0s autopkgtest [14:02:34]: starting date and time: 2025-11-17 14:02:34+0000 0s autopkgtest [14:02:34]: git checkout: 4b346b80 nova: make wait_reboot return success even when a no-op 0s autopkgtest [14:02:34]: host juju-7f2275-prod-proposed-migration-environment-15; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.o4vlfn2v/out --timeout-copy=6000 --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:python3-defaults --apt-upgrade cnvkit --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=python3-defaults/3.13.7-2 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-15@bos03-arm64-19.secgroup --name adt-resolute-arm64-cnvkit-20251117-133314-juju-7f2275-prod-proposed-migration-environment-15-972eee6d-ce5c-4197-baf8-6b5deecbddea --image adt/ubuntu-resolute-arm64-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-15 --net-id=net_prod-proposed-migration -e TERM=linux --mirror=http://ftpmaster.internal/ubuntu/ 3s Creating nova instance adt-resolute-arm64-cnvkit-20251117-133314-juju-7f2275-prod-proposed-migration-environment-15-972eee6d-ce5c-4197-baf8-6b5deecbddea from image adt/ubuntu-resolute-arm64-server-20251117.img (UUID 1cd33fbb-18df-4c5a-b8f0-2dcb25269485)... 77s autopkgtest [14:03:51]: testbed dpkg architecture: arm64 78s autopkgtest [14:03:52]: testbed apt version: 3.1.11 78s autopkgtest [14:03:52]: @@@@@@@@@@@@@@@@@@@@ test bed setup 78s autopkgtest [14:03:52]: testbed release detected to be: None 79s autopkgtest [14:03:53]: updating testbed package index (apt update) 80s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [87.8 kB] 80s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 80s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 80s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 80s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/restricted Sources [9848 B] 80s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [22.9 kB] 80s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [868 kB] 81s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [81.1 kB] 81s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 Packages [149 kB] 81s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 c-n-f Metadata [3084 B] 81s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/restricted arm64 Packages [107 kB] 82s Get:12 http://ftpmaster.internal/ubuntu resolute-proposed/restricted arm64 c-n-f Metadata [324 B] 82s Get:13 http://ftpmaster.internal/ubuntu resolute-proposed/universe arm64 Packages [577 kB] 83s Get:14 http://ftpmaster.internal/ubuntu resolute-proposed/universe arm64 c-n-f Metadata [17.8 kB] 83s Get:15 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse arm64 Packages [12.5 kB] 83s Get:16 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse arm64 c-n-f Metadata [576 B] 85s Fetched 1938 kB in 3s (594 kB/s) 86s Reading package lists... 87s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease 87s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 87s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 88s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 89s Reading package lists... 89s Reading package lists... 90s Building dependency tree... 90s Reading state information... 90s Calculating upgrade... 91s The following packages will be upgraded: 91s libpython3-stdlib python3 python3-minimal usbutils 91s 4 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 91s Need to get 144 kB of archives. 91s After this operation, 0 B of additional disk space will be used. 91s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 python3-minimal arm64 3.13.7-2 [27.8 kB] 91s Get:2 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 python3 arm64 3.13.7-2 [23.9 kB] 91s Get:3 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 libpython3-stdlib arm64 3.13.7-2 [10.6 kB] 91s Get:4 http://ftpmaster.internal/ubuntu resolute/main arm64 usbutils arm64 1:019-1 [81.7 kB] 92s dpkg-preconfigure: unable to re-open stdin: No such file or directory 92s Fetched 144 kB in 1s (211 kB/s) 93s (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 ... 88137 files and directories currently installed.) 93s Preparing to unpack .../python3-minimal_3.13.7-2_arm64.deb ... 93s Unpacking python3-minimal (3.13.7-2) over (3.13.7-1) ... 93s Setting up python3-minimal (3.13.7-2) ... 94s (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 ... 88137 files and directories currently installed.) 94s Preparing to unpack .../python3_3.13.7-2_arm64.deb ... 94s running python pre-rtupdate hooks for python3.13... 94s Unpacking python3 (3.13.7-2) over (3.13.7-1) ... 94s Preparing to unpack .../libpython3-stdlib_3.13.7-2_arm64.deb ... 94s Unpacking libpython3-stdlib:arm64 (3.13.7-2) over (3.13.7-1) ... 95s Preparing to unpack .../usbutils_1%3a019-1_arm64.deb ... 95s Unpacking usbutils (1:019-1) over (1:018-2) ... 95s Setting up usbutils (1:019-1) ... 95s Setting up libpython3-stdlib:arm64 (3.13.7-2) ... 95s Setting up python3 (3.13.7-2) ... 95s running python rtupdate hooks for python3.13... 95s running python post-rtupdate hooks for python3.13... 95s Processing triggers for man-db (2.13.1-1) ... 98s autopkgtest [14:04:12]: upgrading testbed (apt dist-upgrade and autopurge) 98s Reading package lists... 99s Building dependency tree... 99s Reading state information... 99s Calculating upgrade... 100s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 100s Reading package lists... 100s Building dependency tree... 100s Reading state information... 101s Solving dependencies... 101s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 104s autopkgtest [14:04:18]: testbed running kernel: Linux 6.17.0-5-generic #5-Ubuntu SMP PREEMPT_DYNAMIC Mon Sep 22 09:50:31 UTC 2025 105s autopkgtest [14:04:19]: @@@@@@@@@@@@@@@@@@@@ apt-source cnvkit 113s Get:1 http://ftpmaster.internal/ubuntu resolute/universe cnvkit 0.9.12-1 (dsc) [2483 B] 113s Get:2 http://ftpmaster.internal/ubuntu resolute/universe cnvkit 0.9.12-1 (tar) [32.1 MB] 113s Get:3 http://ftpmaster.internal/ubuntu resolute/universe cnvkit 0.9.12-1 (diff) [20.8 kB] 113s gpgv: Signature made Thu Feb 6 14:25:04 2025 UTC 113s gpgv: using RSA key 724D609337113C710550D7473C26763F6C67E6E2 113s gpgv: issuer "crusoe@debian.org" 113s gpgv: Can't check signature: No public key 113s dpkg-source: warning: cannot verify inline signature for ./cnvkit_0.9.12-1.dsc: no acceptable signature found 115s autopkgtest [14:04:29]: testing package cnvkit version 0.9.12-1 116s autopkgtest [14:04:30]: build not needed 132s autopkgtest [14:04:46]: test run-unit-test: preparing testbed 132s Reading package lists... 133s Building dependency tree... 133s Reading state information... 133s Solving dependencies... 134s The following NEW packages will be installed: 134s blt cnvkit cython3 fontconfig fontconfig-config fonts-dejavu-core 134s fonts-dejavu-mono fonts-lyx fonts-urw-base35 libblas3 libcairo2 libdatrie1 134s libdeflate0 libfontconfig1 libfontenc1 libgfortran5 libgomp1 libgpgmepp6t64 134s libgraphite2-3 libharfbuzz0b libhts3t64 libhtscodecs2 libice6 libimagequant0 134s libjbig0 libjpeg-turbo8 libjpeg8 liblapack3 liblcms2-2 liblerc4 libopenjp2-7 134s libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpaper-utils 134s libpaper2 libpixman-1-0 libpoppler147 libqhull-r8.0 libraqm0 libsharpyuv0 134s libsm6 libtcl8.6 libthai-data libthai0 libtiff6 libtk8.6 libwebp7 134s libwebpdemux2 libwebpmux3 libxcb-render0 libxcb-shm0 libxft2 libxrender1 134s libxslt1.1 libxss1 libxt6t64 libzopfli1 poppler-utils python-matplotlib-data 134s python3-biopython python3-brotli python3-cairo python3-charset-normalizer 134s python3-contourpy python3-cycler python3-decorator python3-fonttools 134s python3-freetype python3-fs python3-joblib python3-kiwisolver python3-lxml 134s python3-lz4 python3-matplotlib python3-mpmath python3-networkx python3-numpy 134s python3-numpy-dev python3-pandas python3-pandas-lib python3-pil 134s python3-pil.imagetk python3-platformdirs python3-pomegranate python3-pyfaidx 134s python3-pysam python3-pytz python3-reportlab python3-rlpycairo python3-scipy 134s python3-sklearn python3-sklearn-lib python3-sympy python3-threadpoolctl 134s python3-tk python3-ufolib2 python3-unicodedata2 python3-zopfli python3.13-tk 134s python3.14-tk r-base-core r-bioc-biocgenerics r-bioc-dnacopy sgml-base 134s tk8.6-blt2.5 unicode-data unzip w3c-sgml-lib x11-common xdg-utils 134s xfonts-encodings xfonts-utils xml-core zip 134s 0 upgraded, 115 newly installed, 0 to remove and 0 not upgraded. 134s Need to get 185 MB of archives. 134s After this operation, 846 MB of additional disk space will be used. 134s Get:1 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-numpy-dev arm64 1:2.2.4+ds-1ubuntu1 [146 kB] 134s Get:2 http://ftpmaster.internal/ubuntu resolute/main arm64 libblas3 arm64 3.12.1-7 [181 kB] 134s Get:3 http://ftpmaster.internal/ubuntu resolute/main arm64 libgfortran5 arm64 15.2.0-7ubuntu1 [450 kB] 134s Get:4 http://ftpmaster.internal/ubuntu resolute/main arm64 liblapack3 arm64 3.12.1-7 [2300 kB] 135s Get:5 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-numpy arm64 1:2.2.4+ds-1ubuntu1 [3986 kB] 135s Get:6 http://ftpmaster.internal/ubuntu resolute/main arm64 libtcl8.6 arm64 8.6.17+dfsg-1 [1024 kB] 135s Get:7 http://ftpmaster.internal/ubuntu resolute/main arm64 fonts-dejavu-mono all 2.37-8 [502 kB] 135s Get:8 http://ftpmaster.internal/ubuntu resolute/main arm64 fonts-dejavu-core all 2.37-8 [835 kB] 135s Get:9 http://ftpmaster.internal/ubuntu resolute/main arm64 libfontenc1 arm64 1:1.1.8-1build1 [13.9 kB] 135s Get:10 http://ftpmaster.internal/ubuntu resolute/main arm64 x11-common all 1:7.7+24ubuntu1 [22.4 kB] 135s Get:11 http://ftpmaster.internal/ubuntu resolute/main arm64 xfonts-encodings all 1:1.0.5-0ubuntu2 [578 kB] 135s Get:12 http://ftpmaster.internal/ubuntu resolute/main arm64 xfonts-utils arm64 1:7.7+7 [95.6 kB] 135s Get:13 http://ftpmaster.internal/ubuntu resolute/main arm64 fonts-urw-base35 all 20200910-8 [11.0 MB] 136s Get:14 http://ftpmaster.internal/ubuntu resolute/main arm64 fontconfig-config arm64 2.15.0-2.3ubuntu1 [38.1 kB] 136s Get:15 http://ftpmaster.internal/ubuntu resolute/main arm64 libfontconfig1 arm64 2.15.0-2.3ubuntu1 [144 kB] 136s Get:16 http://ftpmaster.internal/ubuntu resolute/main arm64 libxrender1 arm64 1:0.9.12-1 [19.5 kB] 136s Get:17 http://ftpmaster.internal/ubuntu resolute/main arm64 libxft2 arm64 2.3.6-1build1 [44.1 kB] 136s Get:18 http://ftpmaster.internal/ubuntu resolute/main arm64 libxss1 arm64 1:1.2.3-1build3 [7244 B] 136s Get:19 http://ftpmaster.internal/ubuntu resolute/main arm64 libtk8.6 arm64 8.6.17-1 [811 kB] 136s Get:20 http://ftpmaster.internal/ubuntu resolute/main arm64 tk8.6-blt2.5 arm64 2.5.3+dfsg-8 [624 kB] 136s Get:21 http://ftpmaster.internal/ubuntu resolute/main arm64 blt arm64 2.5.3+dfsg-8 [4824 B] 136s Get:22 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-charset-normalizer arm64 3.4.3-1 [165 kB] 136s Get:23 http://ftpmaster.internal/ubuntu resolute/main arm64 python3.14-tk arm64 3.14.0-4 [107 kB] 136s Get:24 http://ftpmaster.internal/ubuntu resolute/main arm64 python3.13-tk arm64 3.13.9-1 [106 kB] 136s Get:25 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-tk arm64 3.13.9-1 [8946 B] 136s Get:26 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pil.imagetk arm64 11.3.0-1ubuntu2 [9582 B] 136s Get:27 http://ftpmaster.internal/ubuntu resolute/main arm64 libgomp1 arm64 15.2.0-7ubuntu1 [147 kB] 136s Get:28 http://ftpmaster.internal/ubuntu resolute/main arm64 libimagequant0 arm64 2.18.0-1build1 [37.1 kB] 136s Get:29 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg-turbo8 arm64 2.1.5-4ubuntu2 [165 kB] 136s Get:30 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg8 arm64 8c-2ubuntu11 [2148 B] 136s Get:31 http://ftpmaster.internal/ubuntu resolute/main arm64 liblcms2-2 arm64 2.17-1 [170 kB] 136s Get:32 http://ftpmaster.internal/ubuntu resolute/main arm64 libopenjp2-7 arm64 2.5.3-2.1 [179 kB] 136s Get:33 http://ftpmaster.internal/ubuntu resolute/main arm64 libgraphite2-3 arm64 1.3.14-2ubuntu1 [70.6 kB] 136s Get:34 http://ftpmaster.internal/ubuntu resolute/main arm64 libharfbuzz0b arm64 12.1.0-1 [523 kB] 136s Get:35 http://ftpmaster.internal/ubuntu resolute/main arm64 libraqm0 arm64 0.10.3-1 [15.0 kB] 136s Get:36 http://ftpmaster.internal/ubuntu resolute/main arm64 libdeflate0 arm64 1.23-2 [46.4 kB] 136s Get:37 http://ftpmaster.internal/ubuntu resolute/main arm64 libjbig0 arm64 2.1-6.1ubuntu2 [29.3 kB] 136s Get:38 http://ftpmaster.internal/ubuntu resolute/main arm64 liblerc4 arm64 4.0.0+ds-5ubuntu1 [167 kB] 136s Get:39 http://ftpmaster.internal/ubuntu resolute/main arm64 libsharpyuv0 arm64 1.5.0-0.1 [16.9 kB] 136s Get:40 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebp7 arm64 1.5.0-0.1 [194 kB] 136s Get:41 http://ftpmaster.internal/ubuntu resolute/main arm64 libtiff6 arm64 4.7.0-3ubuntu3 [196 kB] 136s Get:42 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebpdemux2 arm64 1.5.0-0.1 [12.5 kB] 136s Get:43 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebpmux3 arm64 1.5.0-0.1 [25.4 kB] 136s Get:44 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-pil arm64 11.3.0-1ubuntu2 [492 kB] 136s Get:45 http://ftpmaster.internal/ubuntu resolute/main arm64 libpixman-1-0 arm64 0.46.4-1 [204 kB] 136s Get:46 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-render0 arm64 1.17.0-2build1 [18.1 kB] 136s Get:47 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-shm0 arm64 1.17.0-2build1 [6234 B] 136s Get:48 http://ftpmaster.internal/ubuntu resolute/main arm64 libcairo2 arm64 1.18.4-1build1 [592 kB] 136s Get:49 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-cairo arm64 1.27.0-2build1 [141 kB] 136s Get:50 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-freetype all 2.5.1-2 [92.2 kB] 136s Get:51 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-rlpycairo all 0.3.0-4 [9332 B] 136s Get:52 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-reportlab all 4.4.4-2 [1147 kB] 136s Get:53 http://ftpmaster.internal/ubuntu resolute/main arm64 sgml-base all 1.31+nmu1 [11.0 kB] 136s Get:54 http://ftpmaster.internal/ubuntu resolute/main arm64 xml-core all 0.19 [20.3 kB] 136s Get:55 http://ftpmaster.internal/ubuntu resolute/universe arm64 w3c-sgml-lib all 1.3-3 [280 kB] 136s Get:56 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-biopython arm64 1.85+dfsg-4 [1710 kB] 136s Get:57 http://ftpmaster.internal/ubuntu resolute/universe arm64 fonts-lyx all 2.4.4-2 [171 kB] 136s Get:58 http://ftpmaster.internal/ubuntu resolute/universe arm64 python-matplotlib-data all 3.10.7+dfsg1-1 [2930 kB] 136s Get:59 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-contourpy arm64 1.3.1-2 [240 kB] 136s Get:60 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-cycler all 0.12.1-2 [9850 B] 136s Get:61 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-brotli arm64 1.1.0-2build6 [343 kB] 136s Get:62 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-platformdirs all 4.3.7-1 [16.9 kB] 136s Get:63 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-fs all 2.4.16-9ubuntu1 [91.5 kB] 136s Get:64 http://ftpmaster.internal/ubuntu resolute/main arm64 libxslt1.1 arm64 1.1.43-0.3 [172 kB] 136s Get:65 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-lxml arm64 6.0.2-1 [2155 kB] 136s Get:66 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-lz4 arm64 4.4.4+dfsg-3 [27.6 kB] 136s Get:67 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-decorator all 5.2.1-2 [28.1 kB] 136s Get:68 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-scipy arm64 1.15.3-1ubuntu1 [18.7 MB] 137s Get:69 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-mpmath all 1.3.0-2 [423 kB] 137s Get:70 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-sympy all 1.14.0-2 [4306 kB] 137s Get:71 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-ufolib2 all 0.17.1+dfsg1-1 [33.5 kB] 137s Get:72 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-unicodedata2 arm64 16.0.0+ds-1build1 [398 kB] 137s Get:73 http://ftpmaster.internal/ubuntu resolute/universe arm64 libzopfli1 arm64 1.0.3-3 [108 kB] 137s Get:74 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-zopfli arm64 0.4.0-1 [10.8 kB] 137s Get:75 http://ftpmaster.internal/ubuntu resolute/universe arm64 unicode-data all 16.0.0-1 [9513 kB] 137s Get:76 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-fonttools arm64 4.57.0-2build1 [1648 kB] 137s Get:77 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-kiwisolver arm64 1.4.10~rc0-1 [60.1 kB] 137s Get:78 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqhull-r8.0 arm64 2020.2-7 [190 kB] 137s Get:79 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-matplotlib arm64 3.10.7+dfsg1-1 [17.1 MB] 138s Get:80 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-pytz all 2025.2-4 [32.3 kB] 138s Get:81 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pandas-lib arm64 2.3.3+dfsg-1ubuntu1 [6979 kB] 138s Get:82 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pandas all 2.3.3+dfsg-1ubuntu1 [2948 kB] 138s Get:83 http://ftpmaster.internal/ubuntu resolute/universe arm64 cython3 arm64 3.1.6+dfsg-1ubuntu1 [3180 kB] 138s Get:84 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-joblib all 1.4.2-4 [205 kB] 138s Get:85 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-networkx all 3.2.1-4ubuntu1 [11.5 MB] 138s Get:86 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pomegranate arm64 0.15.0-2 [4260 kB] 138s Get:87 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pyfaidx all 0.8.1.3-2 [29.7 kB] 138s Get:88 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhtscodecs2 arm64 1.6.1-2 [82.7 kB] 138s Get:89 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhts3t64 arm64 1.22.1+ds2-1 [442 kB] 138s Get:90 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pysam arm64 0.23.3+ds-2 [4348 kB] 139s Get:91 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-sklearn-lib arm64 1.7.2+dfsg-3ubuntu1 [6003 kB] 139s Get:92 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-threadpoolctl all 3.1.0-1 [21.3 kB] 139s Get:93 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-sklearn all 1.7.2+dfsg-3ubuntu1 [2616 kB] 139s Get:94 http://ftpmaster.internal/ubuntu resolute/main arm64 zip arm64 3.0-15ubuntu2 [175 kB] 139s Get:95 http://ftpmaster.internal/ubuntu resolute/main arm64 unzip arm64 6.0-28ubuntu7 [176 kB] 139s Get:96 http://ftpmaster.internal/ubuntu resolute/main arm64 libpaper2 arm64 2.2.5-0.3 [17.3 kB] 139s Get:97 http://ftpmaster.internal/ubuntu resolute/main arm64 libpaper-utils arm64 2.2.5-0.3 [15.4 kB] 139s Get:98 http://ftpmaster.internal/ubuntu resolute/main arm64 xdg-utils all 1.2.1-2ubuntu1 [66.0 kB] 139s Get:99 http://ftpmaster.internal/ubuntu resolute/main arm64 fontconfig arm64 2.15.0-2.3ubuntu1 [191 kB] 139s Get:100 http://ftpmaster.internal/ubuntu resolute/main arm64 libthai-data all 0.1.29-2build1 [158 kB] 139s Get:101 http://ftpmaster.internal/ubuntu resolute/main arm64 libdatrie1 arm64 0.2.13-4 [19.1 kB] 139s Get:102 http://ftpmaster.internal/ubuntu resolute/main arm64 libthai0 arm64 0.1.29-2build1 [18.2 kB] 139s Get:103 http://ftpmaster.internal/ubuntu resolute/main arm64 libpango-1.0-0 arm64 1.56.3-2 [237 kB] 139s Get:104 http://ftpmaster.internal/ubuntu resolute/main arm64 libpangoft2-1.0-0 arm64 1.56.3-2 [50.2 kB] 139s Get:105 http://ftpmaster.internal/ubuntu resolute/main arm64 libpangocairo-1.0-0 arm64 1.56.3-2 [27.7 kB] 139s Get:106 http://ftpmaster.internal/ubuntu resolute/main arm64 libice6 arm64 2:1.1.1-1 [42.3 kB] 139s Get:107 http://ftpmaster.internal/ubuntu resolute/main arm64 libsm6 arm64 2:1.2.6-1 [16.6 kB] 139s Get:108 http://ftpmaster.internal/ubuntu resolute/main arm64 libxt6t64 arm64 1:1.2.1-1.3 [168 kB] 139s Get:109 http://ftpmaster.internal/ubuntu resolute/universe arm64 r-base-core arm64 4.5.2-1 [28.6 MB] 140s Get:110 http://ftpmaster.internal/ubuntu resolute/universe arm64 r-bioc-biocgenerics all 0.52.0-2 [624 kB] 140s Get:111 http://ftpmaster.internal/ubuntu resolute/universe arm64 r-bioc-dnacopy arm64 1.80.0-2 [497 kB] 140s Get:112 http://ftpmaster.internal/ubuntu resolute/universe arm64 cnvkit all 0.9.12-1 [20.6 MB] 141s Get:113 http://ftpmaster.internal/ubuntu resolute/main arm64 libgpgmepp6t64 arm64 1.24.2-3ubuntu2 [117 kB] 141s Get:114 http://ftpmaster.internal/ubuntu resolute/main arm64 libpoppler147 arm64 25.03.0-11.1 [1149 kB] 141s Get:115 http://ftpmaster.internal/ubuntu resolute/main arm64 poppler-utils arm64 25.03.0-11.1 [213 kB] 142s Preconfiguring packages ... 142s Fetched 185 MB in 7s (25.2 MB/s) 142s Selecting previously unselected package python3-numpy-dev:arm64. 142s (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 ... 88137 files and directories currently installed.) 142s Preparing to unpack .../000-python3-numpy-dev_1%3a2.2.4+ds-1ubuntu1_arm64.deb ... 142s Unpacking python3-numpy-dev:arm64 (1:2.2.4+ds-1ubuntu1) ... 142s Selecting previously unselected package libblas3:arm64. 142s Preparing to unpack .../001-libblas3_3.12.1-7_arm64.deb ... 142s Unpacking libblas3:arm64 (3.12.1-7) ... 142s Selecting previously unselected package libgfortran5:arm64. 142s Preparing to unpack .../002-libgfortran5_15.2.0-7ubuntu1_arm64.deb ... 142s Unpacking libgfortran5:arm64 (15.2.0-7ubuntu1) ... 142s Selecting previously unselected package liblapack3:arm64. 142s Preparing to unpack .../003-liblapack3_3.12.1-7_arm64.deb ... 142s Unpacking liblapack3:arm64 (3.12.1-7) ... 142s Selecting previously unselected package python3-numpy. 142s Preparing to unpack .../004-python3-numpy_1%3a2.2.4+ds-1ubuntu1_arm64.deb ... 142s Unpacking python3-numpy (1:2.2.4+ds-1ubuntu1) ... 143s Selecting previously unselected package libtcl8.6:arm64. 143s Preparing to unpack .../005-libtcl8.6_8.6.17+dfsg-1_arm64.deb ... 143s Unpacking libtcl8.6:arm64 (8.6.17+dfsg-1) ... 143s Selecting previously unselected package fonts-dejavu-mono. 143s Preparing to unpack .../006-fonts-dejavu-mono_2.37-8_all.deb ... 143s Unpacking fonts-dejavu-mono (2.37-8) ... 143s Selecting previously unselected package fonts-dejavu-core. 143s Preparing to unpack .../007-fonts-dejavu-core_2.37-8_all.deb ... 143s Unpacking fonts-dejavu-core (2.37-8) ... 143s Selecting previously unselected package libfontenc1:arm64. 143s Preparing to unpack .../008-libfontenc1_1%3a1.1.8-1build1_arm64.deb ... 143s Unpacking libfontenc1:arm64 (1:1.1.8-1build1) ... 143s Selecting previously unselected package x11-common. 143s Preparing to unpack .../009-x11-common_1%3a7.7+24ubuntu1_all.deb ... 143s Unpacking x11-common (1:7.7+24ubuntu1) ... 143s Selecting previously unselected package xfonts-encodings. 143s Preparing to unpack .../010-xfonts-encodings_1%3a1.0.5-0ubuntu2_all.deb ... 143s Unpacking xfonts-encodings (1:1.0.5-0ubuntu2) ... 143s Selecting previously unselected package xfonts-utils. 143s Preparing to unpack .../011-xfonts-utils_1%3a7.7+7_arm64.deb ... 143s Unpacking xfonts-utils (1:7.7+7) ... 143s Selecting previously unselected package fonts-urw-base35. 143s Preparing to unpack .../012-fonts-urw-base35_20200910-8_all.deb ... 143s Unpacking fonts-urw-base35 (20200910-8) ... 144s Selecting previously unselected package fontconfig-config. 144s Preparing to unpack .../013-fontconfig-config_2.15.0-2.3ubuntu1_arm64.deb ... 144s Unpacking fontconfig-config (2.15.0-2.3ubuntu1) ... 144s Selecting previously unselected package libfontconfig1:arm64. 144s Preparing to unpack .../014-libfontconfig1_2.15.0-2.3ubuntu1_arm64.deb ... 144s Unpacking libfontconfig1:arm64 (2.15.0-2.3ubuntu1) ... 144s Selecting previously unselected package libxrender1:arm64. 144s Preparing to unpack .../015-libxrender1_1%3a0.9.12-1_arm64.deb ... 144s Unpacking libxrender1:arm64 (1:0.9.12-1) ... 144s Selecting previously unselected package libxft2:arm64. 144s Preparing to unpack .../016-libxft2_2.3.6-1build1_arm64.deb ... 144s Unpacking libxft2:arm64 (2.3.6-1build1) ... 144s Selecting previously unselected package libxss1:arm64. 144s Preparing to unpack .../017-libxss1_1%3a1.2.3-1build3_arm64.deb ... 144s Unpacking libxss1:arm64 (1:1.2.3-1build3) ... 144s Selecting previously unselected package libtk8.6:arm64. 145s Preparing to unpack .../018-libtk8.6_8.6.17-1_arm64.deb ... 145s Unpacking libtk8.6:arm64 (8.6.17-1) ... 145s Selecting previously unselected package tk8.6-blt2.5. 145s Preparing to unpack .../019-tk8.6-blt2.5_2.5.3+dfsg-8_arm64.deb ... 145s Unpacking tk8.6-blt2.5 (2.5.3+dfsg-8) ... 145s Selecting previously unselected package blt. 145s Preparing to unpack .../020-blt_2.5.3+dfsg-8_arm64.deb ... 145s Unpacking blt (2.5.3+dfsg-8) ... 145s Selecting previously unselected package python3-charset-normalizer. 145s Preparing to unpack .../021-python3-charset-normalizer_3.4.3-1_arm64.deb ... 145s Unpacking python3-charset-normalizer (3.4.3-1) ... 145s Selecting previously unselected package python3.14-tk. 145s Preparing to unpack .../022-python3.14-tk_3.14.0-4_arm64.deb ... 145s Unpacking python3.14-tk (3.14.0-4) ... 145s Selecting previously unselected package python3.13-tk. 145s Preparing to unpack .../023-python3.13-tk_3.13.9-1_arm64.deb ... 145s Unpacking python3.13-tk (3.13.9-1) ... 145s Selecting previously unselected package python3-tk:arm64. 145s Preparing to unpack .../024-python3-tk_3.13.9-1_arm64.deb ... 145s Unpacking python3-tk:arm64 (3.13.9-1) ... 145s Selecting previously unselected package python3-pil.imagetk:arm64. 145s Preparing to unpack .../025-python3-pil.imagetk_11.3.0-1ubuntu2_arm64.deb ... 