evoked.py 119.07s total mne/viz/tests/test_ica.py 104.28s total mne/viz/tests/test_epochs.py 96.32s total mne/inverse_sparse/tests/ 87.61s total mne/preprocessing/tests/test_maxwell.py 86.90s total mne/viz/tests/test_topomap.py 81.13s total mne/tests/test_proj.py 75.67s total mne/stats/tests/ 73.78s total mne/io/fiff/ 67.10s total mne/viz/tests/test_utils.py 54.63s total mne/preprocessing/tests/test_annotate_nan.py ============================= slowest 20 durations ============================= 93.18s call mne/_fiff/tests/test_meas_info.py::test_anonymize 84.49s call mne/tests/test_cov.py::test_cov_estimation_on_raw[shrunk] 69.00s call mne/viz/tests/test_evoked.py::test_plot_white 49.20s call mne/tests/test_cov.py::test_cov_estimation_with_triggers[None] 48.62s call mne/viz/tests/test_topomap.py::test_plot_topomap_basic 48.41s call mne/tests/test_cov.py::test_cov_estimation_with_triggers[full] 48.14s call mne/viz/tests/test_ica.py::test_plot_ica_overlay 45.08s call mne/preprocessing/tests/test_maxwell.py::test_find_bads_maxwell_flat 42.56s call mne/tests/test_epochs.py::test_epoch_eq 41.88s call mne/viz/tests/test_utils.py::test_draggable_colorbar 38.66s call mne/channels/tests/test_interpolation.py::test_interpolate_meg_ctf 35.63s call mne/tests/test_epochs.py::test_epochs_io_preload[True] 35.44s call mne/tests/test_epochs.py::test_epochs_io_preload[False] 34.29s call mne/tests/test_rank.py::test_cov_rank_estimation[None-True-combined] 33.30s call mne/tests/test_rank.py::test_cov_rank_estimation[None-False-separate] 32.91s call mne/tests/test_rank.py::test_cov_rank_estimation[None-True-separate] 32.25s call mne/tests/test_rank.py::test_cov_rank_estimation[info-True-separate] 32.07s call mne/tests/test_rank.py::test_cov_rank_estimation[info-True-combined] 31.52s call mne/tests/test_rank.py::test_cov_rank_estimation[info-False-separate] 31.36s call mne/tests/test_rank.py::test_cov_rank_estimation[None-False-combined] =========================== short test summary info ============================ FAILED mne/preprocessing/tests/test_peak_finder.py::test_peak_finder - AssertionError: FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split2-preload] - AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_... FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split2-no_preload] - AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_... FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split3-preload] - AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_... FAILED mne/tests/test_epochs.py::test_split_saving_and_loading_back[epochs_to_split3-no_preload] - AssertionError: Missing file: /tmp/pytest-of-pbuilder1/pytest-0/test_split_... FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split2-neuromag] - AssertionError: assert 5 == 6 FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split2-bids] - AssertionError: assert 5 == 6 FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split2-mix] - AssertionError: assert 5 == 6 FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split3-neuromag] - AssertionError: assert 5 == 6 FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split3-bids] - AssertionError: assert 5 == 6 FAILED mne/tests/test_epochs.py::test_split_naming[epochs_to_split3-mix] - AssertionError: assert 5 == 6 SKIPPED [1] ../../../usr/lib/python3/dist-packages/_pytest/doctest.py:569: unable to import module PosixPath('/build/reproducible-path/python-mne-1.8.0/mne/gui/_coreg.py') SKIPPED [3] mne/io/eyelink/tests/test_eyelink.py:19: could not import 'pandas': No module named 'pandas' SKIPPED [2] mne/io/fieldtrip/tests/test_fieldtrip.py:53: could not import 'pymatreader': No module named 'pymatreader' SKIPPED [2] mne/io/neuralynx/tests/test_neuralynx.py:21: could not import 'neo': No module named 'neo' SKIPPED [2] mne/io/snirf/tests/test_snirf.py:62: could not import 'h5py': No module named 'h5py' SKIPPED [2] mne/preprocessing/eyetracking/tests/test_pupillometry.py:15: could not import 'pandas': No module named 'pandas' SKIPPED [1] ../../../usr/lib/python3/dist-packages/_pytest/doctest.py:569: unable to import module PosixPath('/build/reproducible-path/python-mne-1.8.0/mne/viz/backends/_notebook.py') SKIPPED [1] ../../../usr/lib/python3/dist-packages/_pytest/doctest.py:569: unable to import module PosixPath('/build/reproducible-path/python-mne-1.8.0/mne/viz/backends/_pyvista.py') SKIPPED [1] ../../../usr/lib/python3/dist-packages/_pytest/doctest.py:569: unable to import module PosixPath('/build/reproducible-path/python-mne-1.8.0/mne/viz/backends/_qt.py') SKIPPED [20] ../../../usr/lib/python3/dist-packages/_pytest/doctest.py:458: all tests skipped by +SKIP option SKIPPED [1] mne/_fiff/tests/test_compensator.py:74: Requires MNE-C SKIPPED [1] mne/_fiff/tests/test_constants.py:119: MNE_SKIP_NETWORK_TESTS is set SKIPPED [1] mne/_fiff/tests/test_meas_info.py:348: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_meas_info.py:796: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_meas_info.py:824: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_meas_info.py:919: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_meas_info.py:927: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_pick.py:428: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_pick.py:585: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_reference.py:85: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_reference.py:164: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_reference.py:307: Requires testing dataset SKIPPED [3] mne/_fiff/tests/test_reference.py:364: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_reference.py:526: Requires testing dataset SKIPPED [1] mne/_fiff/tests/test_what.py:20: Requires testing dataset SKIPPED [8] mne/beamformer/tests/test_dics.py:156: Requires testing dataset SKIPPED [8] mne/beamformer/tests/test_dics.py:436: Requires testing dataset SKIPPED [32] mne/beamformer/tests/test_dics.py:479: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_dics.py:545: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_dics.py:605: Requires testing dataset SKIPPED [2] mne/beamformer/tests/test_dics.py:708: Requires testing dataset SKIPPED [19] mne/beamformer/tests/test_dics.py:789: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_dics.py:852: Requires testing dataset SKIPPED [8] mne/beamformer/tests/test_lcmv.py:812: Requires testing dataset SKIPPED [21] mne/beamformer/tests/test_lcmv.py:854: Requires testing dataset SKIPPED [9] mne/beamformer/tests/test_lcmv.py:975: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_lcmv.py:1043: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:248: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:288: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:334: Requires testing dataset SKIPPED [8] mne/minimum_norm/tests/test_inverse.py:391: Requires testing dataset SKIPPED [16] mne/minimum_norm/tests/test_inverse.py:419: Requires testing dataset SKIPPED [10] mne/minimum_norm/tests/test_inverse.py:462: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:540: Requires testing dataset SKIPPED [6] mne/minimum_norm/tests/test_inverse.py:571: Requires testing dataset SKIPPED [2] mne/minimum_norm/tests/test_inverse.py:599: Requires testing dataset SKIPPED [8] mne/minimum_norm/tests/test_inverse.py:691: Requires testing dataset SKIPPED [8] mne/minimum_norm/tests/test_inverse.py:768: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:813: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:876: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:902: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:940: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:962: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:980: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1002: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1421: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1461: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1501: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1661: Requires testing dataset SKIPPED [2] mne/minimum_norm/tests/test_resolution_matrix.py:29: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:336: Requires testing dataset SKIPPED [6] mne/viz/tests/test_3d.py:1044: Requires testing dataset SKIPPED [3] mne/time_frequency/tests/test_spectrum.py:165: Requires testing dataset SKIPPED [3] mne/time_frequency/tests/test_spectrum.py:239: Requires testing dataset SKIPPED [6] mne/viz/tests/test_evoked.py:398: Requires testing dataset SKIPPED [1] mne/viz/tests/test_evoked.py:420: Requires testing dataset SKIPPED [1] mne/viz/tests/test_topomap.py:209: Requires testing dataset SKIPPED [3] mne/viz/tests/test_topomap.py:259: Requires testing dataset SKIPPED [3] mne/viz/tests/test_topomap.py:279: Requires testing dataset SKIPPED [1] mne/viz/tests/test_topomap.py:648: Requires testing dataset SKIPPED [1] mne/time_frequency/tests/test_tfr.py:702: Requires testing dataset SKIPPED [15] mne/time_frequency/tests/test_tfr.py:833: Requires testing dataset SKIPPED [4] mne/time_frequency/tests/test_tfr.py:883: Requires testing dataset SKIPPED [1] mne/time_frequency/tests/test_tfr.py:898: Requires testing dataset SKIPPED [12] mne/time_frequency/tests/test_tfr.py:1428: Requires testing dataset SKIPPED [5] mne/time_frequency/tests/test_tfr.py:1693: Requires testing dataset SKIPPED [8] mne/beamformer/tests/test_dics.py:914: Requires testing dataset SKIPPED [5] mne/beamformer/tests/test_external.py:66: Requires testing dataset SKIPPED [1] mne/beamformer/tests/test_lcmv.py:165: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_lcmv.py:262: Requires testing dataset SKIPPED [5] mne/beamformer/tests/test_lcmv.py:566: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_lcmv.py:640: Requires testing dataset SKIPPED [1] mne/beamformer/tests/test_lcmv.py:685: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_lcmv.py:715: Requires testing dataset SKIPPED [4] mne/beamformer/tests/test_lcmv.py:1104: Requires testing dataset SKIPPED [13] mne/beamformer/tests/test_lcmv.py:1125: Requires testing dataset SKIPPED [1] mne/beamformer/tests/test_rap_music.py:113: Requires testing dataset SKIPPED [1] mne/beamformer/tests/test_rap_music.py:165: Requires testing dataset SKIPPED [1] mne/beamformer/tests/test_rap_music.py:201: Requires testing dataset SKIPPED [1] mne/beamformer/tests/test_rap_music.py:213: Requires testing dataset SKIPPED [1] mne/beamformer/tests/test_resolution_matrix.py:32: Requires testing dataset SKIPPED [1] mne/channels/tests/test_channels.py:379: MNE_SKIP_NETWORK_TESTS is set SKIPPED [1] mne/channels/tests/test_channels.py:428: Requires testing dataset SKIPPED [1] mne/channels/tests/test_channels.py:464: Requires testing dataset SKIPPED [1] mne/channels/tests/test_channels.py:511: Requires testing dataset SKIPPED [1] mne/channels/tests/test_channels.py:705: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/channels/tests/test_interpolation.py:301: Requires testing dataset SKIPPED [1] mne/channels/tests/test_interpolation.py:311: Requires testing dataset SKIPPED [1] mne/channels/tests/test_interpolation.py:335: Requires testing dataset SKIPPED [1] mne/channels/tests/test_interpolation.py:355: Requires testing dataset SKIPPED [1] mne/channels/tests/test_interpolation.py:429: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:540: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:999: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:1057: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:1107: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:1361: Requires testing dataset SKIPPED [4] mne/channels/tests/test_montage.py:1693: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:1779: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:1899: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:1932: Requires testing dataset SKIPPED [1] mne/channels/tests/test_montage.py:2020: Requires testing dataset SKIPPED [1] mne/commands/tests/test_commands.py:123: Requires MNE-C SKIPPED [1] mne/commands/tests/test_commands.py:170: Requires testing dataset SKIPPED [1] mne/commands/tests/test_commands.py:219: Requires testing dataset SKIPPED [1] mne/commands/tests/test_commands.py:254: Requires testing dataset SKIPPED [1] mne/commands/tests/test_commands.py:301: Requires testing dataset SKIPPED [1] mne/commands/tests/test_commands.py:387: Requires testing dataset SKIPPED [1] mne/commands/tests/test_commands.py:467: Requires testing dataset SKIPPED [1] mne/commands/tests/test_commands.py:497: Requires testing dataset SKIPPED [1] mne/datasets/sleep_physionet/tests/test_physionet.py:56: MNE_SKIP_NETWORK_TESTS is set SKIPPED [1] mne/datasets/sleep_physionet/tests/test_physionet.py:179: MNE_SKIP_NETWORK_TESTS is set SKIPPED [1] mne/datasets/tests/test_datasets.py:97: MNE_SKIP_NETWORK_TESTS is set SKIPPED [1] mne/datasets/tests/test_datasets.py:180: MNE_SKIP_NETWORK_TESTS is set SKIPPED [1] mne/datasets/tests/test_datasets.py:315: MNE_SKIP_NETWORK_TESTS is set SKIPPED [2] ../../../usr/lib/python3/dist-packages/_pytest/unittest.py:385: pandas is not installed: not checking estimators for pandas objects. SKIPPED [2] mne/export/tests/test_export.py:58: could not import 'pybv': No module named 'pybv' SKIPPED [1] mne/export/tests/test_export.py:94: could not import 'eeglabio': No module named 'eeglabio' SKIPPED [1] mne/export/tests/test_export.py:152: unsafe use of private module SKIPPED [1] mne/export/tests/test_export.py:192: unsafe use of private module SKIPPED [5] mne/export/tests/test_export.py:219: unsafe use of private module SKIPPED [1] mne/export/tests/test_export.py:260: unsafe use of private module SKIPPED [1] mne/export/tests/test_export.py:284: unsafe use of private module SKIPPED [1] mne/export/tests/test_export.py:323: unsafe use of private module SKIPPED [1] mne/export/tests/test_export.py:340: unsafe use of private module SKIPPED [1] mne/export/tests/test_export.py:349: unsafe use of private module SKIPPED [2] mne/export/tests/test_export.py:358: unsafe use of private module SKIPPED [2] mne/export/tests/test_export.py:382: unsafe use of private module SKIPPED [1] mne/export/tests/test_export.py:431: unsafe use of private module SKIPPED [2] mne/export/tests/test_export.py:456: could not import 'eeglabio': No module named 'eeglabio' SKIPPED [4] mne/export/tests/test_export.py:497: Requires testing dataset SKIPPED [1] mne/export/tests/test_export.py:556: Requires testing dataset SKIPPED [1] mne/export/tests/test_export.py:571: could not import 'mffpy': No module named 'mffpy' SKIPPED [1] mne/forward/tests/test_field_interpolation.py:44: Requires testing dataset SKIPPED [1] mne/forward/tests/test_field_interpolation.py:124: Requires testing dataset SKIPPED [1] mne/forward/tests/test_field_interpolation.py:156: Requires testing dataset SKIPPED [1] mne/forward/tests/test_field_interpolation.py:216: Requires testing dataset SKIPPED [1] mne/forward/tests/test_field_interpolation.py:272: Requires testing dataset SKIPPED [1] mne/forward/tests/test_forward.py:71: Requires testing dataset SKIPPED [1] mne/forward/tests/test_forward.py:108: Requires testing dataset SKIPPED [1] mne/forward/tests/test_forward.py:212: Requires testing dataset SKIPPED [1] mne/forward/tests/test_forward.py:277: Requires testing dataset SKIPPED [1] mne/forward/tests/test_forward.py:331: Requires testing dataset SKIPPED [2] mne/forward/tests/test_forward.py:393: Requires testing dataset SKIPPED [1] mne/forward/tests/test_forward.py:435: Requires MNE-C SKIPPED [1] mne/forward/tests/test_forward.py:485: Requires testing dataset SKIPPED [1] mne/forward/tests/test_forward.py:520: Requires testing dataset SKIPPED [1] mne/forward/tests/test_make_forward.py:212: Requires MNE-C SKIPPED [1] mne/forward/tests/test_make_forward.py:270: Requires MNE-C SKIPPED [2] mne/forward/tests/test_make_forward.py:295: Requires testing dataset SKIPPED [1] mne/forward/tests/test_make_forward.py:490: Requires testing dataset SKIPPED [1] mne/forward/tests/test_make_forward.py:529: Requires MNE-C SKIPPED [1] mne/forward/tests/test_make_forward.py:409: Requires testing dataset SKIPPED [2] mne/forward/tests/test_make_forward.py:434: Requires testing dataset SKIPPED [1] mne/forward/tests/test_make_forward.py:595: Requires testing dataset SKIPPED [1] mne/forward/tests/test_make_forward.py:652: Requires testing dataset SKIPPED [1] mne/forward/tests/test_make_forward.py:773: Requires testing dataset SKIPPED [1] mne/forward/tests/test_make_forward.py:826: Requires testing dataset SKIPPED [6] mne/gui/tests/test_coreg.py:74: Requires testing dataset SKIPPED [1] mne/gui/tests/test_coreg.py:119: Requires testing dataset SKIPPED [1] mne/gui/tests/test_coreg.py:301: Requires testing dataset SKIPPED [1] mne/gui/tests/test_coreg.py:313: Requires testing dataset SKIPPED [1] mne/gui/tests/test_coreg.py:336: Requires testing dataset SKIPPED [1] mne/gui/tests/test_coreg.py:354: Requires testing dataset SKIPPED [1] mne/gui/tests/test_coreg.py:377: Test skipped, requires pyvista. SKIPPED [1] mne/gui/tests/test_gui_api.py:13: Skipping Notebook test: No module named 'nbformat' SKIPPED [1] mne/gui/tests/test_gui_api.py:383: Test skipped, requires pyvista. SKIPPED [1] mne/inverse_sparse/tests/test_gamma_map.py:59: Requires testing dataset SKIPPED [1] mne/inverse_sparse/tests/test_gamma_map.py:155: Requires testing dataset SKIPPED [1] mne/inverse_sparse/tests/test_mxne_inverse.py:49: Requires testing dataset SKIPPED [6] mne/inverse_sparse/tests/test_mxne_inverse.py:456: Requires testing dataset SKIPPED [1] mne/inverse_sparse/tests/test_mxne_inverse.py:297: Requires testing dataset SKIPPED [1] mne/inverse_sparse/tests/test_mxne_inverse.py:550: Requires testing dataset SKIPPED [1] mne/inverse_sparse/tests/test_mxne_inverse.py:596: Requires testing dataset SKIPPED [1] mne/io/artemis123/tests/test_artemis123.py:44: Requires testing dataset SKIPPED [1] mne/io/artemis123/tests/test_artemis123.py:56: Requires testing dataset SKIPPED [1] mne/io/boxy/tests/test_boxy.py:68: Requires testing dataset SKIPPED [2] mne/io/boxy/tests/test_boxy.py:131: Requires testing dataset SKIPPED [2] mne/io/boxy/tests/test_boxy.py:166: Requires testing dataset SKIPPED [3] mne/io/boxy/tests/test_boxy.py:215: Requires testing dataset SKIPPED [1] mne/io/brainvision/tests/test_brainvision.py:638: Requires testing dataset SKIPPED [1] mne/io/brainvision/tests/test_brainvision.py:647: Requires testing dataset SKIPPED [1] mne/io/brainvision/tests/test_brainvision.py:706: Requires testing dataset SKIPPED [1] mne/io/brainvision/tests/test_brainvision.py:832: Requires testing dataset SKIPPED [1] mne/io/brainvision/tests/test_brainvision.py:841: Requires testing dataset SKIPPED [1] mne/io/brainvision/tests/test_brainvision.py:963: Requires testing dataset SKIPPED [1] mne/io/bti/tests/test_bti.py:62: Requires testing dataset SKIPPED [4] mne/io/bti/tests/test_bti.py:427: Requires testing dataset SKIPPED [1] mne/io/bti/tests/test_bti.py:435: Requires testing dataset SKIPPED [1] mne/io/bti/tests/test_bti.py:444: Requires testing dataset SKIPPED [1] mne/io/cnt/tests/test_cnt.py:24: Requires testing dataset SKIPPED [1] mne/io/cnt/tests/test_cnt.py:43: 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testing dataset SKIPPED [4] mne/io/curry/tests/test_curry.py:90: Requires testing dataset SKIPPED [4] mne/io/curry/tests/test_curry.py:209: Requires testing dataset SKIPPED [2] mne/io/curry/tests/test_curry.py:291: Requires testing dataset SKIPPED [1] mne/io/curry/tests/test_curry.py:310: Requires testing dataset SKIPPED [5] mne/io/curry/tests/test_curry.py:376: Requires testing dataset SKIPPED [4] mne/io/curry/tests/test_curry.py:383: Requires testing dataset SKIPPED [4] mne/io/curry/tests/test_curry.py:467: Requires testing dataset SKIPPED [2] mne/io/curry/tests/test_curry.py:538: Requires testing dataset SKIPPED [3] mne/io/curry/tests/test_curry.py:568: Requires testing dataset SKIPPED [2] mne/io/curry/tests/test_curry.py:590: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:116: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:194: Requires testing dataset SKIPPED [6] mne/io/edf/tests/test_edf.py:201: Requires testing dataset SKIPPED [3] mne/io/edf/tests/test_edf.py:222: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:373: Requires testing dataset SKIPPED [2] mne/io/edf/tests/test_edf.py:401: could not import 'pandas': No module named 'pandas' SKIPPED [2] mne/io/edf/tests/test_edf.py:474: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:482: Requires testing dataset SKIPPED [2] mne/io/edf/tests/test_edf.py:713: Requires testing dataset SKIPPED [2] mne/io/edf/tests/test_edf.py:789: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:826: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:834: Requires testing dataset SKIPPED [5] mne/io/edf/tests/test_edf.py:901: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:1026: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_edf.py:1182: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_gdf.py:23: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_gdf.py:80: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_gdf.py:110: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_gdf.py:149: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_gdf.py:157: Requires testing dataset SKIPPED [1] mne/io/edf/tests/test_gdf.py:177: Requires testing dataset SKIPPED [3] mne/io/eeglab/tests/test_eeglab.py:47: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:108: Requires testing dataset SKIPPED [2] mne/io/eeglab/tests/test_eeglab.py:329: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:359: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:375: Requires testing dataset SKIPPED [2] mne/io/eeglab/tests/test_eeglab.py:419: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:438: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:471: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:530: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:614: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:637: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:649: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:656: Requires testing dataset SKIPPED [2] mne/io/eeglab/tests/test_eeglab.py:687: Requires testing dataset SKIPPED [1] mne/io/eeglab/tests/test_eeglab.py:718: Requires testing dataset SKIPPED [3] mne/io/egi/tests/test_egi.py:64: Requires testing dataset SKIPPED [3] mne/io/egi/tests/test_egi.py:124: Requires testing dataset SKIPPED [2] mne/io/egi/tests/test_egi.py:146: Requires testing dataset SKIPPED [1] mne/io/egi/tests/test_egi.py:292: Requires testing dataset SKIPPED [2] mne/io/egi/tests/test_egi.py:348: Requires testing dataset SKIPPED [1] mne/io/egi/tests/test_egi.py:392: Requires testing dataset SKIPPED [2] mne/io/egi/tests/test_egi.py:405: Requires testing dataset SKIPPED [1] mne/io/egi/tests/test_egi.py:486: Requires testing dataset SKIPPED [1] mne/io/egi/tests/test_egi.py:505: Requires testing dataset SKIPPED [4] mne/io/egi/tests/test_egi.py:534: Requires testing dataset SKIPPED [2] mne/io/egi/tests/test_egi.py:558: Requires testing dataset SKIPPED [1] mne/io/eximia/tests/test_eximia.py:15: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:65: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:156: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:176: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:184: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:210: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:220: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:249: Requires testing dataset SKIPPED [3] mne/io/fiff/tests/test_raw_fiff.py:387: Requires testing dataset SKIPPED [2] mne/io/fiff/tests/test_raw_fiff.py:495: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:686: could not import 'mne_bids': No module named 'mne_bids' SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:775: Requires testing dataset SKIPPED [2] mne/io/fiff/tests/test_raw_fiff.py:912: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:937: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:963: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:972: Requires testing dataset SKIPPED [3] mne/io/fiff/tests/test_raw_fiff.py:1058: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1087: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1246: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1320: Requires testing dataset SKIPPED [3] mne/io/fiff/tests/test_raw_fiff.py:1330: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1495: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1536: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1556: could not import 'pandas': No module named 'pandas' SKIPPED [4] mne/io/fiff/tests/test_raw_fiff.py:1582: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1671: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1706: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1740: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1846: Requires MNE-C SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1887: Requires testing dataset SKIPPED [2] mne/io/fiff/tests/test_raw_fiff.py:1923: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:1947: Requires testing dataset SKIPPED [2] mne/io/fiff/tests/test_raw_fiff.py:2068: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:2088: Requires testing dataset SKIPPED [2] mne/io/fiff/tests/test_raw_fiff.py:2114: Requires testing dataset SKIPPED [1] mne/io/fiff/tests/test_raw_fiff.py:2139: Requires testing dataset SKIPPED [1] mne/io/fil/tests/test_fil.py:144: Requires testing dataset SKIPPED [1] mne/io/fil/tests/test_fil.py:160: Requires testing dataset SKIPPED [1] mne/io/fil/tests/test_fil.py:178: Requires testing dataset SKIPPED [1] mne/io/kit/tests/test_kit.py:61: Requires testing dataset SKIPPED [1] mne/io/kit/tests/test_kit.py:198: Requires testing dataset SKIPPED [2] mne/io/kit/tests/test_kit.py:229: Requires testing dataset SKIPPED [4] mne/io/kit/tests/test_kit.py:413: Requires testing dataset SKIPPED [1] mne/io/kit/tests/test_kit.py:435: Requires testing dataset SKIPPED [1] mne/io/nedf/tests/test_nedf.py:99: Requires testing dataset SKIPPED [1] mne/io/nihon/tests/test_nihon.py:21: Requires testing dataset SKIPPED [1] mne/io/nihon/tests/test_nihon.py:76: Requires testing dataset SKIPPED [2] mne/io/nirx/tests/test_nirx.py:60: could not import 'h5py': No module named 'h5py' SKIPPED [1] mne/io/nirx/tests/test_nirx.py:78: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:145: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:169: Requires testing dataset SKIPPED [4] mne/io/nirx/tests/test_nirx.py:192: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:228: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:239: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:246: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:259: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:272: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:371: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:460: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:492: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:545: Requires testing dataset SKIPPED [1] mne/io/nirx/tests/test_nirx.py:554: Requires testing dataset SKIPPED [4] mne/io/nirx/tests/test_nirx.py:621: Requires testing dataset SKIPPED [7] mne/io/nirx/tests/test_nirx.py:639: Requires testing dataset SKIPPED [1] mne/io/nsx/tests/test_nsx.py:51: Requires testing dataset SKIPPED [1] mne/io/nsx/tests/test_nsx.py:60: Requires testing dataset SKIPPED [1] mne/io/nsx/tests/test_nsx.py:134: Requires testing dataset SKIPPED [1] mne/io/nsx/tests/test_nsx.py:197: Requires testing dataset SKIPPED [1] mne/io/nsx/tests/test_nsx.py:225: Requires testing dataset SKIPPED [1] mne/io/nsx/tests/test_nsx.py:232: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:25: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:50: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:77: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:117: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:141: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:183: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:189: Requires testing dataset SKIPPED [1] mne/io/persyst/tests/test_persyst.py:210: Requires testing dataset SKIPPED [1] mne/io/tests/test_raw.py:846: could not import 'pandas': No module named 'pandas' SKIPPED [4] mne/io/tests/test_read_raw.py:46: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1023: Requires testing dataset SKIPPED [6] mne/minimum_norm/tests/test_inverse.py:1060: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1122: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1181: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1255: Requires testing dataset SKIPPED [2] mne/minimum_norm/tests/test_inverse.py:1370: Requires testing dataset SKIPPED [1] mne/minimum_norm/tests/test_inverse.py:1438: Requires testing dataset 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mne/preprocessing/ieeg/tests/test_projection.py:80: Requires testing dataset SKIPPED [1] mne/preprocessing/ieeg/tests/test_volume.py:20: Requires testing dataset SKIPPED [1] mne/preprocessing/ieeg/tests/test_volume.py:74: Requires testing dataset SKIPPED [6] mne/preprocessing/nirs/tests/test_beer_lambert_law.py:22: Requires testing dataset SKIPPED [1] mne/preprocessing/nirs/tests/test_beer_lambert_law.py:49: Requires testing dataset SKIPPED [1] mne/preprocessing/nirs/tests/test_beer_lambert_law.py:72: Requires testing dataset SKIPPED [1] mne/preprocessing/nirs/tests/test_nirs.py:40: Requires testing dataset SKIPPED [3] mne/preprocessing/nirs/tests/test_nirs.py:107: Requires testing dataset SKIPPED [3] mne/preprocessing/nirs/tests/test_nirs.py:145: Requires testing dataset SKIPPED [3] mne/preprocessing/nirs/tests/test_nirs.py:170: Requires testing dataset SKIPPED [1] mne/preprocessing/nirs/tests/test_nirs.py:499: Requires testing dataset SKIPPED [2] mne/preprocessing/nirs/tests/test_nirs.py:507: could not import 'h5py': No module named 'h5py' SKIPPED [1] mne/preprocessing/nirs/tests/test_optical_density.py:20: Requires testing dataset SKIPPED [1] mne/preprocessing/nirs/tests/test_optical_density.py:34: Requires testing dataset SKIPPED [1] mne/preprocessing/nirs/tests/test_optical_density.py:46: Requires testing dataset SKIPPED [6] mne/preprocessing/nirs/tests/test_scalp_coupling_index.py:29: Requires testing dataset SKIPPED [1] mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py:19: Requires testing dataset SKIPPED [1] mne/preprocessing/tests/test_annotate_amplitude.py:242: Requires testing dataset SKIPPED [2] mne/preprocessing/tests/test_artifact_detection.py:28: Requires testing dataset SKIPPED [2] mne/preprocessing/tests/test_artifact_detection.py:160: Requires testing dataset SKIPPED [2] mne/preprocessing/tests/test_artifact_detection.py:180: Requires testing dataset SKIPPED [2] mne/preprocessing/tests/test_artifact_detection.py:193: Requires testing dataset SKIPPED [1] mne/preprocessing/tests/test_csd.py:63: Requires testing dataset SKIPPED [1] mne/preprocessing/tests/test_csd.py:84: Requires testing dataset SKIPPED [1] mne/preprocessing/tests/test_css.py:15: Requires testing dataset SKIPPED [1] mne/preprocessing/tests/test_eeglab_infomax.py:70: Requires testing dataset SKIPPED [3] mne/preprocessing/tests/test_fine_cal.py:32: Requires testing dataset SKIPPED [1] mne/preprocessing/tests/test_fine_cal.py:48: Requires testing dataset SKIPPED [3] mne/preprocessing/tests/test_hfc.py:64: Requires testing dataset SKIPPED [1] mne/preprocessing/tests/test_hfc.py:93: Requires testing dataset SKIPPED [20] mne/preprocessing/tests/test_ica.py:91: could not import 'picard': No module named 'picard' SKIPPED [8] mne/preprocessing/tests/test_ica.py:461: Qt API None has version None but pyqtgraph needs >= 5.12! 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mne/report/tests/test_report.py:1071: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:29: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:52: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:76: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:98: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:130: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:159: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:184: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:200: Requires testing dataset SKIPPED [1] mne/simulation/metrics/tests/test_metrics.py:224: Requires testing dataset SKIPPED [1] mne/simulation/tests/test_evoked.py:41: Requires testing dataset SKIPPED [1] mne/simulation/tests/test_evoked.py:171: Requires testing dataset 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mne/stats/tests/test_cluster_level.py:252: Numba not installed SKIPPED [1] mne/stats/tests/test_cluster_level.py:335: Numba not installed SKIPPED [1] mne/stats/tests/test_cluster_level.py:570: Numba not installed SKIPPED [1] mne/stats/tests/test_cluster_level.py:649: Numba not installed SKIPPED [1] mne/stats/tests/test_cluster_level.py:770: Numba not installed SKIPPED [1] mne/stats/tests/test_cluster_level.py:795: Numba not installed SKIPPED [1] mne/stats/tests/test_regression.py:22: Requires testing dataset SKIPPED [1] mne/stats/tests/test_regression.py:79: Requires testing dataset SKIPPED [1] mne/stats/tests/test_regression.py:120: Requires testing dataset SKIPPED [1] mne/tests/test_annotations.py:395: Requires testing dataset SKIPPED [2] mne/tests/test_annotations.py:405: Requires testing dataset SKIPPED [1] mne/tests/test_annotations.py:717: Requires testing dataset SKIPPED [4] mne/tests/test_annotations.py:1007: Needs pandas SKIPPED [1] mne/tests/test_annotations.py:1035: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/tests/test_annotations.py:1351: Requires testing dataset SKIPPED [4] mne/tests/test_annotations.py:1465: could not import 'pandas': No module named 'pandas' SKIPPED [2] mne/tests/test_bem.py:103: Requires testing dataset SKIPPED [2] mne/tests/test_bem.py:184: Requires testing dataset SKIPPED [1] mne/tests/test_bem.py:217: Requires testing dataset SKIPPED [2] mne/tests/test_bem.py:242: Requires testing dataset SKIPPED [1] mne/tests/test_bem.py:497: Requires testing dataset SKIPPED [1] mne/tests/test_bem.py:525: could not import 'pyvista': No module named 'pyvista' SKIPPED [4] mne/tests/test_bem.py:574: Requires testing dataset SKIPPED [1] mne/tests/test_chpi.py:88: Requires testing dataset SKIPPED [1] mne/tests/test_chpi.py:133: Requires testing dataset SKIPPED [1] mne/tests/test_chpi.py:153: Requires testing dataset SKIPPED [1] mne/tests/test_chpi.py:308: Requires testing dataset SKIPPED [1] mne/tests/test_chpi.py:319: 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dataset SKIPPED [7] mne/tests/test_coreg.py:395: Requires testing dataset SKIPPED [1] mne/tests/test_coreg.py:489: Requires testing dataset SKIPPED [4] mne/tests/test_coreg.py:578: Requires testing dataset SKIPPED [1] mne/tests/test_cov.py:892: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:90: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:101: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:124: Requires MNE-C SKIPPED [1] mne/tests/test_dipole.py:254: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:316: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:332: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:383: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:431: Requires testing dataset SKIPPED [1] mne/tests/test_dipole.py:494: Requires testing dataset SKIPPED [2] mne/tests/test_dipole.py:525: Requires testing dataset SKIPPED [1] mne/tests/test_docstring_parameters.py:167: could not import 'numpydoc': No module named 'numpydoc' SKIPPED [1] mne/tests/test_epochs.py:364: Requires testing dataset SKIPPED [12] mne/tests/test_epochs.py:1636: could not import 'pandas': No module named 'pandas' SKIPPED [18] mne/tests/test_epochs.py:1657: could not import 'pandas': No module named 'pandas' SKIPPED [5] mne/tests/test_epochs.py:1707: could not import 'mne_bids': No module named 'mne_bids' SKIPPED [1] mne/tests/test_epochs.py:2228: Requires testing dataset SKIPPED [1] mne/tests/test_epochs.py:3113: could not import 'pandas': No module named 'pandas' SKIPPED [5] mne/tests/test_epochs.py:3155: could not import 'pandas': No module named 'pandas' SKIPPED [3] mne/tests/test_epochs.py:3172: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/tests/test_epochs.py:3697: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/tests/test_epochs.py:3951: could not import 'pandas': No module named 'pandas' SKIPPED [7] mne/tests/test_epochs.py:4221: could not import 'pandas': No module named 'pandas' SKIPPED [6] mne/tests/test_epochs.py:4304: could not import 'pandas': No module named 'pandas' SKIPPED [4] mne/tests/test_epochs.py:4560: Requires testing dataset SKIPPED [1] mne/tests/test_epochs.py:4684: Requires testing dataset SKIPPED [1] mne/tests/test_epochs.py:4889: Requires testing dataset SKIPPED [6] mne/tests/test_epochs.py:4928: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/tests/test_epochs.py:5180: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/tests/test_epochs.py:5260: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/tests/test_event.py:523: Requires testing dataset SKIPPED [1] mne/tests/test_event.py:568: Requires testing dataset SKIPPED [1] mne/tests/test_evoked.py:448: could not import 'pandas': No module named 'pandas' SKIPPED [3] mne/tests/test_evoked.py:480: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/tests/test_filter.py:53: Requires MNE-C SKIPPED [1] mne/tests/test_filter.py:838: CUDA not enabled SKIPPED [1] mne/tests/test_freesurfer.py:36: Requires testing dataset SKIPPED [1] mne/tests/test_freesurfer.py:44: Requires testing dataset SKIPPED [1] mne/tests/test_freesurfer.py:55: Requires testing dataset SKIPPED [1] mne/tests/test_freesurfer.py:75: Requires testing dataset SKIPPED [1] mne/tests/test_freesurfer.py:110: Requires testing dataset SKIPPED [2] mne/tests/test_freesurfer.py:219: Requires testing dataset SKIPPED [1] mne/tests/test_freesurfer.py:278: Requires testing dataset SKIPPED [2] mne/tests/test_label.py:277: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:320: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:331: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:365: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:415: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:448: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:468: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:546: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:559: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:738: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:836: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:919: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:955: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:1023: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:1063: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:1092: Requires testing dataset SKIPPED [1] mne/tests/test_label.py:1162: Requires testing dataset SKIPPED [2] mne/tests/test_label.py:1219: Requires testing dataset SKIPPED [1] mne/tests/test_morph.py:80: Requires testing dataset SKIPPED [1] mne/tests/test_morph.py:139: Requires testing dataset SKIPPED [3] mne/tests/test_morph.py:232: Requires testing dataset SKIPPED [1] mne/tests/test_morph.py:265: Requires testing dataset SKIPPED [1] mne/tests/test_morph.py:297: Requires testing dataset SKIPPED [1] mne/tests/test_morph.py:354: Requires testing dataset SKIPPED [5] mne/tests/test_morph.py:551: Requires testing dataset SKIPPED [1] mne/tests/test_morph.py:716: Requires testing dataset SKIPPED [1] mne/tests/test_morph.py:819: Requires testing dataset SKIPPED [3] mne/tests/test_morph.py:892: Requires testing dataset SKIPPED [2] mne/tests/test_morph.py:1016: Requires testing dataset SKIPPED [576] mne/tests/test_morph.py:1103: could not import 'dipy': No module named 'dipy' SKIPPED [1] mne/tests/test_morph_map.py:21: Requires