145s Unpacking python3-pil.imagetk:arm64 (11.3.0-1ubuntu2) ... 145s Selecting previously unselected package libgomp1:arm64. 145s Preparing to unpack .../026-libgomp1_15.2.0-7ubuntu1_arm64.deb ... 145s Unpacking libgomp1:arm64 (15.2.0-7ubuntu1) ... 145s Selecting previously unselected package libimagequant0:arm64. 145s Preparing to unpack .../027-libimagequant0_2.18.0-1build1_arm64.deb ... 145s Unpacking libimagequant0:arm64 (2.18.0-1build1) ... 145s Selecting previously unselected package libjpeg-turbo8:arm64. 145s Preparing to unpack .../028-libjpeg-turbo8_2.1.5-4ubuntu2_arm64.deb ... 145s Unpacking libjpeg-turbo8:arm64 (2.1.5-4ubuntu2) ... 145s Selecting previously unselected package libjpeg8:arm64. 145s Preparing to unpack .../029-libjpeg8_8c-2ubuntu11_arm64.deb ... 145s Unpacking libjpeg8:arm64 (8c-2ubuntu11) ... 145s Selecting previously unselected package liblcms2-2:arm64. 145s Preparing to unpack .../030-liblcms2-2_2.17-1_arm64.deb ... 145s Unpacking liblcms2-2:arm64 (2.17-1) ... 145s Selecting previously unselected package libopenjp2-7:arm64. 145s Preparing to unpack .../031-libopenjp2-7_2.5.3-2.1_arm64.deb ... 145s Unpacking libopenjp2-7:arm64 (2.5.3-2.1) ... 146s Selecting previously unselected package libgraphite2-3:arm64. 146s Preparing to unpack .../032-libgraphite2-3_1.3.14-2ubuntu1_arm64.deb ... 146s Unpacking libgraphite2-3:arm64 (1.3.14-2ubuntu1) ... 146s Selecting previously unselected package libharfbuzz0b:arm64. 146s Preparing to unpack .../033-libharfbuzz0b_12.1.0-1_arm64.deb ... 146s Unpacking libharfbuzz0b:arm64 (12.1.0-1) ... 146s Selecting previously unselected package libraqm0:arm64. 146s Preparing to unpack .../034-libraqm0_0.10.3-1_arm64.deb ... 146s Unpacking libraqm0:arm64 (0.10.3-1) ... 146s Selecting previously unselected package libdeflate0:arm64. 146s Preparing to unpack .../035-libdeflate0_1.23-2_arm64.deb ... 146s Unpacking libdeflate0:arm64 (1.23-2) ... 146s Selecting previously unselected package libjbig0:arm64. 146s Preparing to unpack .../036-libjbig0_2.1-6.1ubuntu2_arm64.deb ... 146s Unpacking libjbig0:arm64 (2.1-6.1ubuntu2) ... 146s Selecting previously unselected package liblerc4:arm64. 146s Preparing to unpack .../037-liblerc4_4.0.0+ds-5ubuntu1_arm64.deb ... 146s Unpacking liblerc4:arm64 (4.0.0+ds-5ubuntu1) ... 146s Selecting previously unselected package libsharpyuv0:arm64. 146s Preparing to unpack .../038-libsharpyuv0_1.5.0-0.1_arm64.deb ... 146s Unpacking libsharpyuv0:arm64 (1.5.0-0.1) ... 146s Selecting previously unselected package libwebp7:arm64. 146s Preparing to unpack .../039-libwebp7_1.5.0-0.1_arm64.deb ... 146s Unpacking libwebp7:arm64 (1.5.0-0.1) ... 146s Selecting previously unselected package libtiff6:arm64. 146s Preparing to unpack .../040-libtiff6_4.7.0-3ubuntu3_arm64.deb ... 146s Unpacking libtiff6:arm64 (4.7.0-3ubuntu3) ... 146s Selecting previously unselected package libwebpdemux2:arm64. 146s Preparing to unpack .../041-libwebpdemux2_1.5.0-0.1_arm64.deb ... 146s Unpacking libwebpdemux2:arm64 (1.5.0-0.1) ... 146s Selecting previously unselected package libwebpmux3:arm64. 146s Preparing to unpack .../042-libwebpmux3_1.5.0-0.1_arm64.deb ... 146s Unpacking libwebpmux3:arm64 (1.5.0-0.1) ... 146s Selecting previously unselected package python3-pil:arm64. 146s Preparing to unpack .../043-python3-pil_11.3.0-1ubuntu2_arm64.deb ... 146s Unpacking python3-pil:arm64 (11.3.0-1ubuntu2) ... 146s Selecting previously unselected package libpixman-1-0:arm64. 146s Preparing to unpack .../044-libpixman-1-0_0.46.4-1_arm64.deb ... 146s Unpacking libpixman-1-0:arm64 (0.46.4-1) ... 146s Selecting previously unselected package libxcb-render0:arm64. 146s Preparing to unpack .../045-libxcb-render0_1.17.0-2build1_arm64.deb ... 146s Unpacking libxcb-render0:arm64 (1.17.0-2build1) ... 146s Selecting previously unselected package libxcb-shm0:arm64. 147s Preparing to unpack .../046-libxcb-shm0_1.17.0-2build1_arm64.deb ... 147s Unpacking libxcb-shm0:arm64 (1.17.0-2build1) ... 147s Selecting previously unselected package libcairo2:arm64. 147s Preparing to unpack .../047-libcairo2_1.18.4-1build1_arm64.deb ... 147s Unpacking libcairo2:arm64 (1.18.4-1build1) ... 147s Selecting previously unselected package python3-cairo. 147s Preparing to unpack .../048-python3-cairo_1.27.0-2build1_arm64.deb ... 147s Unpacking python3-cairo (1.27.0-2build1) ... 147s Selecting previously unselected package python3-freetype. 147s Preparing to unpack .../049-python3-freetype_2.5.1-2_all.deb ... 147s Unpacking python3-freetype (2.5.1-2) ... 147s Selecting previously unselected package python3-rlpycairo. 147s Preparing to unpack .../050-python3-rlpycairo_0.3.0-4_all.deb ... 147s Unpacking python3-rlpycairo (0.3.0-4) ... 147s Selecting previously unselected package python3-reportlab. 147s Preparing to unpack .../051-python3-reportlab_4.4.4-2_all.deb ... 147s Unpacking python3-reportlab (4.4.4-2) ... 147s Selecting previously unselected package sgml-base. 147s Preparing to unpack .../052-sgml-base_1.31+nmu1_all.deb ... 147s Unpacking sgml-base (1.31+nmu1) ... 147s Selecting previously unselected package xml-core. 147s Preparing to unpack .../053-xml-core_0.19_all.deb ... 147s Unpacking xml-core (0.19) ... 147s Selecting previously unselected package w3c-sgml-lib. 147s Preparing to unpack .../054-w3c-sgml-lib_1.3-3_all.deb ... 147s Unpacking w3c-sgml-lib (1.3-3) ... 147s Selecting previously unselected package python3-biopython. 147s Preparing to unpack .../055-python3-biopython_1.85+dfsg-4_arm64.deb ... 147s Unpacking python3-biopython (1.85+dfsg-4) ... 148s Selecting previously unselected package fonts-lyx. 148s Preparing to unpack .../056-fonts-lyx_2.4.4-2_all.deb ... 148s Unpacking fonts-lyx (2.4.4-2) ... 148s Selecting previously unselected package python-matplotlib-data. 148s Preparing to unpack .../057-python-matplotlib-data_3.10.7+dfsg1-1_all.deb ... 148s Unpacking python-matplotlib-data (3.10.7+dfsg1-1) ... 148s Selecting previously unselected package python3-contourpy. 148s Preparing to unpack .../058-python3-contourpy_1.3.1-2_arm64.deb ... 148s Unpacking python3-contourpy (1.3.1-2) ... 148s Selecting previously unselected package python3-cycler. 148s Preparing to unpack .../059-python3-cycler_0.12.1-2_all.deb ... 148s Unpacking python3-cycler (0.12.1-2) ... 148s Selecting previously unselected package python3-brotli. 148s Preparing to unpack .../060-python3-brotli_1.1.0-2build6_arm64.deb ... 148s Unpacking python3-brotli (1.1.0-2build6) ... 148s Selecting previously unselected package python3-platformdirs. 148s Preparing to unpack .../061-python3-platformdirs_4.3.7-1_all.deb ... 148s Unpacking python3-platformdirs (4.3.7-1) ... 148s Selecting previously unselected package python3-fs. 148s Preparing to unpack .../062-python3-fs_2.4.16-9ubuntu1_all.deb ... 148s Unpacking python3-fs (2.4.16-9ubuntu1) ... 148s Selecting previously unselected package libxslt1.1:arm64. 148s Preparing to unpack .../063-libxslt1.1_1.1.43-0.3_arm64.deb ... 148s Unpacking libxslt1.1:arm64 (1.1.43-0.3) ... 148s Selecting previously unselected package python3-lxml:arm64. 148s Preparing to unpack .../064-python3-lxml_6.0.2-1_arm64.deb ... 148s Unpacking python3-lxml:arm64 (6.0.2-1) ... 148s Selecting previously unselected package python3-lz4. 149s Preparing to unpack .../065-python3-lz4_4.4.4+dfsg-3_arm64.deb ... 149s Unpacking python3-lz4 (4.4.4+dfsg-3) ... 149s Selecting previously unselected package python3-decorator. 149s Preparing to unpack .../066-python3-decorator_5.2.1-2_all.deb ... 149s Unpacking python3-decorator (5.2.1-2) ... 149s Selecting previously unselected package python3-scipy. 149s Preparing to unpack .../067-python3-scipy_1.15.3-1ubuntu1_arm64.deb ... 149s Unpacking python3-scipy (1.15.3-1ubuntu1) ... 150s Selecting previously unselected package python3-mpmath. 150s Preparing to unpack .../068-python3-mpmath_1.3.0-2_all.deb ... 150s Unpacking python3-mpmath (1.3.0-2) ... 150s Selecting previously unselected package python3-sympy. 150s Preparing to unpack .../069-python3-sympy_1.14.0-2_all.deb ... 150s Unpacking python3-sympy (1.14.0-2) ... 150s Selecting previously unselected package python3-ufolib2. 150s Preparing to unpack .../070-python3-ufolib2_0.17.1+dfsg1-1_all.deb ... 150s Unpacking python3-ufolib2 (0.17.1+dfsg1-1) ... 150s Selecting previously unselected package python3-unicodedata2. 150s Preparing to unpack .../071-python3-unicodedata2_16.0.0+ds-1build1_arm64.deb ... 150s Unpacking python3-unicodedata2 (16.0.0+ds-1build1) ... 150s Selecting previously unselected package libzopfli1. 150s Preparing to unpack .../072-libzopfli1_1.0.3-3_arm64.deb ... 150s Unpacking libzopfli1 (1.0.3-3) ... 150s Selecting previously unselected package python3-zopfli. 151s Preparing to unpack .../073-python3-zopfli_0.4.0-1_arm64.deb ... 151s Unpacking python3-zopfli (0.4.0-1) ... 151s Selecting previously unselected package unicode-data. 151s Preparing to unpack .../074-unicode-data_16.0.0-1_all.deb ... 151s Unpacking unicode-data (16.0.0-1) ... 151s Selecting previously unselected package python3-fonttools. 151s Preparing to unpack .../075-python3-fonttools_4.57.0-2build1_arm64.deb ... 151s Unpacking python3-fonttools (4.57.0-2build1) ... 151s Selecting previously unselected package python3-kiwisolver. 151s Preparing to unpack .../076-python3-kiwisolver_1.4.10~rc0-1_arm64.deb ... 151s Unpacking python3-kiwisolver (1.4.10~rc0-1) ... 151s Selecting previously unselected package libqhull-r8.0:arm64. 151s Preparing to unpack .../077-libqhull-r8.0_2020.2-7_arm64.deb ... 151s Unpacking libqhull-r8.0:arm64 (2020.2-7) ... 151s Selecting previously unselected package python3-matplotlib. 151s Preparing to unpack .../078-python3-matplotlib_3.10.7+dfsg1-1_arm64.deb ... 151s Unpacking python3-matplotlib (3.10.7+dfsg1-1) ... 152s Selecting previously unselected package python3-pytz. 152s Preparing to unpack .../079-python3-pytz_2025.2-4_all.deb ... 152s Unpacking python3-pytz (2025.2-4) ... 152s Selecting previously unselected package python3-pandas-lib:arm64. 152s Preparing to unpack .../080-python3-pandas-lib_2.3.3+dfsg-1ubuntu1_arm64.deb ... 152s Unpacking python3-pandas-lib:arm64 (2.3.3+dfsg-1ubuntu1) ... 153s Selecting previously unselected package python3-pandas. 153s Preparing to unpack .../081-python3-pandas_2.3.3+dfsg-1ubuntu1_all.deb ... 153s Unpacking python3-pandas (2.3.3+dfsg-1ubuntu1) ... 153s Selecting previously unselected package cython3. 153s Preparing to unpack .../082-cython3_3.1.6+dfsg-1ubuntu1_arm64.deb ... 153s Unpacking cython3 (3.1.6+dfsg-1ubuntu1) ... 153s Selecting previously unselected package python3-joblib. 153s Preparing to unpack .../083-python3-joblib_1.4.2-4_all.deb ... 153s Unpacking python3-joblib (1.4.2-4) ... 153s Selecting previously unselected package python3-networkx. 153s Preparing to unpack .../084-python3-networkx_3.2.1-4ubuntu1_all.deb ... 153s Unpacking python3-networkx (3.2.1-4ubuntu1) ... 154s Selecting previously unselected package python3-pomegranate. 154s Preparing to unpack .../085-python3-pomegranate_0.15.0-2_arm64.deb ... 154s Unpacking python3-pomegranate (0.15.0-2) ... 155s Selecting previously unselected package python3-pyfaidx. 155s Preparing to unpack .../086-python3-pyfaidx_0.8.1.3-2_all.deb ... 155s Unpacking python3-pyfaidx (0.8.1.3-2) ... 155s Selecting previously unselected package libhtscodecs2:arm64. 155s Preparing to unpack .../087-libhtscodecs2_1.6.1-2_arm64.deb ... 155s Unpacking libhtscodecs2:arm64 (1.6.1-2) ... 155s Selecting previously unselected package libhts3t64:arm64. 155s Preparing to unpack .../088-libhts3t64_1.22.1+ds2-1_arm64.deb ... 155s Unpacking libhts3t64:arm64 (1.22.1+ds2-1) ... 155s Selecting previously unselected package python3-pysam. 155s Preparing to unpack .../089-python3-pysam_0.23.3+ds-2_arm64.deb ... 155s Unpacking python3-pysam (0.23.3+ds-2) ... 156s Selecting previously unselected package python3-sklearn-lib:arm64. 156s Preparing to unpack .../090-python3-sklearn-lib_1.7.2+dfsg-3ubuntu1_arm64.deb ... 156s Unpacking python3-sklearn-lib:arm64 (1.7.2+dfsg-3ubuntu1) ... 156s Selecting previously unselected package python3-threadpoolctl. 156s Preparing to unpack .../091-python3-threadpoolctl_3.1.0-1_all.deb ... 156s Unpacking python3-threadpoolctl (3.1.0-1) ... 156s Selecting previously unselected package python3-sklearn. 156s Preparing to unpack .../092-python3-sklearn_1.7.2+dfsg-3ubuntu1_all.deb ... 156s Unpacking python3-sklearn (1.7.2+dfsg-3ubuntu1) ... 156s Selecting previously unselected package zip. 156s Preparing to unpack .../093-zip_3.0-15ubuntu2_arm64.deb ... 156s Unpacking zip (3.0-15ubuntu2) ... 156s Selecting previously unselected package unzip. 156s Preparing to unpack .../094-unzip_6.0-28ubuntu7_arm64.deb ... 156s Unpacking unzip (6.0-28ubuntu7) ... 156s Selecting previously unselected package libpaper2:arm64. 156s Preparing to unpack .../095-libpaper2_2.2.5-0.3_arm64.deb ... 156s Unpacking libpaper2:arm64 (2.2.5-0.3) ... 157s Selecting previously unselected package libpaper-utils. 157s Preparing to unpack .../096-libpaper-utils_2.2.5-0.3_arm64.deb ... 157s Unpacking libpaper-utils (2.2.5-0.3) ... 157s Selecting previously unselected package xdg-utils. 157s Preparing to unpack .../097-xdg-utils_1.2.1-2ubuntu1_all.deb ... 157s Unpacking xdg-utils (1.2.1-2ubuntu1) ... 157s Selecting previously unselected package fontconfig. 157s Preparing to unpack .../098-fontconfig_2.15.0-2.3ubuntu1_arm64.deb ... 157s Unpacking fontconfig (2.15.0-2.3ubuntu1) ... 157s Selecting previously unselected package 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python3-numpy (1:2.2.4+ds-1ubuntu1) ... 186s Setting up python3-lxml:arm64 (6.0.2-1) ... 186s Setting up libtiff6:arm64 (4.7.0-3ubuntu3) ... 186s Setting up xml-core (0.19) ... 187s Setting up python3-contourpy (1.3.1-2) ... 187s Setting up libfontconfig1:arm64 (2.15.0-2.3ubuntu1) ... 187s Setting up libsm6:arm64 (2:1.2.6-1) ... 187s Setting up fontconfig (2.15.0-2.3ubuntu1) ... 190s Regenerating fonts cache... done. 190s Setting up libxft2:arm64 (2.3.6-1build1) ... 190s Setting up python3-scipy (1.15.3-1ubuntu1) ... 197s Setting up libpoppler147:arm64 (25.03.0-11.1) ... 197s Setting up python3-pomegranate (0.15.0-2) ... 197s Setting up libtk8.6:arm64 (8.6.17-1) ... 197s Setting up python3-pandas-lib:arm64 (2.3.3+dfsg-1ubuntu1) ... 197s Setting up libpango-1.0-0:arm64 (1.56.3-2) ... 197s Setting up python3-sklearn-lib:arm64 (1.7.2+dfsg-3ubuntu1) ... 197s Setting up fonts-urw-base35 (20200910-8) ... 197s Setting up libcairo2:arm64 (1.18.4-1build1) ... 197s Setting up python3.13-tk (3.13.9-1) ... 197s Setting up python3-pil:arm64 (11.3.0-1ubuntu2) ... 198s Setting up python3-pandas (2.3.3+dfsg-1ubuntu1) ... 208s Setting up libxt6t64:arm64 (1:1.2.1-1.3) ... 208s Setting up python3-sklearn (1.7.2+dfsg-3ubuntu1) ... 212s Setting up poppler-utils (25.03.0-11.1) ... 212s Setting up libpangoft2-1.0-0:arm64 (1.56.3-2) ... 212s Setting up libpangocairo-1.0-0:arm64 (1.56.3-2) ... 212s Setting up tk8.6-blt2.5 (2.5.3+dfsg-8) ... 212s Setting up python3.14-tk (3.14.0-4) ... 212s Setting up python3-cairo (1.27.0-2build1) ... 212s Setting up blt (2.5.3+dfsg-8) ... 212s Setting up python3-tk:arm64 (3.13.9-1) ... 212s Setting up python3-pil.imagetk:arm64 (11.3.0-1ubuntu2) ... 212s Setting up python3-rlpycairo (0.3.0-4) ... 212s Setting up r-base-core (4.5.2-1) ... 213s Creating config file /etc/R/Renviron with new version 213s Setting up python3-reportlab (4.4.4-2) ... 215s Setting up r-bioc-biocgenerics (0.52.0-2) ... 215s Setting up r-bioc-dnacopy (1.80.0-2) ... 215s Setting up python3-fonttools (4.57.0-2build1) ... 216s Setting up python3-ufolib2 (0.17.1+dfsg1-1) ... 217s Setting up python3-matplotlib (3.10.7+dfsg1-1) ... 220s Processing triggers for man-db (2.13.1-1) ... 221s Processing triggers for install-info (7.2-5) ... 221s Processing triggers for libc-bin (2.42-2ubuntu2) ... 222s Processing triggers for sgml-base (1.31+nmu1) ... 222s Setting up w3c-sgml-lib (1.3-3) ... 275s Setting up python3-biopython (1.85+dfsg-4) ... 278s Setting up cnvkit (0.9.12-1) ... 279s autopkgtest [14:07:13]: test run-unit-test: [----------------------- 280s cnvkit.py batch -n -f formats/chrM-Y-trunc.hg19.fa -m wgs formats/na12878-chrM-Y-trunc.bam -d build 284s CNVkit 0.9.12 284s WGS protocol: recommend '--annotate' option (e.g. refFlat.txt) to help locate genes in output files. 284s chrM: Scanning for accessible regions 284s Accessible region chrM:0-121 (size 121) 284s Accessible region chrM:122-1271 (size 1149) 284s Accessible region chrM:1274-1288 (size 14) 284s Accessible region chrM:1289-1547 (size 258) 284s Accessible region chrM:1553-16571 (size 15018) 284s chrY: Scanning for accessible regions 284s Accessible region chrY:500-14900 (size 14400) 284s Accessible region chrY:15600-22966 (size 7366) 284s chrY: Joining over small gaps 285s Joining chrY 500-14900 and 15600-22966 (gap size 700) 285s Wrote chrM-Y-trunc.hg19.bed with 1 regions 285s Detected file format: bed 285s Splitting large targets 285s Created directory build 285s Wrote build/chrM-Y-trunc.hg19.target.bed with 4 regions 285s Wrote build/chrM-Y-trunc.hg19.antitarget.bed with 0 regions 285s Building a flat reference... 285s Detected file format: bed 285s Calculating GC and RepeatMasker content in formats/chrM-Y-trunc.hg19.fa ... 285s Extracting sequences from chromosome chrY 285s Wrote build/reference.cnn with 4 regions 285s Running 1 samples in serial 285s Running the CNVkit pipeline on formats/na12878-chrM-Y-trunc.bam ... 285s Indexing BAM file formats/na12878-chrM-Y-trunc.bam 285s Processing reads in na12878-chrM-Y-trunc.bam 285s Time: 0.018 seconds (2010 reads/sec, 217 bins/sec) 285s Summary: #bins=4, #reads=37, mean=9.2500, min=0.0, max=21.0 285s Percent reads in regions: 0.063 (of 58636 mapped) 285s Wrote build/na12878-chrM-Y-trunc.targetcoverage.cnn with 4 regions 285s Skip processing na12878-chrM-Y-trunc.bam with empty regions file build/chrM-Y-trunc.hg19.antitarget.bed 285s Wrote build/na12878-chrM-Y-trunc.antitargetcoverage.cnn with 0 regions 285s Processing target: na12878-chrM-Y-trunc 285s Keeping 4 of 4 bins 285s Correcting for GC bias... 285s Processing antitarget: na12878-chrM-Y-trunc 285s Wrote build/na12878-chrM-Y-trunc.cnr with 4 regions 285s Segmenting build/na12878-chrM-Y-trunc.cnr ... 285s Segmenting with method 'cbs', significance threshold 1e-06, in 1 processes 285s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 285s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 285s A typical example is when you are setting values in a column of a DataFrame, like: 285s 285s df["col"][row_indexer] = value 285s 285s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 285s 285s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 285s 285s segments.start.iat[0] = bins_start 285s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 285s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 285s A typical example is when you are setting values in a column of a DataFrame, like: 285s 285s df["col"][row_indexer] = value 285s 285s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 285s 285s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 285s 285s segments.end.iat[-1] = bins_end 285s Post-processing build/na12878-chrM-Y-trunc.cns ... 285s Wrote build/na12878-chrM-Y-trunc.cns with 1 regions 285s Applying filter 'ci' 285s Filtered by 'ci' from 1 to 1 rows 285s Calling copy number with thresholds: -1.1 => 0, -0.25 => 1, 0.2 => 2, 0.7 => 3 285s Wrote build/na12878-chrM-Y-trunc.call.cns with 1 regions 285s Significant hits in 4/4 bins (100%) 285s Wrote build/na12878-chrM-Y-trunc.bintest.cns with 4 regions 286s cnvkit.py import-picard picard/p2-20_5.antitargetcoverage.csv picard/p2-20_5.targetcoverage.csv picard/p2-5_5.antitargetcoverage.csv picard/p2-5_5.targetcoverage.csv picard/p2-9_5.antitargetcoverage.csv picard/p2-9_5.targetcoverage.csv -d build/ 289s Wrote build/p2-20_5.antitargetcoverage.cnn with 12563 regions 289s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 290s Wrote build/p2-20_5.targetcoverage.cnn with 6646 regions 290s Wrote build/p2-5_5.antitargetcoverage.cnn with 12563 regions 290s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 290s Wrote build/p2-5_5.targetcoverage.cnn with 6646 regions 290s Wrote build/p2-9_5.antitargetcoverage.cnn with 12563 regions 290s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 290s Wrote build/p2-9_5.targetcoverage.cnn with 6646 regions 291s cnvkit.py reference build/p2-*_5.*targetcoverage.cnn -y -o build/reference-picard.cnn 294s Number of target and antitarget files: 3, 3 294s No FASTA reference genome provided; skipping GC, RM calculations 294s Sample sex not provided; inferring from samples. 294s Relative log2 coverage of chrX=-0.325, chrY=-6.57 (maleness=0.0191 x 0.532 = 0.0102) --> assuming female 294s Relative log2 coverage of chrX=-0.324, chrY=-11 (maleness=0.0317 x 0.532 = 0.0168) --> assuming female 294s Relative log2 coverage of chrX=-0.17, chrY=-17.9 (maleness=0.0141 x 0.532 = 0.00752) --> assuming female 294s Relative log2 coverage of chrX=-0.522, chrY=-11.2 (maleness=0.179 x 0.809 = 0.145) --> assuming female 294s Relative log2 coverage of chrX=-0.531, chrY=-12.6 (maleness=0.11 x 0.895 = 0.0984) --> assuming female 295s Relative log2 coverage of chrX=-0.412, chrY=-16.1 (maleness=0.0599 x 0.895 = 0.0536) --> assuming female 295s Loading build/p2-20_5.targetcoverage.cnn 295s Correcting for GC bias for p2-20_5... 295s Correcting for density bias for p2-20_5... 295s Loading build/p2-5_5.targetcoverage.cnn 295s Correcting for GC bias for p2-5_5... 295s Correcting for density bias for p2-5_5... 295s Loading build/p2-9_5.targetcoverage.cnn 295s Correcting for GC bias for p2-9_5... 295s Correcting for density bias for p2-9_5... 295s Loading build/p2-20_5.antitargetcoverage.cnn 295s Correcting for GC bias for p2-20_5... 296s Loading build/p2-5_5.antitargetcoverage.cnn 296s Correcting for GC bias for p2-5_5... 296s Loading build/p2-9_5.antitargetcoverage.cnn 296s Correcting for GC bias for p2-9_5... 296s Calculating average bin coverages 307s Calculating bin spreads 309s Targets: 338 (5.086%) bins failed filters (log2 < -5.0, log2 > 5.0, spread > 1.0) 309s PLCH2 chr1:2433503-2433878 log2=-0.586 spread=0.505 309s " chr1:2435319-2436685 log2=-0.709 spread=0.503 309s ARID1A chr1:27022844-27024053 log2=-1.481 spread=0.110 309s MYCL1 chr1:40366439-40367601 log2=0.085 spread=0.245 309s LRRC8B chr1:90000112-90000296 log2=0.225 spread=0.274 309s CGH chr1:106510773-106510953 log2=-0.394 spread=0.325 309s NOTCH2 chr1:120572470-120572622 log2=-2.593 spread=1.502 309s " chr1:120611883-120612051 log2=0.070 spread=0.050 309s NTRK1 chr1:156830676-156830968 log2=-0.814 spread=0.513 309s " chr1:156842391-156842479 log2=-0.398 spread=0.290 309s MPC2 chr1:167906099-167906278 log2=0.166 spread=0.393 309s ABL2 chr1:179198326-179198565 log2=-0.546 spread=0.389 309s SMG7 chr1:183441669-183441848 log2=0.368 spread=0.171 309s " chr1:183481886-183481951 log2=-0.040 spread=0.119 309s CDC73 chr1:193219724-193219907 log2=0.087 spread=0.141 309s AKT3 chr1:243675572-243675743 log2=0.222 spread=0.106 309s " chr1:243800859-243801066 log2=0.023 spread=0.063 309s MYCN chr2:16082132-16082989 log2=-1.145 spread=0.371 309s DNMT3A chr2:25475011-25475213 log2=-1.033 spread=0.468 309s " chr2:25536716-25536891 log2=0.702 spread=0.033 309s MSH2 chr2:47698049-47698231 log2=0.129 spread=0.196 309s MSH6 chr2:48010322-48010657 log2=-0.616 spread=0.644 309s VRK2 chr2:58386820-58386989 log2=0.422 spread=0.287 309s REL chr2:61108881-61109061 log2=1.026 spread=0.559 309s " chr2:61128072-61128256 log2=0.115 spread=0.173 309s " chr2:61145275-61145462 log2=0.014 spread=0.099 309s CGH chr2:82511447-82511627 log2=-0.205 spread=0.129 309s DUSP2 chr2:96810448-96811208 log2=-0.655 spread=0.435 309s MAP3K2 chr2:128081427-128081603 log2=-0.218 spread=0.144 309s " chr2:128095237-128095425 log2=0.215 spread=0.212 309s METTL8 chr2:172291040-172291240 log2=-0.027 spread=0.068 309s VHL chr3:10183481-10183898 log2=-0.155 spread=0.209 309s TGFBR2 chr3:30648320-30648501 log2=0.496 spread=0.504 309s EPHA6 chr3:96585608-96585773 log2=0.045 spread=0.100 309s " chr3:97160189-97160367 log2=-0.050 spread=0.191 309s ATR chr3:142254935-142255077 log2=0.024 spread=0.027 309s " chr3:142286854-142287031 log2=-0.076 spread=0.116 309s GAK chr4:896278-896357 log2=-1.505 spread=1.261 309s FGFR3 chr4:1795608-1795799 log2=-0.750 spread=0.491 309s " chr4:1803044-1803507 log2=-0.073 spread=0.117 309s " chr4:1808792-1809022 log2=0.252 spread=0.108 309s CGH chr4:31509625-31509802 log2=-0.057 spread=0.101 309s EPHA5 chr4:66535226-66535490 log2=-0.681 spread=0.567 309s CGH chr4:163514273-163514460 log2=-0.403 spread=0.271 309s " chr4:178514600-178514725 log2=-0.098 spread=0.096 309s TERT chr5:1293376-1294792 log2=-0.724 spread=0.511 309s TERT Promoter chr5:1294836-1295203 log2=-1.970 spread=0.777 309s " chr5:1295312-1295381 log2=-0.690 spread=0.462 309s CGH chr5:12000292-12000462 log2=-0.342 spread=0.056 309s " chr5:25519857-25520034 log2=-0.015 spread=0.095 309s RICTOR chr5:38958717-38958965 log2=0.011 spread=0.018 309s " chr5:38962338-38962525 log2=0.005 spread=0.019 309s " chr5:38966685-38966862 log2=0.006 spread=0.165 309s " chr5:38978620-38978763 log2=0.040 spread=0.030 309s " chr5:39074133-39074327 log2=-0.158 spread=0.139 309s CGH chr5:42002796-42002944 log2=-0.083 spread=0.071 309s " chr5:51030131-51030313 log2=-0.034 spread=0.192 309s MAP3K1 chr5:56111354-56111894 log2=-1.478 spread=1.050 309s " chr5:56168412-56168590 log2=0.479 spread=0.038 309s CGH chr5:61573234-61573421 log2=-0.072 spread=0.161 309s PIK3R1 chr5:67589487-67589699 log2=0.349 spread=0.264 309s RASA1 chr5:86633752-86633938 log2=0.119 spread=0.112 309s " chr5:86637023-86637168 log2=0.054 spread=0.111 309s " chr5:86642414-86642592 log2=0.254 spread=0.162 309s " chr5:86648912-86649096 log2=0.092 spread=0.062 309s " chr5:86670598-86670778 log2=0.222 spread=0.198 309s MCTP1 chr5:94619518-94620311 log2=-1.064 spread=0.754 309s CGH chr5:99008357-99008506 log2=-0.069 spread=0.390 309s APC chr5:112101965-112102138 log2=0.104 spread=0.217 309s NPM1 chr5:170832371-170832440 log2=-0.398 spread=0.335 309s " chr5:170837567-170837631 log2=0.085 spread=0.349 309s FLT4 chr5:180045962-180046145 log2=-0.884 spread=0.532 309s " chr5:180046203-180046404 log2=-1.531 spread=0.722 309s " chr5:180076418-180076603 log2=-2.224 spread=1.416 309s TPMT chr6:18130863-18131048 log2=0.022 spread=0.099 309s DOM3Z chr6:31938905-31939081 log2=-20.014 spread=0.042 309s " chr6:31939599-31940313 log2=-19.582 spread=0.181 309s STK19 chr6:31940351-31940562 log2=-18.990 spread=0.181 309s " chr6:31946632-31946824 log2=-20.052 spread=0.088 309s " chr6:31947142-31947366 log2=-20.114 spread=0.066 309s " chr6:31948176-31948360 log2=-19.982 spread=0.149 309s " chr6:31948382-31948612 log2=-19.757 spread=0.165 309s " chr6:31948730-31949253 log2=-20.048 spread=0.014 309s NOTCH4 chr6:32163159-32163956 log2=-19.306 spread=0.203 309s " chr6:32164046-32164227 log2=-19.755 spread=0.189 309s " chr6:32164652-32164888 log2=-19.984 spread=0.154 309s " chr6:32165021-32165403 log2=-19.625 spread=0.196 309s " chr6:32166163-32166545 log2=-20.043 spread=0.072 309s " chr6:32166648-32166962 log2=-3.514 spread=5.742 309s " chr6:32168554-32168821 log2=-19.601 spread=0.197 309s " chr6:32168844-32169306 log2=-19.683 spread=0.166 309s " chr6:32169801-32170391 log2=-19.405 spread=0.205 309s " chr6:32171492-32171691 log2=-20.095 spread=0.101 309s " chr6:32171861-32172198 log2=-3.141 spread=7.288 309s " chr6:32178478-32178749 log2=-19.927 spread=0.164 309s " chr6:32180197-32180441 log2=-10.902 spread=9.532 309s " chr6:32180544-32180722 log2=-19.675 spread=0.186 309s " chr6:32180857-32181062 log2=-19.562 spread=0.227 309s " chr6:32181411-32181646 log2=-19.966 spread=0.171 309s " chr6:32181832-32182066 log2=-19.961 spread=0.172 309s " chr6:32182948-32183194 log2=-19.935 spread=0.168 309s " chr6:32184667-32185079 log2=-19.923 spread=0.165 309s " chr6:32185720-32185919 log2=-19.707 spread=0.197 309s " chr6:32187317-32187601 log2=-19.765 spread=0.173 309s " chr6:32187871-32188094 log2=-19.660 spread=0.188 309s " chr6:32188127-32188451 log2=-19.622 spread=0.169 309s " chr6:32188481-32188693 log2=-19.902 spread=0.132 309s " chr6:32188704-32189133 log2=-19.501 spread=0.193 309s " chr6:32190258-32190616 log2=-19.570 spread=0.209 309s " chr6:32190724-32190908 log2=-19.540 spread=0.232 309s " chr6:32191570-32191668 log2=-19.773 spread=0.178 309s " chr6:32191676-32191753 log2=-19.374 spread=0.201 309s FOXP4 chr6:41565467-41565725 log2=-0.259 spread=0.369 309s CCND3 chr6:41909139-41909415 log2=0.039 spread=0.240 309s NFKBIE chr6:44232672-44233479 log2=0.034 spread=0.050 309s CGH chr6:49502071-49502248 log2=-0.093 spread=0.241 309s POU3F2 chr6:99282701-99283145 log2=-1.559 spread=0.536 309s ROS1 chr6:117657131-117657335 log2=-0.377 spread=0.282 309s RSPO3 chr6:127510871-127511054 log2=-0.128 spread=0.105 309s PTPRK chr6:128313744-128313923 log2=0.177 spread=0.190 309s " chr6:128316545-128316699 log2=-0.070 spread=0.050 309s MAP3K5 chr6:137026207-137026289 log2=0.053 spread=0.094 309s ARID1B chr6:157099014-157099324 log2=-0.621 spread=0.410 309s " chr6:157099442-157099991 log2=-0.846 spread=0.504 309s " chr6:157100042-157100634 log2=-1.570 spread=0.825 309s IGF2R chr6:160390228-160390461 log2=-2.660 spread=0.959 309s RAC1 chr7:6414287-6414459 log2=0.162 spread=0.232 309s COL28A1 chr7:7521080-7521229 log2=0.015 spread=0.050 309s FKBP9 chr7:32997131-32997427 log2=-0.367 spread=0.272 309s CGH chr7:52520101-52520286 log2=-1.525 spread=1.176 309s EGFR chr7:55086913-55087091 log2=-0.081 spread=0.180 309s CDK6 chr7:92462355-92462676 log2=0.309 spread=0.198 309s TRRAP chr7:98479525-98479705 log2=0.060 spread=0.087 309s " chr7:98491365-98491541 log2=0.209 spread=0.153 309s " chr7:98493314-98493496 log2=0.389 spread=0.207 309s SMO chr7:128828938-128829041 log2=-2.056 spread=1.317 309s " chr7:128829048-128829358 log2=-0.474 spread=0.315 309s BRAF chr7:140481968-140482374 log2=0.013 spread=0.017 309s " chr7:140484736-140484912 log2=0.081 spread=0.150 309s " chr7:140487956-140488430 log2=-0.089 spread=0.142 309s " chr7:140493336-140493442 log2=-0.340 spread=0.283 309s " chr7:140624315-140624540 log2=-0.497 spread=0.342 309s TNKS chr8:9609998-9610174 log2=0.021 spread=0.082 309s WRN chr8:30925752-30925890 log2=0.120 spread=0.158 309s " chr8:30941153-30941335 log2=0.079 spread=0.294 309s " chr8:30942625-30942802 log2=0.044 spread=0.247 309s " chr8:30947918-30948092 log2=0.151 spread=0.124 309s " chr8:31000128-31000252 log2=-0.099 spread=0.098 309s " chr8:31001006-31001182 log2=-0.002 spread=0.062 309s GPR124 chr8:37654740-37655077 log2=-1.617 spread=1.020 309s " chr8:37698557-37699898 log2=-0.612 spread=0.029 309s ADAM32 chr8:39022338-39022511 log2=-0.103 spread=0.127 309s PRKDC chr8:48827831-48828019 log2=0.018 spread=0.101 309s " chr8:48845528-48845758 log2=0.056 spread=0.196 309s " chr8:48866846-48867043 log2=-0.067 spread=0.097 309s " chr8:48868369-48868526 log2=-0.239 spread=0.174 309s " chr8:48872485-48872722 log2=-0.636 spread=0.464 309s CGH chr8:88503010-88503173 log2=-0.137 spread=0.254 309s FBXO43 chr8:101149746-101149928 log2=0.049 spread=0.082 309s SMARCA2 chr9:2047182-2047507 log2=-0.353 spread=0.368 309s " chr9:2101491-2101640 log2=0.244 spread=0.104 309s JAK2 chr9:5064539-5066968 log2=0.119 spread=0.077 309s " chr9:5067014-5067883 log2=0.146 spread=0.089 309s " chr9:5068184-5070306 log2=0.133 spread=0.092 309s " chr9:5076944-5077152 log2=0.046 spread=0.137 309s PTPRD chr9:8527276-8527424 log2=-0.239 spread=0.237 309s CDKN2A chr9:21973657-21973843 log2=0.148 spread=0.043 309s " chr9:21986759-21986899 log2=0.058 spread=0.175 309s LINGO2 chr9:28505910-28506090 log2=-0.396 spread=0.123 309s CGH chr9:39011524-39011705 log2=-0.205 spread=0.230 309s TRPM3 chr9:73240339-73240498 log2=0.009 spread=0.086 309s NTRK2 chr9:87356721-87356908 log2=0.166 spread=0.265 309s " chr9:87425375-87425452 log2=0.494 spread=0.066 309s PTCH1 chr9:98278654-98278833 log2=-0.560 spread=0.038 309s GPSM1 chr9:139222089-139222248 log2=-4.879 spread=2.118 309s " chr9:139250747-139251032 log2=-0.223 spread=0.273 309s " chr9:139252418-139252703 log2=-0.512 spread=0.264 309s NOTCH1 chr9:139396674-139396979 log2=-0.595 spread=0.295 309s " chr9:139417262-139417677 log2=-0.173 spread=0.191 309s " chr9:139440110-139440287 log2=-1.257 spread=0.500 309s MRC1 chr10:18136438-18136613 log2=-1.945 spread=3.242 309s CGH chr10:22505654-22505837 log2=0.072 spread=0.107 309s RET chr10:43572644-43572830 log2=-1.376 spread=1.092 309s " chr10:43600360-43600677 log2=-0.341 spread=0.222 309s PTEN chr10:89653722-89653907 log2=0.394 spread=0.030 309s " chr10:89685254-89685374 log2=0.244 spread=0.259 309s TNKS2 chr10:93558396-93558682 log2=-0.037 spread=0.051 309s " chr10:93579008-93579121 log2=0.166 spread=0.220 309s SUFU chr10:104263859-104264127 log2=0.269 spread=0.224 309s SHOC2 chr10:112679251-112679451 log2=-0.434 spread=0.304 309s " chr10:112679716-112679934 log2=-0.008 spread=0.028 309s CGH chr10:114003863-114003979 log2=-0.115 spread=0.290 309s WT1 chr11:32456191-32456924 log2=-1.020 spread=0.782 309s CCND1 chr11:69465991-69466081 log2=0.058 spread=0.209 309s GAB2 chr11:78128632-78128813 log2=-0.754 spread=0.552 309s MRE11A chr11:94153219-94153345 log2=-0.021 spread=0.076 309s " chr11:94170270-94170454 log2=0.095 spread=0.285 309s YAP1 chr11:101981528-101981920 log2=-0.331 spread=0.340 309s GUCY1A2 chr11:106888426-106888817 log2=-1.400 spread=0.707 309s ATM chr11:108153392-108153639 log2=0.113 spread=0.136 309s " chr11:108164020-108164234 log2=-0.056 spread=0.040 309s " chr11:108217986-108218135 log2=0.359 spread=0.277 309s MLL chr11:118307178-118307688 log2=-1.486 spread=0.278 309s ARHGAP32 chr11:129003788-129003964 log2=0.053 spread=0.102 309s ETV6 chr12:12018196-12018602 log2=-0.014 spread=0.112 309s KRAS chr12:25362676-25362878 log2=0.128 spread=0.150 309s " chr12:25391004-25391186 log2=0.368 spread=0.128 309s DIP2B chr12:51002431-51002557 log2=0.344 spread=0.188 309s ATF1 chr12:51206642-51206737 log2=0.009 spread=0.024 309s " chr12:51206782-51207287 log2=-0.012 spread=0.101 309s PTPN11 chr12:112856822-112857002 log2=-0.268 spread=0.071 309s CDK8 chr13:26911641-26911828 log2=-0.125 spread=0.167 309s " chr13:26970356-26970536 log2=0.137 spread=0.121 309s FLT3 chr13:28674525-28674708 log2=-2.257 spread=1.745 309s FLT1 chr13:29068850-29069036 log2=-0.914 spread=0.672 309s BRCA2 chr13:32900164-32900478 log2=-0.076 spread=0.110 309s " chr13:32903506-32903689 log2=0.019 spread=0.232 309s " chr13:32918645-32918823 log2=0.158 spread=0.114 309s " chr13:32920899-32921077 log2=0.014 spread=0.247 309s RB1 chr13:48877994-48878223 log2=-0.155 spread=0.250 309s " chr13:48919164-48919369 log2=0.025 spread=0.094 309s " chr13:48921877-48922044 log2=0.106 spread=0.147 309s " chr13:48923026-48923204 log2=0.360 spread=0.016 309s " chr13:48938965-48939147 log2=0.084 spread=0.356 309s " chr13:48941578-48941767 log2=0.238 spread=0.212 309s " chr13:48947486-48947665 log2=0.025 spread=0.126 309s " chr13:48954166-48954251 log2=0.061 spread=0.273 309s " chr13:49037815-49038005 log2=0.063 spread=0.062 309s " chr13:49047410-49047589 log2=0.053 spread=0.170 309s CGH chr13:63016375-63016514 log2=-0.434 spread=0.282 309s " chr13:85527771-85527948 log2=0.007 spread=0.222 309s GPC5 chr13:93003992-93004180 log2=-0.192 spread=0.289 309s IRS2 chr13:110434582-110438324 log2=-0.768 spread=0.252 309s " chr13:110438332-110438432 log2=-3.643 spread=2.325 309s CGH chr14:19220933-19221047 log2=-1.230 spread=1.205 309s " chr14:20291466-20291612 log2=0.697 spread=0.248 309s NKX2-1 chr14:36986433-36987264 log2=-1.444 spread=0.135 309s " chr14:36989195-36989373 log2=0.023 spread=0.162 309s SOS2 chr14:50596601-50596777 log2=0.237 spread=0.146 309s " chr14:50619741-50619923 log2=0.003 spread=0.272 309s " chr14:50697856-50698044 log2=0.277 spread=0.334 309s MAP3K9 chr14:71275435-71275784 log2=-0.385 spread=0.461 309s " chr14:71275790-71275923 log2=-2.815 spread=1.938 309s TSHR chr14:81528428-81528609 log2=0.070 spread=0.244 309s CGH chr15:20870700-20870881 log2=1.102 spread=0.611 309s " chr15:38544477-38545455 log2=-0.348 spread=0.179 309s SPRED1 chr15:38647081-38647177 log2=0.225 spread=0.350 309s LTK chr15:41803310-41803809 log2=-1.956 spread=1.443 309s " chr15:41803968-41804185 log2=-2.202 spread=0.188 309s " chr15:41804261-41804494 log2=-0.402 spread=0.392 309s NTRK3 chr15:88532873-88532940 log2=-0.337 spread=0.240 309s IDH2 chr15:90645456-90645663 log2=-0.951 spread=0.705 309s PDPK1 chr16:2588022-2588201 log2=-1.782 spread=1.082 309s " chr16:2611404-2611585 log2=-5.171 spread=1.963 309s " chr16:2611721-2611943 log2=-11.810 spread=9.172 309s " chr16:2615504-2615725 log2=-20.113 spread=0.007 309s " chr16:2616307-2616486 log2=-20.054 spread=0.072 309s " chr16:2631267-2631414 log2=-20.009 spread=0.061 309s " chr16:2631583-2631734 log2=-20.089 spread=0.090 309s " chr16:2633363-2633616 log2=-20.101 spread=0.089 309s CREBBP chr16:3799553-3799731 log2=0.082 spread=0.155 309s SOCS1 chr16:11349442-11349617 log2=0.110 spread=0.079 309s BOLA2B chr16:29466011-29466278 log2=-18.994 spread=0.172 309s CGH chr16:31526705-31526886 log2=0.057 spread=0.074 309s CYLD chr16:50821673-50821804 log2=0.007 spread=0.188 309s " chr16:50826460-50826650 log2=0.239 spread=0.204 309s CGH chr16:51098326-51098475 log2=-0.578 spread=0.376 309s " chr16:60005694-60005857 log2=-0.366 spread=0.298 309s CDH1 chr16:68771239-68771428 log2=-0.813 spread=0.625 309s CGH chr16:81005282-81005469 log2=-0.134 spread=0.229 310s MAP2K4 chr17:11924151-11924355 log2=-2.093 spread=1.900 310s " chr17:12011053-12011253 log2=0.028 spread=0.232 310s NF1 chr17:29422259-29422433 log2=-0.466 spread=0.381 310s " chr17:29496854-29497046 log2=0.226 spread=0.193 310s RHOT1 chr17:30469629-30469811 log2=-0.034 spread=0.252 310s " chr17:30519172-30519355 log2=0.163 spread=0.289 310s " chr17:30525911-30526093 log2=-0.157 spread=0.046 310s ERBB2 chr17:37856430-37856609 log2=0.221 spread=0.369 310s RPTOR chr17:78896472-78896661 log2=0.267 spread=0.290 310s C18orf56 chr18:657548-657727 log2=-2.406 spread=0.189 310s CDH2 chr18:25756859-25757030 log2=-3.262 spread=0.283 310s KIAA1328 chr18:34512037-34512192 log2=0.081 spread=0.180 310s CGH chr18:42005882-42006049 log2=-0.031 spread=0.038 310s SMAD4 chr18:48575581-48575754 log2=0.224 spread=0.215 310s CGH chr18:58519179-58519311 log2=-0.254 spread=0.214 310s STK11 chr19:1226401-1226680 log2=-0.501 spread=0.149 310s DOT1L chr19:2164127-2164302 log2=-1.071 spread=0.866 310s " chr19:2210351-2210545 log2=-0.860 spread=0.261 310s " chr19:2226130-2227152 log2=-0.113 spread=0.120 310s GNA11 chr19:3094603-3094817 log2=-0.229 spread=0.152 310s GIPC3 chr19:3585549-3585853 log2=-1.516 spread=1.086 310s MAP2K2 chr19:4123728-4123907 log2=-0.004 spread=0.187 310s INSR chr19:7293751-7294042 log2=-2.020 spread=1.538 310s SMARCA4 chr19:11098288-11098630 log2=-0.647 spread=0.206 310s PODNL1 chr19:14063277-14063492 log2=-0.226 spread=0.272 310s NOTCH3 chr19:15288281-15288927 log2=-1.510 spread=0.106 310s " chr19:15311544-15311749 log2=-1.887 spread=1.202 310s JAK3 chr19:17953074-17953444 log2=-0.886 spread=0.423 310s CCNE1 chr19:30303376-30303718 log2=-0.486 spread=0.368 310s CEBPA chr19:33792189-33792764 log2=-0.086 spread=0.251 310s " chr19:33792774-33793010 log2=-1.781 spread=0.127 310s " chr19:33793014-33793356 log2=-0.541 spread=0.385 310s CD79A chr19:42384673-42384848 log2=-1.881 spread=1.227 310s ERCC2 chr19:45866950-45867408 log2=-0.750 spread=0.536 310s " chr19:45873698-45873881 log2=-0.522 spread=0.327 310s BCL2L12 chr19:50173423-50173770 log2=-0.005 spread=0.243 310s SRC chr20:36012507-36012834 log2=-0.373 spread=0.448 310s TOP1 chr20:39657625-39657796 log2=-0.187 spread=0.282 310s PLCG1 chr20:39766233-39766531 log2=-0.105 spread=0.074 310s AURKA chr20:54959250-54959432 log2=0.185 spread=0.183 310s SYCP2 chr20:58505615-58505798 log2=-0.018 spread=0.034 310s ARFRP1 chr20:62339169-62339402 log2=-1.471 spread=0.976 310s RUNX1 chr21:36164379-36164936 log2=0.163 spread=0.307 310s " chr21:36265140-36265318 log2=0.042 spread=0.039 310s CHEK2 chr22:29105918-29106101 log2=0.091 spread=0.114 310s " chr22:29115325-29115465 log2=0.055 spread=0.390 310s SOX10 chr22:38379312-38379815 log2=-0.427 spread=0.308 310s CGH chr22:41487738-41489134 log2=-0.174 spread=0.116 310s EP300 chr22:41562534-41562711 log2=0.160 spread=0.125 310s CRLF2 chrX:1314835-1315039 log2=-21.107 spread=0.037 310s " chrX:1317372-1317594 log2=-21.121 spread=0.049 310s " chrX:1321225-1321435 log2=-20.851 spread=0.196 310s " chrX:1325274-1325530 log2=-21.121 spread=0.011 310s " chrX:1327651-1327830 log2=-21.000 spread=0.029 310s " chrX:1331391-1331576 log2=-21.018 spread=0.052 310s KDM6A chrX:44820507-44820671 log2=-0.582 spread=0.279 310s PAK3 chrX:110435687-110435871 log2=-1.172 spread=0.053 310s STAG2 chrX:123182790-123182965 log2=-0.941 spread=0.228 310s " chrX:123185143-123185290 log2=-1.018 spread=0.062 310s " chrX:123202378-123202544 log2=-0.959 spread=0.103 310s FAM58A chrX:152864353-152864586 log2=-2.437 spread=0.754 310s CGH chrY:4564417-4564597 log2=-1.004 spread=0.063 310s " chrY:9008607-9008759 log2=-1.012 spread=0.097 310s " chrY:13131970-13132039 log2=-1.004 spread=0.074 310s " chrY:19506485-19506650 log2=-1.018 spread=0.034 310s " chrY:21033918-21034071 log2=-1.013 spread=0.057 310s " chrY:28463435-28463622 log2=-0.952 spread=0.039 310s " chrY:28514033-28514208 log2=-0.992 spread=0.026 310s Antitargets: 108 (0.8597%) bins failed filters 310s Wrote build/reference-picard.cnn with 19209 regions 310s cnvkit.py fix build/p2-5_5.targetcoverage.cnn build/p2-5_5.antitargetcoverage.cnn build/reference-picard.cnn -o build/p2-5_5.cnr 314s Processing target: p2-5_5 314s Keeping 6308 of 6646 bins 314s Correcting for GC bias... 314s Correcting for density bias... 314s Processing antitarget: p2-5_5 314s Keeping 12455 of 12563 bins 314s Correcting for GC bias... 315s WARNING: Skipping correction for RepeatMasker bias 315s Targets are 1.18 x more variable than antitargets 315s Wrote build/p2-5_5.cnr with 18763 regions 316s cnvkit.py import-picard picard/p2-9_2.targetcoverage.csv -d build/ 319s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 319s Wrote build/p2-9_2.targetcoverage.cnn with 6646 regions 320s cnvkit.py import-picard picard/p2-9_2.antitargetcoverage.csv -d build/ 323s Wrote build/p2-9_2.antitargetcoverage.cnn with 12563 regions 324s cnvkit.py fix build/p2-9_2.targetcoverage.cnn build/p2-9_2.antitargetcoverage.cnn build/reference-picard.cnn -o build/p2-9_2.cnr 327s Processing target: p2-9_2 327s Keeping 6308 of 6646 bins 327s Correcting for GC bias... 327s Correcting for density bias... 327s Processing antitarget: p2-9_2 328s Keeping 12455 of 12563 bins 328s Correcting for GC bias... 328s WARNING: Skipping correction for RepeatMasker bias 328s Targets are 1.72 x more variable than antitargets 328s Wrote build/p2-9_2.cnr with 18763 regions 329s cnvkit.py import-picard picard/p2-20_1.targetcoverage.csv -d build/ 332s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 332s Wrote build/p2-20_1.targetcoverage.cnn with 6646 regions 333s cnvkit.py import-picard picard/p2-20_1.antitargetcoverage.csv -d build/ 336s Wrote build/p2-20_1.antitargetcoverage.cnn with 12563 regions 337s cnvkit.py fix build/p2-20_1.targetcoverage.cnn build/p2-20_1.antitargetcoverage.cnn build/reference-picard.cnn -o build/p2-20_1.cnr 340s Processing target: p2-20_1 340s Keeping 6308 of 6646 bins 340s Correcting for GC bias... 340s Correcting for density bias... 340s Processing antitarget: p2-20_1 340s Keeping 12455 of 12563 bins 341s Correcting for GC bias... 341s WARNING: Skipping correction for RepeatMasker bias 341s Targets are 1.12 x more variable than antitargets 341s Wrote build/p2-20_1.cnr with 18763 regions 342s cnvkit.py import-picard picard/p2-20_2.targetcoverage.csv -d build/ 345s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 345s Wrote build/p2-20_2.targetcoverage.cnn with 6646 regions 346s cnvkit.py import-picard picard/p2-20_2.antitargetcoverage.csv -d build/ 349s Wrote build/p2-20_2.antitargetcoverage.cnn with 12563 regions 349s cnvkit.py fix build/p2-20_2.targetcoverage.cnn build/p2-20_2.antitargetcoverage.cnn build/reference-picard.cnn -o build/p2-20_2.cnr 353s Processing target: p2-20_2 353s Keeping 6308 of 6646 bins 353s Correcting for GC bias... 353s Correcting for density bias... 353s Processing antitarget: p2-20_2 353s Keeping 12455 of 12563 bins 353s Correcting for GC bias... 353s WARNING: Skipping correction for RepeatMasker bias 354s Targets are 1.10 x more variable than antitargets 354s Wrote build/p2-20_2.cnr with 18763 regions 355s cnvkit.py import-picard picard/p2-20_3.targetcoverage.csv -d build/ 358s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 358s Wrote build/p2-20_3.targetcoverage.cnn with 6646 regions 358s cnvkit.py import-picard picard/p2-20_3.antitargetcoverage.csv -d build/ 362s Wrote build/p2-20_3.antitargetcoverage.cnn with 12563 regions 362s cnvkit.py fix build/p2-20_3.targetcoverage.cnn build/p2-20_3.antitargetcoverage.cnn build/reference-picard.cnn -o build/p2-20_3.cnr 366s Processing target: p2-20_3 366s Keeping 6308 of 6646 bins 366s Correcting for GC bias... 366s Correcting for density bias... 366s Processing antitarget: p2-20_3 366s Keeping 12455 of 12563 bins 366s Correcting for GC bias... 366s WARNING: Skipping correction for RepeatMasker bias 367s Antitargets are 1.09 x more variable than targets 367s Wrote build/p2-20_3.cnr with 18763 regions 368s cnvkit.py import-picard picard/p2-20_4.targetcoverage.csv -d build/ 371s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 371s Wrote build/p2-20_4.targetcoverage.cnn with 6646 regions 371s cnvkit.py import-picard picard/p2-20_4.antitargetcoverage.csv -d build/ 375s Wrote build/p2-20_4.antitargetcoverage.cnn with 12563 regions 375s cnvkit.py fix build/p2-20_4.targetcoverage.cnn build/p2-20_4.antitargetcoverage.cnn build/reference-picard.cnn -o build/p2-20_4.cnr 378s Processing target: p2-20_4 379s Keeping 6308 of 6646 bins 379s Correcting for GC bias... 379s Correcting for density bias... 379s Processing antitarget: p2-20_4 379s Keeping 12455 of 12563 bins 379s Correcting for GC bias... 379s WARNING: Skipping correction for RepeatMasker bias 380s Targets are 1.43 x more variable than antitargets 380s Wrote build/p2-20_4.cnr with 18763 regions 381s cnvkit.py fix build/p2-20_5.targetcoverage.cnn build/p2-20_5.antitargetcoverage.cnn build/reference-picard.cnn -o build/p2-20_5.cnr 384s Processing target: p2-20_5 384s Keeping 6308 of 6646 bins 384s Correcting for GC bias... 384s Correcting for density bias... 384s Processing antitarget: p2-20_5 385s Keeping 12455 of 12563 bins 385s Correcting for GC bias... 385s WARNING: Skipping correction for RepeatMasker bias 385s Targets are 2.50 x more variable than antitargets 385s Wrote build/p2-20_5.cnr with 18763 regions 386s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-5_5.cnr -o build/p2-5_5.cns 390s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 390s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 390s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 390s A typical example is when you are setting values in a column of a DataFrame, like: 390s 390s df["col"][row_indexer] = value 390s 390s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 390s 390s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 390s 390s segments.start.iat[0] = bins_start 390s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 390s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 390s A typical example is when you are setting values in a column of a DataFrame, like: 390s 390s df["col"][row_indexer] = value 390s 390s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 390s 390s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 390s 390s segments.end.iat[-1] = bins_end 390s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 390s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 390s A typical example is when you are setting values in a column of a DataFrame, like: 390s 390s df["col"][row_indexer] = value 390s 390s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 390s 390s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 390s 390s segments.start.iat[0] = bins_start 390s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 390s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 390s A typical example is when you are setting values in a column of a DataFrame, like: 390s 390s df["col"][row_indexer] = value 390s 390s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 390s 390s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 390s 390s segments.end.iat[-1] = bins_end 391s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 391s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 391s A typical example is when you are setting values in a column of a DataFrame, like: 391s 391s df["col"][row_indexer] = value 391s 391s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 391s 391s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 391s 391s segments.start.iat[0] = bins_start 391s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 391s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 391s A typical example is when you are setting values in a column of a DataFrame, like: 391s 391s df["col"][row_indexer] = value 391s 391s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 391s 391s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 391s 391s segments.end.iat[-1] = bins_end 391s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 391s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 391s A typical example is when you are setting values in a column of a DataFrame, like: 391s 391s df["col"][row_indexer] = value 391s 391s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 391s 391s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 391s 391s segments.start.iat[0] = bins_start 391s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 391s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 391s A typical example is when you are setting values in a column of a DataFrame, like: 391s 391s df["col"][row_indexer] = value 391s 391s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 391s 391s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 391s 391s segments.end.iat[-1] = bins_end 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.start.iat[0] = bins_start 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.end.iat[-1] = bins_end 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.start.iat[0] = bins_start 