testing dataset SKIPPED [6] mne/tests/test_parallel.py:29: MNE_FORCE_SERIAL cannot be set SKIPPED [1] mne/tests/test_proj.py:126: Requires testing dataset SKIPPED [1] mne/tests/test_proj.py:559: Requires testing dataset SKIPPED [18] mne/tests/test_rank.py:221: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:118: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:146: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:182: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:252: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:280: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:304: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:345: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:392: Requires testing dataset SKIPPED [4] mne/tests/test_source_estimate.py:502: could not import 'h5io': No module named 'h5io' SKIPPED [4] mne/tests/test_source_estimate.py:588: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:659: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:668: Requires testing dataset SKIPPED [4] mne/tests/test_source_estimate.py:682: Requires testing dataset SKIPPED [6] mne/tests/test_source_estimate.py:844: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1043: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1173: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1211: could not import 'pandas': No module named 'pandas' SKIPPED [3] mne/tests/test_source_estimate.py:1235: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/tests/test_source_estimate.py:1305: Requires testing dataset SKIPPED [16] mne/tests/test_source_estimate.py:1344: could not import 'h5io': No module named 'h5io' SKIPPED [1] mne/tests/test_source_estimate.py:1472: Requires testing dataset SKIPPED [3] mne/tests/test_source_estimate.py:1527: Requires testing dataset SKIPPED [3] mne/tests/test_source_estimate.py:1536: Requires testing dataset SKIPPED [3] mne/tests/test_source_estimate.py:1545: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1568: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1599: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1618: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1645: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1666: Requires testing dataset SKIPPED [1] mne/tests/test_source_estimate.py:1780: Requires testing dataset SKIPPED [6] mne/tests/test_source_estimate.py:1821: Requires testing dataset SKIPPED [2] mne/tests/test_source_estimate.py:2005: Requires testing dataset SKIPPED [1] mne/tests/test_surface.py:79: Requires testing dataset SKIPPED [1] mne/tests/test_surface.py:126: Requires testing dataset SKIPPED [1] mne/tests/test_surface.py:158: Requires testing dataset SKIPPED [3] mne/tests/test_surface.py:173: could not import 'pyvista': No module named 'pyvista' SKIPPED [1] mne/tests/test_surface.py:196: Requires Freesurfer command: mris_sphere SKIPPED [3] mne/tests/test_surface.py:214: Requires testing dataset SKIPPED [24] mne/tests/test_surface.py:254: could not import 'pyvista': No module named 'pyvista' SKIPPED [1] mne/tests/test_surface.py:284: Requires testing dataset SKIPPED [4] mne/tests/test_surface.py:336: Requires testing dataset SKIPPED [1] mne/tests/test_transforms.py:93: Requires testing dataset SKIPPED [1] mne/tests/test_transforms.py:102: Requires testing dataset SKIPPED [1] mne/tests/test_transforms.py:301: Requires testing dataset SKIPPED [2] mne/tests/test_transforms.py:422: Requires testing dataset SKIPPED [1] mne/tests/test_transforms.py:573: Requires testing dataset SKIPPED [1] mne/time_frequency/tests/test_ar.py:20: could not import 'patsy': No module named 'patsy' SKIPPED [1] mne/time_frequency/tests/test_csd.py:250: could not import 'h5io': No module named 'h5io' SKIPPED [1] mne/time_frequency/tests/test_multitaper.py:16: could not import 'nitime': No module named 'nitime' SKIPPED [6] mne/time_frequency/tests/test_multitaper.py:41: could not import 'nitime': No module named 'nitime' SKIPPED [6] mne/time_frequency/tests/test_spectrum.py:305: could not import 'pandas': No module named 'pandas' SKIPPED [3] mne/time_frequency/tests/test_tfr.py:611: could not import 'h5io': No module named 'h5io' SKIPPED [1] mne/time_frequency/tests/test_tfr.py:674: could not import 'h5io': No module named 'h5io' SKIPPED [2] mne/time_frequency/tests/test_tfr.py:1224: could not import 'pandas': No module named 'pandas' SKIPPED [1] mne/time_frequency/tests/test_tfr.py:1252: could not import 'pandas': No module named 'pandas' SKIPPED [5] mne/time_frequency/tests/test_tfr.py:1342: could not import 'pandas': No module named 'pandas' SKIPPED [3] mne/time_frequency/tests/test_tfr.py:1379: could not import 'pandas': No module named 'pandas' SKIPPED [12] mne/time_frequency/tests/test_tfr.py:1422: could not import 'h5io': No module named 'h5io' SKIPPED [1] mne/utils/tests/test_check.py:48: Requires testing dataset SKIPPED [6] mne/utils/tests/test_check.py:78: Requires testing dataset SKIPPED [1] mne/utils/tests/test_check.py:128: Requires testing dataset SKIPPED [1] mne/utils/tests/test_check.py:239: Requires testing dataset SKIPPED [1] mne/utils/tests/test_check.py:282: Requires testing dataset SKIPPED [1] mne/utils/tests/test_check.py:365: Requires testing dataset SKIPPED [1] mne/utils/tests/test_config.py:138: could not import 'mne_qt_browser': No module named 'mne_qt_browser' SKIPPED [1] mne/utils/tests/test_config.py:173: MNE_SKIP_NETWORK_TESTS is set SKIPPED [1] mne/utils/tests/test_mixin.py:15: Requires testing dataset SKIPPED [16] mne/utils/tests/test_numerics.py:554: Numba not installed SKIPPED [1] mne/viz/_brain/tests/test_brain.py:121: Test skipped, requires pyvista. SKIPPED [1] mne/viz/_brain/tests/test_brain.py:166: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_brain.py:173: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_brain.py:180: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_brain.py:189: Requires testing dataset SKIPPED [2] mne/viz/_brain/tests/test_brain.py:569: Test skipped, requires pyvista. SKIPPED [1] mne/viz/_brain/tests/test_brain.py:601: Test skipped, requires pyvista. SKIPPED [2] mne/viz/_brain/tests/test_brain.py:635: Requires testing dataset SKIPPED [2] mne/viz/_brain/tests/test_brain.py:659: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_brain.py:738: Test skipped, requires pyvista. SKIPPED [1] mne/viz/_brain/tests/test_brain.py:772: Requires testing dataset SKIPPED [16] mne/viz/_brain/tests/test_brain.py:860: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_brain.py:1098: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_brain.py:1142: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_brain.py:1279: Test skipped, requires