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.end.iat[-1] = bins_end 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.start.iat[0] = bins_start 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.end.iat[-1] = bins_end 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.start.iat[0] = bins_start 392s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 392s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 392s A typical example is when you are setting values in a column of a DataFrame, like: 392s 392s df["col"][row_indexer] = value 392s 392s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 392s 392s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 392s 392s segments.end.iat[-1] = bins_end 393s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 393s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 393s A typical example is when you are setting values in a column of a DataFrame, like: 393s 393s df["col"][row_indexer] = value 393s 393s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 393s 393s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 393s 393s segments.start.iat[0] = bins_start 393s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 393s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 393s A typical example is when you are setting values in a column of a DataFrame, like: 393s 393s df["col"][row_indexer] = value 393s 393s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 393s 393s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 393s 393s segments.end.iat[-1] = bins_end 393s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 393s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 393s A typical example is when you are setting values in a column of a DataFrame, like: 393s 393s df["col"][row_indexer] = value 393s 393s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 393s 393s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 393s 393s segments.start.iat[0] = bins_start 393s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 393s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 393s A typical example is when you are setting values in a column of a DataFrame, like: 393s 393s df["col"][row_indexer] = value 393s 393s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 393s 393s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 393s 393s segments.end.iat[-1] = bins_end 393s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 393s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 393s A typical example is when you are setting values in a column of a DataFrame, like: 393s 393s df["col"][row_indexer] = value 393s 393s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 393s 393s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 393s 393s segments.start.iat[0] = bins_start 393s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 393s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 393s A typical example is when you are setting values in a column of a DataFrame, like: 393s 393s df["col"][row_indexer] = value 393s 393s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 393s 393s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 393s 393s segments.end.iat[-1] = bins_end 394s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 394s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 394s A typical example is when you are setting values in a column of a DataFrame, like: 394s 394s df["col"][row_indexer] = value 394s 394s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 394s 394s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 394s 394s segments.start.iat[0] = bins_start 394s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 394s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 394s A typical example is when you are setting values in a column of a DataFrame, like: 394s 394s df["col"][row_indexer] = value 394s 394s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 394s 394s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 394s 394s segments.end.iat[-1] = bins_end 394s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 394s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 394s A typical example is when you are setting values in a column of a DataFrame, like: 394s 394s df["col"][row_indexer] = value 394s 394s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 394s 394s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 394s 394s segments.start.iat[0] = bins_start 394s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 394s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 394s A typical example is when you are setting values in a column of a DataFrame, like: 394s 394s df["col"][row_indexer] = value 394s 394s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 394s 394s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 394s 394s segments.end.iat[-1] = bins_end 394s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 394s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 394s A typical example is when you are setting values in a column of a DataFrame, like: 394s 394s df["col"][row_indexer] = value 394s 394s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 394s 394s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 394s 394s segments.start.iat[0] = bins_start 394s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 394s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 394s A typical example is when you are setting values in a column of a DataFrame, like: 394s 394s df["col"][row_indexer] = value 394s 394s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 394s 394s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 394s 394s segments.end.iat[-1] = bins_end 395s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 395s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 395s A typical example is when you are setting values in a column of a DataFrame, like: 395s 395s df["col"][row_indexer] = value 395s 395s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 395s 395s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 395s 395s segments.start.iat[0] = bins_start 395s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 395s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 395s A typical example is when you are setting values in a column of a DataFrame, like: 395s 395s df["col"][row_indexer] = value 395s 395s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 395s 395s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 395s 395s segments.end.iat[-1] = bins_end 395s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 395s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 395s A typical example is when you are setting values in a column of a DataFrame, like: 395s 395s df["col"][row_indexer] = value 395s 395s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 395s 395s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 395s 395s segments.start.iat[0] = bins_start 395s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 395s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 395s A typical example is when you are setting values in a column of a DataFrame, like: 395s 395s df["col"][row_indexer] = value 395s 395s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 395s 395s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 395s 395s segments.end.iat[-1] = bins_end 395s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 395s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 395s A typical example is when you are setting values in a column of a DataFrame, like: 395s 395s df["col"][row_indexer] = value 395s 395s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 395s 395s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 395s 395s segments.start.iat[0] = bins_start 395s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 395s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 395s A typical example is when you are setting values in a column of a DataFrame, like: 395s 395s df["col"][row_indexer] = value 395s 395s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 395s 395s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 395s 395s segments.end.iat[-1] = bins_end 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.start.iat[0] = bins_start 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.end.iat[-1] = bins_end 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.start.iat[0] = bins_start 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.end.iat[-1] = bins_end 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.start.iat[0] = bins_start 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.end.iat[-1] = bins_end 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.start.iat[0] = bins_start 396s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 396s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 396s A typical example is when you are setting values in a column of a DataFrame, like: 396s 396s df["col"][row_indexer] = value 396s 396s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 396s 396s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 396s 396s segments.end.iat[-1] = bins_end 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.start.iat[0] = bins_start 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.end.iat[-1] = bins_end 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.start.iat[0] = bins_start 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.end.iat[-1] = bins_end 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.start.iat[0] = bins_start 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.end.iat[-1] = bins_end 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.start.iat[0] = bins_start 397s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 397s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 397s A typical example is when you are setting values in a column of a DataFrame, like: 397s 397s df["col"][row_indexer] = value 397s 397s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 397s 397s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 397s 397s segments.end.iat[-1] = bins_end 398s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 398s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 398s A typical example is when you are setting values in a column of a DataFrame, like: 398s 398s df["col"][row_indexer] = value 398s 398s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 398s 398s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 398s 398s segments.start.iat[0] = bins_start 398s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 398s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 398s A typical example is when you are setting values in a column of a DataFrame, like: 398s 398s df["col"][row_indexer] = value 398s 398s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 398s 398s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 398s 398s segments.end.iat[-1] = bins_end 398s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 398s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 398s A typical example is when you are setting values in a column of a DataFrame, like: 398s 398s df["col"][row_indexer] = value 398s 398s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 398s 398s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 398s 398s segments.start.iat[0] = bins_start 398s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 398s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 398s A typical example is when you are setting values in a column of a DataFrame, like: 398s 398s df["col"][row_indexer] = value 398s 398s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 398s 398s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 398s 398s segments.end.iat[-1] = bins_end 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.start.iat[0] = bins_start 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.end.iat[-1] = bins_end 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.start.iat[0] = bins_start 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.end.iat[-1] = bins_end 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.start.iat[0] = bins_start 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.end.iat[-1] = bins_end 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.start.iat[0] = bins_start 399s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 399s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 399s A typical example is when you are setting values in a column of a DataFrame, like: 399s 399s df["col"][row_indexer] = value 399s 399s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 399s 399s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 399s 399s segments.end.iat[-1] = bins_end 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.start.iat[0] = bins_start 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.end.iat[-1] = bins_end 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.start.iat[0] = bins_start 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.end.iat[-1] = bins_end 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.start.iat[0] = bins_start 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.end.iat[-1] = bins_end 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.start.iat[0] = bins_start 400s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 400s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 400s A typical example is when you are setting values in a column of a DataFrame, like: 400s 400s df["col"][row_indexer] = value 400s 400s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 400s 400s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 400s 400s segments.end.iat[-1] = bins_end 401s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 401s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 401s A typical example is when you are setting values in a column of a DataFrame, like: 401s 401s df["col"][row_indexer] = value 401s 401s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 401s 401s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 401s 401s segments.start.iat[0] = bins_start 401s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 401s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 401s A typical example is when you are setting values in a column of a DataFrame, like: 401s 401s df["col"][row_indexer] = value 401s 401s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 401s 401s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 401s 401s segments.end.iat[-1] = bins_end 401s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 401s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 401s A typical example is when you are setting values in a column of a DataFrame, like: 401s 401s df["col"][row_indexer] = value 401s 401s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 401s 401s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 401s 401s segments.start.iat[0] = bins_start 401s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 401s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 401s A typical example is when you are setting values in a column of a DataFrame, like: 401s 401s df["col"][row_indexer] = value 401s 401s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 401s 401s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 401s 401s segments.end.iat[-1] = bins_end 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.start.iat[0] = bins_start 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.end.iat[-1] = bins_end 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.start.iat[0] = bins_start 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.end.iat[-1] = bins_end 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.start.iat[0] = bins_start 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.end.iat[-1] = bins_end 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.start.iat[0] = bins_start 402s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 402s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 402s A typical example is when you are setting values in a column of a DataFrame, like: 402s 402s df["col"][row_indexer] = value 402s 402s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 402s 402s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 402s 402s segments.end.iat[-1] = bins_end 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.start.iat[0] = bins_start 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.end.iat[-1] = bins_end 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.start.iat[0] = bins_start 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.end.iat[-1] = bins_end 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.start.iat[0] = bins_start 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.end.iat[-1] = bins_end 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.start.iat[0] = bins_start 403s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 403s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 403s A typical example is when you are setting values in a column of a DataFrame, like: 403s 403s df["col"][row_indexer] = value 403s 403s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 403s 403s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 403s 403s segments.end.iat[-1] = bins_end 403s Dropped 8 / 49 bins on chromosome chrY 404s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 404s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 404s A typical example is when you are setting values in a column of a DataFrame, like: 404s 404s df["col"][row_indexer] = value 404s 404s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 404s 404s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 404s 404s segments.start.iat[0] = bins_start 404s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 404s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 404s A typical example is when you are setting values in a column of a DataFrame, like: 404s 404s df["col"][row_indexer] = value 404s 404s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 404s 404s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 404s 404s segments.end.iat[-1] = bins_end 404s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 404s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 404s A typical example is when you are setting values in a column of a DataFrame, like: 404s 404s df["col"][row_indexer] = value 404s 404s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 404s 404s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 404s 404s segments.start.iat[0] = bins_start 404s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 404s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 404s A typical example is when you are setting values in a column of a DataFrame, like: 404s 404s df["col"][row_indexer] = value 404s 404s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 404s 404s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 404s 404s segments.end.iat[-1] = bins_end 404s Wrote build/p2-5_5.cns with 71 regions 405s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-9_2.cnr -o build/p2-9_2.cns 408s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 409s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 409s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 409s A typical example is when you are setting values in a column of a DataFrame, like: 409s 409s df["col"][row_indexer] = value 409s 409s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 409s 409s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 409s 409s segments.start.iat[0] = bins_start 409s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 409s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 409s A typical example is when you are setting values in a column of a DataFrame, like: 409s 409s df["col"][row_indexer] = value 409s 409s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 409s 409s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 409s 409s segments.end.iat[-1] = bins_end 409s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 409s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 409s A typical example is when you are setting values in a column of a DataFrame, like: 409s 409s df["col"][row_indexer] = value 409s 409s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 409s 409s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 409s 409s segments.start.iat[0] = bins_start 409s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 409s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 409s A typical example is when you are setting values in a column of a DataFrame, like: 409s 409s df["col"][row_indexer] = value 409s 409s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 409s 409s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 409s 409s segments.end.iat[-1] = bins_end 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.start.iat[0] = bins_start 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.end.iat[-1] = bins_end 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.start.iat[0] = bins_start 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.end.iat[-1] = bins_end 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.start.iat[0] = bins_start 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.end.iat[-1] = bins_end 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.start.iat[0] = bins_start 410s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 410s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 410s A typical example is when you are setting values in a column of a DataFrame, like: 410s 410s df["col"][row_indexer] = value 410s 410s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 410s 410s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 410s 410s segments.end.iat[-1] = bins_end 411s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 411s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 411s A typical example is when you are setting values in a column of a DataFrame, like: 411s 411s df["col"][row_indexer] = value 411s 411s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 411s 411s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 411s 411s segments.start.iat[0] = bins_start 411s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 411s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 411s A typical example is when you are setting values in a column of a DataFrame, like: 411s 411s df["col"][row_indexer] = value 411s 411s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 411s 411s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 411s 411s segments.end.iat[-1] = bins_end 411s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 411s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 411s A typical example is when you are setting values in a column of a DataFrame, like: 411s 411s df["col"][row_indexer] = value 411s 411s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 411s 411s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 411s 411s segments.start.iat[0] = bins_start 411s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 411s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 411s A typical example is when you are setting values in a column of a DataFrame, like: 411s 411s df["col"][row_indexer] = value 411s 411s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 411s 411s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 411s 411s segments.end.iat[-1] = bins_end 411s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 411s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 411s A typical example is when you are setting values in a column of a DataFrame, like: 411s 411s df["col"][row_indexer] = value 411s 411s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 411s 411s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 411s 411s segments.start.iat[0] = bins_start 411s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 411s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 411s A typical example is when you are setting values in a column of a DataFrame, like: 411s 411s df["col"][row_indexer] = value 411s 411s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 411s 411s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 411s 411s segments.end.iat[-1] = bins_end 412s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 412s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 412s A typical example is when you are setting values in a column of a DataFrame, like: 412s 412s df["col"][row_indexer] = value 412s 412s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 412s 412s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 412s 412s segments.start.iat[0] = bins_start 412s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 412s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 412s A typical example is when you are setting values in a column of a DataFrame, like: 412s 412s df["col"][row_indexer] = value 412s 412s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 412s 412s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 412s 412s segments.end.iat[-1] = bins_end 412s Dropped 1 / 949 bins on chromosome chr6 412s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 412s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 412s A typical example is when you are setting values in a column of a DataFrame, like: 412s 412s df["col"][row_indexer] = value 412s 412s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 412s 412s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 412s 412s segments.start.iat[0] = bins_start 412s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 412s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 412s A typical example is when you are setting values in a column of a DataFrame, like: 412s 412s df["col"][row_indexer] = value 412s 412s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 412s 412s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 412s 412s segments.end.iat[-1] = bins_end 412s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 412s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 412s A typical example is when you are setting values in a column of a DataFrame, like: 412s 412s df["col"][row_indexer] = value 412s 412s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 412s 412s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 412s 412s segments.start.iat[0] = bins_start 412s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 412s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 412s A typical example is when you are setting values in a column of a DataFrame, like: 412s 412s df["col"][row_indexer] = value 412s 412s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 412s 412s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 412s 412s segments.end.iat[-1] = bins_end 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.start.iat[0] = bins_start 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.end.iat[-1] = bins_end 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.start.iat[0] = bins_start 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.end.iat[-1] = bins_end 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.start.iat[0] = bins_start 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.end.iat[-1] = bins_end 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.start.iat[0] = bins_start 413s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 413s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 413s A typical example is when you are setting values in a column of a DataFrame, like: 413s 413s df["col"][row_indexer] = value 413s 413s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 413s 413s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 413s 413s segments.end.iat[-1] = bins_end 414s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 414s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 414s A typical example is when you are setting values in a column of a DataFrame, like: 414s 414s df["col"][row_indexer] = value 414s 414s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 414s 414s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 414s 414s segments.start.iat[0] = bins_start 414s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 414s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 414s A typical example is when you are setting values in a column of a DataFrame, like: 414s 414s df["col"][row_indexer] = value 414s 414s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 414s 414s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 414s 414s segments.end.iat[-1] = bins_end 414s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 414s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 414s A typical example is when you are setting values in a column of a DataFrame, like: 414s 414s df["col"][row_indexer] = value 414s 414s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 414s 414s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 414s 414s segments.start.iat[0] = bins_start 414s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 414s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 414s A typical example is when you are setting values in a column of a DataFrame, like: 414s 414s df["col"][row_indexer] = value 414s 414s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 414s 414s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 414s 414s segments.end.iat[-1] = bins_end 414s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 414s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 414s A typical example is when you are setting values in a column of a DataFrame, like: 414s 414s df["col"][row_indexer] = value 414s 414s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 414s 414s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 414s 414s segments.start.iat[0] = bins_start 414s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 414s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 414s A typical example is when you are setting values in a column of a DataFrame, like: 414s 414s df["col"][row_indexer] = value 414s 414s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 414s 414s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 414s 414s segments.end.iat[-1] = bins_end 415s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 415s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 415s A typical example is when you are setting values in a column of a DataFrame, like: 415s 415s df["col"][row_indexer] = value 415s 415s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 415s 415s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 415s 415s segments.start.iat[0] = bins_start 415s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 415s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 415s A typical example is when you are setting values in a column of a DataFrame, like: 415s 415s df["col"][row_indexer] = value 415s 415s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 415s 415s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 415s 415s segments.end.iat[-1] = bins_end 415s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 415s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 415s A typical example is when you are setting values in a column of a DataFrame, like: 415s 415s df["col"][row_indexer] = value 415s 415s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 415s 415s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 415s 415s segments.start.iat[0] = bins_start 415s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 415s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 415s A typical example is when you are setting values in a column of a DataFrame, like: 415s 415s df["col"][row_indexer] = value 415s 415s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 415s 415s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 415s 415s segments.end.iat[-1] = bins_end 415s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 415s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 415s A typical example is when you are setting values in a column of a DataFrame, like: 415s 415s df["col"][row_indexer] = value 415s 415s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 415s 415s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 415s 415s segments.start.iat[0] = bins_start 415s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 415s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 415s A typical example is when you are setting values in a column of a DataFrame, like: 415s 415s df["col"][row_indexer] = value 415s 415s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 415s 415s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 415s 415s segments.end.iat[-1] = bins_end 416s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 416s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 416s A typical example is when you are setting values in a column of a DataFrame, like: 416s 416s df["col"][row_indexer] = value 416s 416s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 416s 416s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 416s 416s segments.start.iat[0] = bins_start 416s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 416s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 416s A typical example is when you are setting values in a column of a DataFrame, like: 416s 416s df["col"][row_indexer] = value 416s 416s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 416s 416s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 416s 416s segments.end.iat[-1] = bins_end 416s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 416s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 416s A typical example is when you are setting values in a column of a DataFrame, like: 416s 416s df["col"][row_indexer] = value 416s 416s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 416s 416s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 416s 416s segments.start.iat[0] = bins_start 416s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 416s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 416s A typical example is when you are setting values in a column of a DataFrame, like: 416s 416s df["col"][row_indexer] = value 416s 416s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 416s 416s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 416s 416s segments.end.iat[-1] = bins_end 416s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 416s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 416s A typical example is when you are setting values in a column of a DataFrame, like: 416s 416s df["col"][row_indexer] = value 416s 416s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 416s 416s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 416s 416s segments.start.iat[0] = bins_start 416s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 416s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 416s A typical example is when you are setting values in a column of a DataFrame, like: 416s 416s df["col"][row_indexer] = value 416s 416s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 416s 416s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 416s 416s segments.end.iat[-1] = bins_end 417s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 417s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 417s A typical example is when you are setting values in a column of a DataFrame, like: 417s 417s df["col"][row_indexer] = value 417s 417s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 417s 417s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 417s 417s segments.start.iat[0] = bins_start 417s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 417s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 417s A typical example is when you are setting values in a column of a DataFrame, like: 417s 417s df["col"][row_indexer] = value 417s 417s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 417s 417s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 417s 417s segments.end.iat[-1] = bins_end 417s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 417s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 417s A typical example is when you are setting values in a column of a DataFrame, like: 417s 417s df["col"][row_indexer] = value 417s 417s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 417s 417s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 417s 417s segments.start.iat[0] = bins_start 417s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 417s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 417s A typical example is when you are setting values in a column of a DataFrame, like: 417s 417s df["col"][row_indexer] = value 417s 417s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 417s 417s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 417s 417s segments.end.iat[-1] = bins_end 417s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 417s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 417s A typical example is when you are setting values in a column of a DataFrame, like: 417s 417s df["col"][row_indexer] = value 417s 417s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 417s 417s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 417s 417s segments.start.iat[0] = bins_start 417s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 417s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 417s A typical example is when you are setting values in a column of a DataFrame, like: 417s 417s df["col"][row_indexer] = value 417s 417s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 417s 417s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 417s 417s segments.end.iat[-1] = bins_end 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.start.iat[0] = bins_start 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.end.iat[-1] = bins_end 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.start.iat[0] = bins_start 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.end.iat[-1] = bins_end 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.start.iat[0] = bins_start 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.end.iat[-1] = bins_end 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.start.iat[0] = bins_start 418s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 418s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 418s A typical example is when you are setting values in a column of a DataFrame, like: 418s 418s df["col"][row_indexer] = value 418s 418s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 418s 418s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 418s 418s segments.end.iat[-1] = bins_end 419s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 419s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 419s A typical example is when you are setting values in a column of a DataFrame, like: 419s 419s df["col"][row_indexer] = value 419s 419s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 419s 419s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 419s 419s segments.start.iat[0] = bins_start 419s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 419s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 419s A typical example is when you are setting values in a column of a DataFrame, like: 419s 419s df["col"][row_indexer] = value 419s 419s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 419s 419s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 419s 419s segments.end.iat[-1] = bins_end 419s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 419s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 419s A typical example is when you are setting values in a column of a DataFrame, like: 419s 419s df["col"][row_indexer] = value 419s 419s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 419s 419s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 419s 419s segments.start.iat[0] = bins_start 419s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 419s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 419s A typical example is when you are setting values in a column of a DataFrame, like: 419s 419s df["col"][row_indexer] = value 419s 419s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 419s 419s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 419s 419s segments.end.iat[-1] = bins_end 419s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 419s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 419s A typical example is when you are setting values in a column of a DataFrame, like: 419s 419s df["col"][row_indexer] = value 419s 419s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 419s 419s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 419s 419s segments.start.iat[0] = bins_start 419s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 419s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 419s A typical example is when you are setting values in a column of a DataFrame, like: 419s 419s df["col"][row_indexer] = value 419s 419s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 419s 419s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 419s 419s segments.end.iat[-1] = bins_end 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.start.iat[0] = bins_start 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.end.iat[-1] = bins_end 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.start.iat[0] = bins_start 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.end.iat[-1] = bins_end 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.start.iat[0] = bins_start 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.end.iat[-1] = bins_end 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.start.iat[0] = bins_start 420s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 420s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 420s A typical example is when you are setting values in a column of a DataFrame, like: 420s 420s df["col"][row_indexer] = value 420s 420s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 420s 420s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 420s 420s segments.end.iat[-1] = bins_end 421s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 421s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 421s A typical example is when you are setting values in a column of a DataFrame, like: 421s 421s df["col"][row_indexer] = value 421s 421s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 421s 421s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 421s 421s segments.start.iat[0] = bins_start 421s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 421s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 421s A typical example is when you are setting values in a column of a DataFrame, like: 421s 421s df["col"][row_indexer] = value 421s 421s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 421s 421s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 421s 421s segments.end.iat[-1] = bins_end 421s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 421s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 421s A typical example is when you are setting values in a column of a DataFrame, like: 421s 421s df["col"][row_indexer] = value 421s 421s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 421s 421s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 421s 421s segments.start.iat[0] = bins_start 421s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 421s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 421s A typical example is when you are setting values in a column of a DataFrame, like: 421s 421s df["col"][row_indexer] = value 421s 421s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 421s 421s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 421s 421s segments.end.iat[-1] = bins_end 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.start.iat[0] = bins_start 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.start.iat[0] = bins_start 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.end.iat[-1] = bins_end 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.end.iat[-1] = bins_end 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.start.iat[0] = bins_start 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.end.iat[-1] = bins_end 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.start.iat[0] = bins_start 422s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 422s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 422s A typical example is when you are setting values in a column of a DataFrame, like: 422s 422s df["col"][row_indexer] = value 422s 422s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 422s 422s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 422s 422s segments.end.iat[-1] = bins_end 422s Dropped 27 / 49 bins on chromosome chrY 423s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 423s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 423s A typical example is when you are setting values in a column of a DataFrame, like: 423s 423s df["col"][row_indexer] = value 423s 423s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 423s 423s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 423s 423s segments.start.iat[0] = bins_start 423s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 423s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 423s A typical example is when you are setting values in a column of a DataFrame, like: 423s 423s df["col"][row_indexer] = value 423s 423s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 423s 423s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 423s 423s segments.end.iat[-1] = bins_end 423s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 423s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 423s A typical example is when you are setting values in a column of a DataFrame, like: 423s 423s df["col"][row_indexer] = value 423s 423s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 423s 423s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 423s 423s segments.start.iat[0] = bins_start 423s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 423s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 423s A typical example is when you are setting values in a column of a DataFrame, like: 423s 423s df["col"][row_indexer] = value 423s 423s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 423s 423s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 423s 423s segments.end.iat[-1] = bins_end 423s Wrote build/p2-9_2.cns with 103 regions 424s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_1.cnr -o build/p2-20_1.cns 427s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.start.iat[0] = bins_start 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.end.iat[-1] = bins_end 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.start.iat[0] = bins_start 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.end.iat[-1] = bins_end 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.start.iat[0] = bins_start 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.end.iat[-1] = bins_end 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.start.iat[0] = bins_start 428s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 428s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 428s A typical example is when you are setting values in a column of a DataFrame, like: 428s 428s df["col"][row_indexer] = value 428s 428s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 428s 428s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 428s 428s segments.end.iat[-1] = bins_end 429s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 429s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 429s A typical example is when you are setting values in a column of a DataFrame, like: 429s 429s df["col"][row_indexer] = value 429s 429s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 429s 429s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 429s 429s segments.start.iat[0] = bins_start 429s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 429s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 429s A typical example is when you are setting values in a column of a DataFrame, like: 429s 429s df["col"][row_indexer] = value 429s 429s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 429s 429s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 429s 429s segments.end.iat[-1] = bins_end 429s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 429s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 429s A typical example is when you are setting values in a column of a DataFrame, like: 429s 429s df["col"][row_indexer] = value 429s 429s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 429s 429s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 429s 429s segments.start.iat[0] = bins_start 429s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 429s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 429s A typical example is when you are setting values in a column of a DataFrame, like: 429s 429s df["col"][row_indexer] = value 429s 429s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 429s 429s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 429s 429s segments.end.iat[-1] = bins_end 429s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 429s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 429s A typical example is when you are setting values in a column of a DataFrame, like: 429s 429s df["col"][row_indexer] = value 429s 429s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 429s 429s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 429s 429s segments.start.iat[0] = bins_start 429s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 429s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 429s A typical example is when you are setting values in a column of a DataFrame, like: 429s 429s df["col"][row_indexer] = value 429s 429s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 429s 429s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 429s 429s segments.end.iat[-1] = bins_end 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.start.iat[0] = bins_start 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.end.iat[-1] = bins_end 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.start.iat[0] = bins_start 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.end.iat[-1] = bins_end 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.start.iat[0] = bins_start 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.end.iat[-1] = bins_end 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.start.iat[0] = bins_start 430s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 430s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 430s A typical example is when