pyvista. SKIPPED [1] mne/viz/_brain/tests/test_brain.py:1386: Test skipped, requires pyvista. SKIPPED [1] mne/viz/_brain/tests/test_notebook.py:13: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_notebook.py:41: Requires testing dataset SKIPPED [1] mne/viz/_brain/tests/test_notebook.py:133: Requires testing dataset SKIPPED [1] mne/viz/backends/tests/test_abstract.py:109: requires pyvistaqt SKIPPED [1] mne/viz/backends/tests/test_abstract.py:117: Skipping Notebook test: No module named 'nbformat' SKIPPED [1] mne/viz/backends/tests/test_renderer.py:19: requires pyvistaqt SKIPPED [1] mne/viz/backends/tests/test_renderer.py:41: Test skipped, requires pyvista. SKIPPED [1] mne/viz/backends/tests/test_renderer.py:61: Test skipped, requires pyvista. SKIPPED [1] mne/viz/backends/tests/test_renderer.py:177: Test skipped, requires pyvista. SKIPPED [1] mne/viz/backends/tests/test_renderer.py:185: Test skipped, requires pyvista. SKIPPED [1] mne/viz/backends/tests/test_renderer.py:217: Test skipped, requires pyvista. SKIPPED [3] mne/viz/backends/tests/test_utils.py:53: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_3d.py:119: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:174: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:216: Requires testing dataset SKIPPED [4] mne/viz/tests/test_3d.py:265: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:318: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:719: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:745: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:875: Requires testing dataset SKIPPED [2] mne/viz/tests/test_3d.py:932: Requires testing dataset SKIPPED [3] mne/viz/tests/test_3d.py:964: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:999: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:1016: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:1174: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d.py:1202: Requires testing dataset SKIPPED [4] mne/viz/tests/test_3d_mpl.py:35: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d_mpl.py:111: Requires testing dataset SKIPPED [1] mne/viz/tests/test_3d_mpl.py:156: Requires testing dataset SKIPPED [1] mne/viz/tests/test_epochs.py:18: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_epochs.py:27: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [3] mne/viz/tests/test_epochs.py:75: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_epochs.py:83: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_epochs.py:97: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_epochs.py:111: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_epochs.py:160: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [4] mne/viz/tests/test_epochs.py:215: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_epochs.py:260: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_epochs.py:423: Requires testing dataset SKIPPED [2] mne/viz/tests/test_epochs.py:430: Requires testing dataset SKIPPED [1] mne/viz/tests/test_epochs.py:485: Requires testing dataset SKIPPED [1] mne/viz/tests/test_epochs.py:504: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_evoked.py:608: Requires testing dataset SKIPPED [1] mne/viz/tests/test_ica.py:264: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_ica.py:477: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_misc.py:139: Requires testing dataset SKIPPED [1] mne/viz/tests/test_misc.py:276: Requires testing dataset SKIPPED [1] mne/viz/tests/test_misc.py:299: Requires testing dataset SKIPPED [1] mne/viz/tests/test_misc.py:308: Requires testing dataset SKIPPED [1] mne/viz/tests/test_misc.py:329: Requires testing dataset SKIPPED [1] mne/viz/tests/test_proj.py:17: Requires testing dataset SKIPPED [1] mne/viz/tests/test_raw.py:271: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:298: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:400: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:438: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:473: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:501: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:543: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:638: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [2] mne/viz/tests/test_raw.py:656: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:691: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:704: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [2] mne/viz/tests/test_raw.py:713: Requires testing dataset SKIPPED [2] mne/viz/tests/test_raw.py:723: Requires testing dataset SKIPPED [1] mne/viz/tests/test_raw.py:731: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:741: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [3] mne/viz/tests/test_raw.py:784: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [4] mne/viz/tests/test_raw.py:841: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:873: The MPL backend does not support draggable annotations. SKIPPED [1] mne/viz/tests/test_raw.py:865: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [2] mne/viz/tests/test_raw.py:948: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [2] mne/viz/tests/test_raw.py:1158: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:1172: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [3] mne/viz/tests/test_raw.py:1178: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:1199: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:1211: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_raw.py:1223: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_scraper.py:12: Qt API None has version None but pyqtgraph needs >= 5.12! SKIPPED [1] mne/viz/tests/test_topomap.py:135: Requires testing dataset SKIPPED [1] mne/viz/tests/test_topomap.py:665: Requires testing dataset SKIPPED [1] mne/viz/tests/test_topomap.py:738: Requires testing dataset SKIPPED [1] mne/viz/tests/test_topomap.py:744: Requires testing dataset SKIPPED [1] mne/viz/tests/test_ui_events.py:35: Requires testing dataset ==== 11 failed, 2262 passed, 2176 skipped, 6 xfailed in 3790.51s (1:03:10) ===== make[1]: *** [debian/rules:23: override_dh_auto_test] Error 1 make[1]: Leaving directory '/build/reproducible-path/python-mne-1.8.0' make: *** [debian/rules:14: binary] Error 2 dpkg-buildpackage: error: debian/rules binary subprocess returned exit status 2 I: copying local configuration E: Failed autobuilding of package I: unmounting dev/ptmx filesystem I: unmounting dev/pts filesystem I: unmounting dev/shm filesystem I: unmounting proc filesystem I: unmounting sys filesystem I: cleaning the build env I: removing directory /srv/workspace/pbuilder/16195 and its subdirectories Fri Oct 11 14:31:45 UTC 2024 W: No second build log, what happened?