you are setting values in a column of a DataFrame, like: 430s 430s df["col"][row_indexer] = value 430s 430s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 430s 430s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 430s 430s segments.end.iat[-1] = bins_end 431s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 431s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 431s A typical example is when you are setting values in a column of a DataFrame, like: 431s 431s df["col"][row_indexer] = value 431s 431s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 431s 431s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 431s 431s segments.start.iat[0] = bins_start 431s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 431s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 431s A typical example is when you are setting values in a column of a DataFrame, like: 431s 431s df["col"][row_indexer] = value 431s 431s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 431s 431s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 431s 431s segments.end.iat[-1] = bins_end 431s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 431s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 431s A typical example is when you are setting values in a column of a DataFrame, like: 431s 431s df["col"][row_indexer] = value 431s 431s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 431s 431s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 431s 431s segments.start.iat[0] = bins_start 431s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 431s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 431s A typical example is when you are setting values in a column of a DataFrame, like: 431s 431s df["col"][row_indexer] = value 431s 431s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 431s 431s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 431s 431s segments.end.iat[-1] = bins_end 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.start.iat[0] = bins_start 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.end.iat[-1] = bins_end 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.start.iat[0] = bins_start 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.end.iat[-1] = bins_end 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.start.iat[0] = bins_start 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.end.iat[-1] = bins_end 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.start.iat[0] = bins_start 432s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 432s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 432s A typical example is when you are setting values in a column of a DataFrame, like: 432s 432s df["col"][row_indexer] = value 432s 432s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 432s 432s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 432s 432s segments.end.iat[-1] = bins_end 433s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 433s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 433s A typical example is when you are setting values in a column of a DataFrame, like: 433s 433s df["col"][row_indexer] = value 433s 433s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 433s 433s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 433s 433s segments.start.iat[0] = bins_start 433s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 433s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 433s A typical example is when you are setting values in a column of a DataFrame, like: 433s 433s df["col"][row_indexer] = value 433s 433s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 433s 433s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 433s 433s segments.end.iat[-1] = bins_end 433s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 433s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 433s A typical example is when you are setting values in a column of a DataFrame, like: 433s 433s df["col"][row_indexer] = value 433s 433s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 433s 433s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 433s 433s segments.start.iat[0] = bins_start 433s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 433s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 433s A typical example is when you are setting values in a column of a DataFrame, like: 433s 433s df["col"][row_indexer] = value 433s 433s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 433s 433s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 433s 433s segments.end.iat[-1] = bins_end 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.start.iat[0] = bins_start 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.end.iat[-1] = bins_end 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.start.iat[0] = bins_start 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.end.iat[-1] = bins_end 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.start.iat[0] = bins_start 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.end.iat[-1] = bins_end 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.start.iat[0] = bins_start 434s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 434s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 434s A typical example is when you are setting values in a column of a DataFrame, like: 434s 434s df["col"][row_indexer] = value 434s 434s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 434s 434s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 434s 434s segments.end.iat[-1] = bins_end 435s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 435s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 435s A typical example is when you are setting values in a column of a DataFrame, like: 435s 435s df["col"][row_indexer] = value 435s 435s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 435s 435s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 435s 435s segments.start.iat[0] = bins_start 435s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 435s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 435s A typical example is when you are setting values in a column of a DataFrame, like: 435s 435s df["col"][row_indexer] = value 435s 435s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 435s 435s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 435s 435s segments.end.iat[-1] = bins_end 435s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 435s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 435s A typical example is when you are setting values in a column of a DataFrame, like: 435s 435s df["col"][row_indexer] = value 435s 435s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 435s 435s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 435s 435s segments.start.iat[0] = bins_start 435s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 435s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 435s A typical example is when you are setting values in a column of a DataFrame, like: 435s 435s df["col"][row_indexer] = value 435s 435s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 435s 435s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 435s 435s segments.end.iat[-1] = bins_end 436s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 436s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 436s A typical example is when you are setting values in a column of a DataFrame, like: 436s 436s df["col"][row_indexer] = value 436s 436s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 436s 436s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 436s 436s segments.start.iat[0] = bins_start 436s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 436s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 436s A typical example is when you are setting values in a column of a DataFrame, like: 436s 436s df["col"][row_indexer] = value 436s 436s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 436s 436s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 436s 436s segments.end.iat[-1] = bins_end 436s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 436s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 436s A typical example is when you are setting values in a column of a DataFrame, like: 436s 436s df["col"][row_indexer] = value 436s 436s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 436s 436s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 436s 436s segments.start.iat[0] = bins_start 436s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 436s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 436s A typical example is when you are setting values in a column of a DataFrame, like: 436s 436s df["col"][row_indexer] = value 436s 436s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 436s 436s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 436s 436s segments.end.iat[-1] = bins_end 436s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 436s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 436s A typical example is when you are setting values in a column of a DataFrame, like: 436s 436s df["col"][row_indexer] = value 436s 436s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 436s 436s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 436s 436s segments.start.iat[0] = bins_start 436s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 436s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 436s A typical example is when you are setting values in a column of a DataFrame, like: 436s 436s df["col"][row_indexer] = value 436s 436s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 436s 436s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 436s 436s segments.end.iat[-1] = bins_end 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.start.iat[0] = bins_start 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.end.iat[-1] = bins_end 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.start.iat[0] = bins_start 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.end.iat[-1] = bins_end 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.start.iat[0] = bins_start 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.end.iat[-1] = bins_end 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.start.iat[0] = bins_start 437s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 437s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 437s A typical example is when you are setting values in a column of a DataFrame, like: 437s 437s df["col"][row_indexer] = value 437s 437s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 437s 437s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 437s 437s segments.end.iat[-1] = bins_end 438s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 438s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 438s A typical example is when you are setting values in a column of a DataFrame, like: 438s 438s df["col"][row_indexer] = value 438s 438s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 438s 438s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 438s 438s segments.start.iat[0] = bins_start 438s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 438s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 438s A typical example is when you are setting values in a column of a DataFrame, like: 438s 438s df["col"][row_indexer] = value 438s 438s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 438s 438s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 438s 438s segments.end.iat[-1] = bins_end 438s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 438s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 438s A typical example is when you are setting values in a column of a DataFrame, like: 438s 438s df["col"][row_indexer] = value 438s 438s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 438s 438s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 438s 438s segments.start.iat[0] = bins_start 438s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 438s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 438s A typical example is when you are setting values in a column of a DataFrame, like: 438s 438s df["col"][row_indexer] = value 438s 438s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 438s 438s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 438s 438s segments.end.iat[-1] = bins_end 438s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 438s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 438s A typical example is when you are setting values in a column of a DataFrame, like: 438s 438s df["col"][row_indexer] = value 438s 438s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 438s 438s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 438s 438s segments.start.iat[0] = bins_start 438s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 438s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 438s A typical example is when you are setting values in a column of a DataFrame, like: 438s 438s df["col"][row_indexer] = value 438s 438s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 438s 438s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 438s 438s segments.end.iat[-1] = bins_end 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.start.iat[0] = bins_start 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.end.iat[-1] = bins_end 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.start.iat[0] = bins_start 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.end.iat[-1] = bins_end 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.start.iat[0] = bins_start 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.end.iat[-1] = bins_end 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.start.iat[0] = bins_start 439s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 439s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 439s A typical example is when you are setting values in a column of a DataFrame, like: 439s 439s df["col"][row_indexer] = value 439s 439s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 439s 439s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 439s 439s segments.end.iat[-1] = bins_end 440s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 440s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 440s A typical example is when you are setting values in a column of a DataFrame, like: 440s 440s df["col"][row_indexer] = value 440s 440s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 440s 440s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 440s 440s segments.start.iat[0] = bins_start 440s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 440s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 440s A typical example is when you are setting values in a column of a DataFrame, like: 440s 440s df["col"][row_indexer] = value 440s 440s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 440s 440s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 440s 440s segments.end.iat[-1] = bins_end 440s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 440s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 440s A typical example is when you are setting values in a column of a DataFrame, like: 440s 440s df["col"][row_indexer] = value 440s 440s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 440s 440s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 440s 440s segments.start.iat[0] = bins_start 440s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 440s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 440s A typical example is when you are setting values in a column of a DataFrame, like: 440s 440s df["col"][row_indexer] = value 440s 440s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 440s 440s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 440s 440s segments.end.iat[-1] = bins_end 440s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 440s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 440s A typical example is when you are setting values in a column of a DataFrame, like: 440s 440s df["col"][row_indexer] = value 440s 440s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 440s 440s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 440s 440s segments.start.iat[0] = bins_start 440s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 440s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 440s A typical example is when you are setting values in a column of a DataFrame, like: 440s 440s df["col"][row_indexer] = value 440s 440s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 440s 440s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 440s 440s segments.end.iat[-1] = bins_end 441s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 441s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 441s A typical example is when you are setting values in a column of a DataFrame, like: 441s 441s df["col"][row_indexer] = value 441s 441s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 441s 441s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 441s 441s segments.start.iat[0] = bins_start 441s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 441s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 441s A typical example is when you are setting values in a column of a DataFrame, like: 441s 441s df["col"][row_indexer] = value 441s 441s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 441s 441s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 441s 441s segments.end.iat[-1] = bins_end 441s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 441s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 441s A typical example is when you are setting values in a column of a DataFrame, like: 441s 441s df["col"][row_indexer] = value 441s 441s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 441s 441s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 441s 441s segments.start.iat[0] = bins_start 441s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 441s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 441s A typical example is when you are setting values in a column of a DataFrame, like: 441s 441s df["col"][row_indexer] = value 441s 441s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 441s 441s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 441s 441s segments.end.iat[-1] = bins_end 441s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 441s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 441s A typical example is when you are setting values in a column of a DataFrame, like: 441s 441s df["col"][row_indexer] = value 441s 441s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 441s 441s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 441s 441s segments.start.iat[0] = bins_start 441s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 441s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 441s A typical example is when you are setting values in a column of a DataFrame, like: 441s 441s df["col"][row_indexer] = value 441s 441s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 441s 441s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 441s 441s segments.end.iat[-1] = bins_end 441s Dropped 6 / 49 bins on chromosome chrY 442s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 442s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 442s A typical example is when you are setting values in a column of a DataFrame, like: 442s 442s df["col"][row_indexer] = value 442s 442s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 442s 442s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 442s 442s segments.start.iat[0] = bins_start 442s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 442s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 442s A typical example is when you are setting values in a column of a DataFrame, like: 442s 442s df["col"][row_indexer] = value 442s 442s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 442s 442s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 442s 442s segments.end.iat[-1] = bins_end 442s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 442s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 442s A typical example is when you are setting values in a column of a DataFrame, like: 442s 442s df["col"][row_indexer] = value 442s 442s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 442s 442s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 442s 442s segments.start.iat[0] = bins_start 442s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 442s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 442s A typical example is when you are setting values in a column of a DataFrame, like: 442s 442s df["col"][row_indexer] = value 442s 442s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 442s 442s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 442s 442s segments.end.iat[-1] = bins_end 442s Wrote build/p2-20_1.cns with 121 regions 443s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_2.cnr -o build/p2-20_2.cns 446s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 447s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 447s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 447s A typical example is when you are setting values in a column of a DataFrame, like: 447s 447s df["col"][row_indexer] = value 447s 447s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 447s 447s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 447s 447s segments.start.iat[0] = bins_start 447s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 447s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 447s A typical example is when you are setting values in a column of a DataFrame, like: 447s 447s df["col"][row_indexer] = value 447s 447s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 447s 447s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 447s 447s segments.end.iat[-1] = bins_end 447s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 447s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 447s A typical example is when you are setting values in a column of a DataFrame, like: 447s 447s df["col"][row_indexer] = value 447s 447s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 447s 447s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 447s 447s segments.start.iat[0] = bins_start 447s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 447s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 447s A typical example is when you are setting values in a column of a DataFrame, like: 447s 447s df["col"][row_indexer] = value 447s 447s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 447s 447s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 447s 447s segments.end.iat[-1] = bins_end 447s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 447s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 447s A typical example is when you are setting values in a column of a DataFrame, like: 447s 447s df["col"][row_indexer] = value 447s 447s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 447s 447s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 447s 447s segments.start.iat[0] = bins_start 447s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 447s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 447s A typical example is when you are setting values in a column of a DataFrame, like: 447s 447s df["col"][row_indexer] = value 447s 447s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 447s 447s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 447s 447s segments.end.iat[-1] = bins_end 448s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 448s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 448s A typical example is when you are setting values in a column of a DataFrame, like: 448s 448s df["col"][row_indexer] = value 448s 448s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 448s 448s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 448s 448s segments.start.iat[0] = bins_start 448s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 448s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 448s A typical example is when you are setting values in a column of a DataFrame, like: 448s 448s df["col"][row_indexer] = value 448s 448s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 448s 448s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 448s 448s segments.end.iat[-1] = bins_end 448s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 448s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 448s A typical example is when you are setting values in a column of a DataFrame, like: 448s 448s df["col"][row_indexer] = value 448s 448s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 448s 448s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 448s 448s segments.start.iat[0] = bins_start 448s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 448s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 448s A typical example is when you are setting values in a column of a DataFrame, like: 448s 448s df["col"][row_indexer] = value 448s 448s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 448s 448s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 448s 448s segments.end.iat[-1] = bins_end 448s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 448s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 448s A typical example is when you are setting values in a column of a DataFrame, like: 448s 448s df["col"][row_indexer] = value 448s 448s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 448s 448s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 448s 448s segments.start.iat[0] = bins_start 448s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 448s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 448s A typical example is when you are setting values in a column of a DataFrame, like: 448s 448s df["col"][row_indexer] = value 448s 448s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 448s 448s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 448s 448s segments.end.iat[-1] = bins_end 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.start.iat[0] = bins_start 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.end.iat[-1] = bins_end 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.start.iat[0] = bins_start 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.end.iat[-1] = bins_end 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.start.iat[0] = bins_start 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.end.iat[-1] = bins_end 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.start.iat[0] = bins_start 449s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 449s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 449s A typical example is when you are setting values in a column of a DataFrame, like: 449s 449s df["col"][row_indexer] = value 449s 449s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 449s 449s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 449s 449s segments.end.iat[-1] = bins_end 450s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 450s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 450s A typical example is when you are setting values in a column of a DataFrame, like: 450s 450s df["col"][row_indexer] = value 450s 450s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 450s 450s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 450s 450s segments.start.iat[0] = bins_start 450s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 450s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 450s A typical example is when you are setting values in a column of a DataFrame, like: 450s 450s df["col"][row_indexer] = value 450s 450s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 450s 450s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 450s 450s segments.end.iat[-1] = bins_end 450s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 450s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 450s A typical example is when you are setting values in a column of a DataFrame, like: 450s 450s df["col"][row_indexer] = value 450s 450s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 450s 450s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 450s 450s segments.start.iat[0] = bins_start 450s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 450s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 450s A typical example is when you are setting values in a column of a DataFrame, like: 450s 450s df["col"][row_indexer] = value 450s 450s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 450s 450s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 450s 450s segments.end.iat[-1] = bins_end 450s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 450s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 450s A typical example is when you are setting values in a column of a DataFrame, like: 450s 450s df["col"][row_indexer] = value 450s 450s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 450s 450s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 450s 450s segments.start.iat[0] = bins_start 450s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 450s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 450s A typical example is when you are setting values in a column of a DataFrame, like: 450s 450s df["col"][row_indexer] = value 450s 450s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 450s 450s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 450s 450s segments.end.iat[-1] = bins_end 451s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 451s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 451s A typical example is when you are setting values in a column of a DataFrame, like: 451s 451s df["col"][row_indexer] = value 451s 451s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 451s 451s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 451s 451s segments.start.iat[0] = bins_start 451s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 451s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 451s A typical example is when you are setting values in a column of a DataFrame, like: 451s 451s df["col"][row_indexer] = value 451s 451s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 451s 451s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 451s 451s segments.end.iat[-1] = bins_end 451s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 451s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 451s A typical example is when you are setting values in a column of a DataFrame, like: 451s 451s df["col"][row_indexer] = value 451s 451s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 451s 451s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 451s 451s segments.start.iat[0] = bins_start 451s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 451s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 451s A typical example is when you are setting values in a column of a DataFrame, like: 451s 451s df["col"][row_indexer] = value 451s 451s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 451s 451s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 451s 451s segments.end.iat[-1] = bins_end 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.start.iat[0] = bins_start 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.end.iat[-1] = bins_end 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.start.iat[0] = bins_start 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.end.iat[-1] = bins_end 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.start.iat[0] = bins_start 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.end.iat[-1] = bins_end 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.start.iat[0] = bins_start 452s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 452s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 452s A typical example is when you are setting values in a column of a DataFrame, like: 452s 452s df["col"][row_indexer] = value 452s 452s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 452s 452s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 452s 452s segments.end.iat[-1] = bins_end 453s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 453s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 453s A typical example is when you are setting values in a column of a DataFrame, like: 453s 453s df["col"][row_indexer] = value 453s 453s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 453s 453s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 453s 453s segments.start.iat[0] = bins_start 453s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 453s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 453s A typical example is when you are setting values in a column of a DataFrame, like: 453s 453s df["col"][row_indexer] = value 453s 453s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 453s 453s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 453s 453s segments.end.iat[-1] = bins_end 453s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 453s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 453s A typical example is when you are setting values in a column of a DataFrame, like: 453s 453s df["col"][row_indexer] = value 453s 453s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 453s 453s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 453s 453s segments.start.iat[0] = bins_start 453s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 453s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 453s A typical example is when you are setting values in a column of a DataFrame, like: 453s 453s df["col"][row_indexer] = value 453s 453s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 453s 453s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 453s 453s segments.end.iat[-1] = bins_end 453s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 453s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 453s A typical example is when you are setting values in a column of a DataFrame, like: 453s 453s df["col"][row_indexer] = value 453s 453s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 453s 453s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 453s 453s segments.start.iat[0] = bins_start 453s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 453s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 453s A typical example is when you are setting values in a column of a DataFrame, like: 453s 453s df["col"][row_indexer] = value 453s 453s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 453s 453s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 453s 453s segments.end.iat[-1] = bins_end 454s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 454s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 454s A typical example is when you are setting values in a column of a DataFrame, like: 454s 454s df["col"][row_indexer] = value 454s 454s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 454s 454s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 454s 454s segments.start.iat[0] = bins_start 454s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 454s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 454s A typical example is when you are setting values in a column of a DataFrame, like: 454s 454s df["col"][row_indexer] = value 454s 454s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 454s 454s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 454s 454s segments.end.iat[-1] = bins_end 454s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 454s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 454s A typical example is when you are setting values in a column of a DataFrame, like: 454s 454s df["col"][row_indexer] = value 454s 454s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 454s 454s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 454s 454s segments.start.iat[0] = bins_start 454s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 454s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 454s A typical example is when you are setting values in a column of a DataFrame, like: 454s 454s df["col"][row_indexer] = value 454s 454s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 454s 454s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 454s 454s segments.end.iat[-1] = bins_end 454s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 454s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 454s A typical example is when you are setting values in a column of a DataFrame, like: 454s 454s df["col"][row_indexer] = value 454s 454s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 454s 454s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 454s 454s segments.start.iat[0] = bins_start 454s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 454s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 454s A typical example is when you are setting values in a column of a DataFrame, like: 454s 454s df["col"][row_indexer] = value 454s 454s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 454s 454s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 454s 454s segments.end.iat[-1] = bins_end 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.start.iat[0] = bins_start 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.end.iat[-1] = bins_end 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.start.iat[0] = bins_start 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.end.iat[-1] = bins_end 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.start.iat[0] = bins_start 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.end.iat[-1] = bins_end 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.start.iat[0] = bins_start 455s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 455s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 455s A typical example is when you are setting values in a column of a DataFrame, like: 455s 455s df["col"][row_indexer] = value 455s 455s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 455s 455s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 455s 455s segments.end.iat[-1] = bins_end 456s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 456s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 456s A typical example is when you are setting values in a column of a DataFrame, like: 456s 456s df["col"][row_indexer] = value 456s 456s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 456s 456s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 456s 456s segments.start.iat[0] = bins_start 456s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 456s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 456s A typical example is when you are setting values in a column of a DataFrame, like: 456s 456s df["col"][row_indexer] = value 456s 456s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 456s 456s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 456s 456s segments.end.iat[-1] = bins_end 456s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 456s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 456s A typical example is when you are setting values in a column of a DataFrame, like: 456s 456s df["col"][row_indexer] = value 456s 456s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 456s 456s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 456s 456s segments.start.iat[0] = bins_start 456s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 456s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 456s A typical example is when you are setting values in a column of a DataFrame, like: 456s 456s df["col"][row_indexer] = value 456s 456s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 456s 456s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 456s 456s segments.end.iat[-1] = bins_end 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.start.iat[0] = bins_start 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.end.iat[-1] = bins_end 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.start.iat[0] = bins_start 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.end.iat[-1] = bins_end 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.start.iat[0] = bins_start 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.end.iat[-1] = bins_end 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.start.iat[0] = bins_start 457s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 457s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 457s A typical example is when you are setting values in a column of a DataFrame, like: 457s 457s df["col"][row_indexer] = value 457s 457s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 457s 457s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 457s 457s segments.end.iat[-1] = bins_end 458s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 458s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 458s A typical example is when you are setting values in a column of a DataFrame, like: 458s 458s df["col"][row_indexer] = value 458s 458s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 458s 458s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 458s 458s segments.start.iat[0] = bins_start 458s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 458s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 458s A typical example is when you are setting values in a column of a DataFrame, like: 458s 458s df["col"][row_indexer] = value 458s 458s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 458s 458s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 458s 458s segments.end.iat[-1] = bins_end 458s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 458s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 458s A typical example is when you are setting values in a column of a DataFrame, like: 458s 458s df["col"][row_indexer] = value 458s 458s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 458s 458s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 458s 458s segments.start.iat[0] = bins_start 458s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 458s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 458s A typical example is when you are setting values in a column of a DataFrame, like: 458s 458s df["col"][row_indexer] = value 458s 458s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 458s 458s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 458s 458s segments.end.iat[-1] = bins_end 458s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 458s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 458s A typical example is when you are setting values in a column of a DataFrame, like: 458s 458s df["col"][row_indexer] = value 458s 458s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 458s 458s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 458s 458s segments.start.iat[0] = bins_start 458s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 458s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 458s A typical example is when you are setting values in a column of a DataFrame, like: 458s 458s df["col"][row_indexer] = value 458s 458s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 458s 458s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 458s 458s segments.end.iat[-1] = bins_end 459s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 459s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 459s A typical example is when you are setting values in a column of a DataFrame, like: 459s 459s df["col"][row_indexer] = value 459s 459s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 459s 459s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 459s 459s segments.start.iat[0] = bins_start 459s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 459s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 459s A typical example is when you are setting values in a column of a DataFrame, like: 459s 459s df["col"][row_indexer] = value 459s 459s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 459s 459s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 459s 459s segments.end.iat[-1] = bins_end 459s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 459s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 459s A typical example is when you are setting values in a column of a DataFrame, like: 459s 459s df["col"][row_indexer] = value 459s 459s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 459s 459s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 459s 459s segments.start.iat[0] = bins_start 459s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 459s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 459s A typical example is when you are setting values in a column of a DataFrame, like: 459s 459s df["col"][row_indexer] = value 459s 459s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 459s 459s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 459s 459s segments.end.iat[-1] = bins_end 459s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 459s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 459s A typical example is when you are setting values in a column of a DataFrame, like: 459s 459s df["col"][row_indexer] = value 459s 459s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 459s 459s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 459s 459s segments.start.iat[0] = bins_start 459s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 459s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 459s A typical example is when you are setting values in a column of a DataFrame, like: 459s 459s df["col"][row_indexer] = value 459s 459s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 459s 459s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 459s 459s segments.end.iat[-1] = bins_end 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.start.iat[0] = bins_start 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.end.iat[-1] = bins_end 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.start.iat[0] = bins_start 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.end.iat[-1] = bins_end 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.start.iat[0] = bins_start 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.end.iat[-1] = bins_end 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.start.iat[0] = bins_start 460s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 460s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 460s A typical example is when you are setting values in a column of a DataFrame, like: 460s 460s df["col"][row_indexer] = value 460s 460s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 460s 460s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 460s 460s segments.end.iat[-1] = bins_end 460s Dropped 7 / 49 bins on chromosome chrY 461s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 461s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 461s A typical example is when you are setting values in a column of a DataFrame, like: 461s 461s df["col"][row_indexer] = value 461s 461s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 461s 461s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 461s 461s segments.start.iat[0] = bins_start 461s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 461s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 461s A typical example is when you are setting values in a column of a DataFrame, like: 461s 461s df["col"][row_indexer] = value 461s 461s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 461s 461s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 461s 461s segments.end.iat[-1] = bins_end 461s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 461s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 461s A typical example is when you are setting values in a column of a DataFrame, like: 461s 461s df["col"][row_indexer] = value 461s 461s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 461s 461s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 461s 461s segments.start.iat[0] = bins_start 461s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 461s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 461s A typical example is when you are setting values in a column of a DataFrame, like: 461s 461s df["col"][row_indexer] = value 461s 461s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 461s 461s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 461s 461s segments.end.iat[-1] = bins_end 461s Wrote build/p2-20_2.cns with 117 regions 462s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_3.cnr -o build/p2-20_3.cns 465s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.start.iat[0] = bins_start 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.end.iat[-1] = bins_end 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.start.iat[0] = bins_start 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.end.iat[-1] = bins_end 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.start.iat[0] = bins_start 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.end.iat[-1] = bins_end 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.start.iat[0] = bins_start 466s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 466s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 466s A typical example is when you are setting values in a column of a DataFrame, like: 466s 466s df["col"][row_indexer] = value 466s 466s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 466s 466s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 466s 466s segments.end.iat[-1] = bins_end 467s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 467s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 467s A typical example is when you are setting values in a column of a DataFrame, like: 467s 467s df["col"][row_indexer] = value 467s 467s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 467s 467s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 467s 467s segments.start.iat[0] = bins_start 467s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 467s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 467s A typical example is when you are setting values in a column of a DataFrame, like: 467s 467s df["col"][row_indexer] = value 467s 467s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 467s 467s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 467s 467s segments.end.iat[-1] = bins_end 467s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 467s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 467s A typical example is when you are setting values in a column of a DataFrame, like: 467s 467s df["col"][row_indexer] = value 467s 467s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 467s 467s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 467s 467s segments.start.iat[0] = bins_start 467s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 467s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 467s A typical example is when you are setting values in a column of a DataFrame, like: 467s 467s df["col"][row_indexer] = value 467s 467s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 467s 467s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 467s 467s segments.end.iat[-1] = bins_end 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.start.iat[0] = bins_start 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.end.iat[-1] = bins_end 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.start.iat[0] = bins_start 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.end.iat[-1] = bins_end 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.start.iat[0] = bins_start 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.end.iat[-1] = bins_end 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.start.iat[0] = bins_start 468s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 468s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 468s A typical example is when you are setting values in a column of a DataFrame, like: 468s 468s df["col"][row_indexer] = value 468s 468s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 468s 468s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 468s 468s segments.end.iat[-1] = bins_end 469s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 469s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 469s A typical example is when you are setting values in a column of a DataFrame, like: 469s 469s df["col"][row_indexer] = value 469s 469s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 469s 469s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 469s 469s segments.start.iat[0] = bins_start 469s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 469s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 469s A typical example is when you are setting values in a column of a DataFrame, like: 469s 469s df["col"][row_indexer] = value 469s 469s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 469s 469s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 469s 469s segments.end.iat[-1] = bins_end 469s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 469s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 469s A typical example is when you are setting values in a column of a DataFrame, like: 469s 469s df["col"][row_indexer] = value 469s 469s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 469s 469s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 469s 469s segments.start.iat[0] = bins_start 469s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 469s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 469s A typical example is when you are setting values in a column of a DataFrame, like: 469s 469s df["col"][row_indexer] = value 469s 469s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 469s 469s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 469s 469s segments.end.iat[-1] = bins_end 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.start.iat[0] = bins_start 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.end.iat[-1] = bins_end 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.start.iat[0] = bins_start 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.end.iat[-1] = bins_end 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.start.iat[0] = bins_start 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.end.iat[-1] = bins_end 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.start.iat[0] = bins_start 470s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 470s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 470s A typical example is when you are setting values in a column of a DataFrame, like: 470s 470s df["col"][row_indexer] = value 470s 470s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 470s 470s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 470s 470s segments.end.iat[-1] = bins_end 471s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 471s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 471s A typical example is when you are setting values in a column of a DataFrame, like: 471s 471s df["col"][row_indexer] = value 471s 471s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 471s 471s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 471s 471s segments.start.iat[0] = bins_start 471s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 471s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 471s A typical example is when you are setting values in a column of a DataFrame, like: 471s 471s df["col"][row_indexer] = value 471s 471s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 471s 471s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 471s 471s segments.end.iat[-1] = bins_end 471s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 471s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 471s A typical example is when you are setting values in a column of a DataFrame, like: 471s 471s df["col"][row_indexer] = value 471s 471s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 471s 471s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 471s 471s segments.start.iat[0] = bins_start 471s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 471s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 471s A typical example is when you are setting values in a column of a DataFrame, like: 471s 471s df["col"][row_indexer] = value 471s 471s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 471s 471s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 471s 471s segments.end.iat[-1] = bins_end 471s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 471s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 471s A typical example is when you are setting values in a column of a DataFrame, like: 471s 471s df["col"][row_indexer] = value 471s 471s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 471s 471s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 471s 471s segments.start.iat[0] = bins_start 471s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 471s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 471s A typical example is when you are setting values in a column of a DataFrame, like: 471s 471s df["col"][row_indexer] = value 471s 471s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 471s 471s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 471s 471s segments.end.iat[-1] = bins_end 472s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 472s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 472s A typical example is when you are setting values in a column of a DataFrame, like: 472s 472s df["col"][row_indexer] = value 472s 472s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 472s 472s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 472s 472s segments.start.iat[0] = bins_start 472s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 472s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 472s A typical example is when you are setting values in a column of a DataFrame, like: 472s 472s df["col"][row_indexer] = value 472s 472s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 472s 472s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 472s 472s segments.end.iat[-1] = bins_end 472s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 472s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 472s A typical example is when you are setting values in a column of a DataFrame, like: 472s 472s df["col"][row_indexer] = value 472s 472s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 472s 472s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 472s 472s segments.start.iat[0] = bins_start 472s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 472s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 472s A typical example is when you are setting values in a column of a DataFrame, like: 472s 472s df["col"][row_indexer] = value 472s 472s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 472s 472s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 472s 472s segments.end.iat[-1] = bins_end 472s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 472s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 472s A typical example is when you are setting values in a column of a DataFrame, like: 472s 472s df["col"][row_indexer] = value 472s 472s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 472s 472s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 472s 472s segments.start.iat[0] = bins_start 472s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 472s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 472s A typical example is when you are setting values in a column of a DataFrame, like: 472s 472s df["col"][row_indexer] = value 472s 472s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 472s 472s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 472s 472s segments.end.iat[-1] = bins_end 473s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 473s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 473s A typical example is when you are setting values in a column of a DataFrame, like: 473s 473s df["col"][row_indexer] = value 473s 473s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 473s 473s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 473s 473s segments.start.iat[0] = bins_start 473s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 473s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 473s A typical example is when you are setting values in a column of a DataFrame, like: 473s 473s df["col"][row_indexer] = value 473s 473s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 473s 473s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 473s 473s segments.end.iat[-1] = bins_end 473s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 473s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 473s A typical example is when you are setting values in a column of a DataFrame, like: 473s 473s df["col"][row_indexer] = value 473s 473s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 473s 473s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 473s 473s segments.start.iat[0] = bins_start 473s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 473s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 473s A typical example is when you are setting values in a column of a DataFrame, like: 473s 473s df["col"][row_indexer] = value 473s 473s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 473s 473s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 473s 473s segments.end.iat[-1] = bins_end 473s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 473s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 473s A typical example is when you are setting values in a column of a DataFrame, like: 473s 473s df["col"][row_indexer] = value 473s 473s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 473s 473s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 473s 473s segments.start.iat[0] = bins_start 473s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 473s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 473s A typical example is when you are setting values in a column of a DataFrame, like: 473s 473s df["col"][row_indexer] = value 473s 473s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 473s 473s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 473s 473s segments.end.iat[-1] = bins_end 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.start.iat[0] = bins_start 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.end.iat[-1] = bins_end 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.start.iat[0] = bins_start 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.end.iat[-1] = bins_end 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.start.iat[0] = bins_start 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.end.iat[-1] = bins_end 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.start.iat[0] = bins_start 474s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 474s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 474s A typical example is when you are setting values in a column of a DataFrame, like: 474s 474s df["col"][row_indexer] = value 474s 474s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 474s 474s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 474s 474s segments.end.iat[-1] = bins_end 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.start.iat[0] = bins_start 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.end.iat[-1] = bins_end 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.start.iat[0] = bins_start 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.end.iat[-1] = bins_end 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.start.iat[0] = bins_start 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.end.iat[-1] = bins_end 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.start.iat[0] = bins_start 475s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 475s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 475s A typical example is when you are setting values in a column of a DataFrame, like: 475s 475s df["col"][row_indexer] = value 475s 475s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 475s 475s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 475s 475s segments.end.iat[-1] = bins_end 476s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 476s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 476s A typical example is when you are setting values in a column of a DataFrame, like: 476s 476s df["col"][row_indexer] = value 476s 476s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 476s 476s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 476s 476s segments.start.iat[0] = bins_start 476s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 476s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 476s A typical example is when you are setting values in a column of a DataFrame, like: 476s 476s df["col"][row_indexer] = value 476s 476s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 476s 476s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 476s 476s segments.end.iat[-1] = bins_end 476s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 476s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 476s A typical example is when you are setting values in a column of a DataFrame, like: 476s 476s df["col"][row_indexer] = value 476s 476s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 476s 476s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 476s 476s segments.start.iat[0] = bins_start 476s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 476s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 476s A typical example is when you are setting values in a column of a DataFrame, like: 476s 476s df["col"][row_indexer] = value 476s 476s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 476s 476s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 476s 476s segments.end.iat[-1] = bins_end 476s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 476s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 476s A typical example is when you are setting values in a column of a DataFrame, like: 476s 476s df["col"][row_indexer] = value 476s 476s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 476s 476s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 476s 476s segments.start.iat[0] = bins_start 476s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 476s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 476s A typical example is when you are setting values in a column of a DataFrame, like: 476s 476s df["col"][row_indexer] = value 476s 476s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 476s 476s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 476s 476s segments.end.iat[-1] = bins_end 477s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 477s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 477s A typical example is when you are setting values in a column of a DataFrame, like: 477s 477s df["col"][row_indexer] = value 477s 477s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 477s 477s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 477s 477s segments.start.iat[0] = bins_start 477s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 477s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 477s A typical example is when you are setting values in a column of a DataFrame, like: 477s 477s df["col"][row_indexer] = value 477s 477s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 477s 477s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 477s 477s segments.end.iat[-1] = bins_end 477s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 477s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 477s A typical example is when you are setting values in a column of a DataFrame, like: 477s 477s df["col"][row_indexer] = value 477s 477s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 477s 477s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 477s 477s segments.start.iat[0] = bins_start 477s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 477s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 477s A typical example is when you are setting values in a column of a DataFrame, like: 477s 477s df["col"][row_indexer] = value 477s 477s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 477s 477s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 477s 477s segments.end.iat[-1] = bins_end 477s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 477s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 477s A typical example is when you are setting values in a column of a DataFrame, like: 477s 477s df["col"][row_indexer] = value 477s 477s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 477s 477s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 477s 477s segments.start.iat[0] = bins_start 477s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 477s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 477s A typical example is when you are setting values in a column of a DataFrame, like: 477s 477s df["col"][row_indexer] = value 477s 477s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 477s 477s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 477s 477s segments.end.iat[-1] = bins_end 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.start.iat[0] = bins_start 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.end.iat[-1] = bins_end 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.start.iat[0] = bins_start 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.end.iat[-1] = bins_end 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.start.iat[0] = bins_start 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.end.iat[-1] = bins_end 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.start.iat[0] = bins_start 478s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 478s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 478s A typical example is when you are setting values in a column of a DataFrame, like: 478s 478s df["col"][row_indexer] = value 478s 478s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 478s 478s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 478s 478s segments.end.iat[-1] = bins_end 479s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 479s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 479s A typical example is when you are setting values in a column of a DataFrame, like: 479s 479s df["col"][row_indexer] = value 479s 479s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 479s 479s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 479s 479s segments.start.iat[0] = bins_start 479s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 479s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 479s A typical example is when you are setting values in a column of a DataFrame, like: 479s 479s df["col"][row_indexer] = value 479s 479s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 479s 479s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 479s 479s segments.end.iat[-1] = bins_end 479s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 479s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 479s A typical example is when you are setting values in a column of a DataFrame, like: 479s 479s df["col"][row_indexer] = value 479s 479s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 479s 479s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 479s 479s segments.start.iat[0] = bins_start 479s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 479s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 479s A typical example is when you are setting values in a column of a DataFrame, like: 479s 479s df["col"][row_indexer] = value 479s 479s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 479s 479s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 479s 479s segments.end.iat[-1] = bins_end 479s Dropped 11 / 49 bins on chromosome chrY 480s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 480s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 480s A typical example is when you are setting values in a column of a DataFrame, like: 480s 480s df["col"][row_indexer] = value 480s 480s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 480s 480s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 480s 480s segments.start.iat[0] = bins_start 480s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 480s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 480s A typical example is when you are setting values in a column of a DataFrame, like: 480s 480s df["col"][row_indexer] = value 480s 480s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 480s 480s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 480s 480s segments.end.iat[-1] = bins_end 480s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 480s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 480s A typical example is when you are setting values in a column of a DataFrame, like: 480s 480s df["col"][row_indexer] = value 480s 480s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 480s 480s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 480s 480s segments.start.iat[0] = bins_start 480s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 480s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 480s A typical example is when you are setting values in a column of a DataFrame, like: 480s 480s df["col"][row_indexer] = value 480s 480s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 480s 480s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 480s 480s segments.end.iat[-1] = bins_end 480s Wrote build/p2-20_3.cns with 64 regions 480s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_4.cnr -o build/p2-20_4.cns 484s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.start.iat[0] = bins_start 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.end.iat[-1] = bins_end 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.start.iat[0] = bins_start 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.end.iat[-1] = bins_end 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.start.iat[0] = bins_start 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.end.iat[-1] = bins_end 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.start.iat[0] = bins_start 485s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 485s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 485s A typical example is when you are setting values in a column of a DataFrame, like: 485s 485s df["col"][row_indexer] = value 485s 485s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 485s 485s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 485s 485s segments.end.iat[-1] = bins_end 486s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 486s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 486s A typical example is when you are setting values in a column of a DataFrame, like: 486s 486s df["col"][row_indexer] = value 486s 486s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 486s 486s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 486s 486s segments.start.iat[0] = bins_start 486s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 486s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 486s A typical example is when you are setting values in a column of a DataFrame, like: 486s 486s df["col"][row_indexer] = value 486s 486s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 486s 486s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 486s 486s segments.end.iat[-1] = bins_end 486s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 486s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 486s A typical example is when you are setting values in a column of a DataFrame, like: 486s 486s df["col"][row_indexer] = value 486s 486s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 486s 486s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 486s 486s segments.start.iat[0] = bins_start 486s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 486s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 486s A typical example is when you are setting values in a column of a DataFrame, like: 486s 486s df["col"][row_indexer] = value 486s 486s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 486s 486s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 486s 486s segments.end.iat[-1] = bins_end 487s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 487s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 487s A typical example is when you are setting values in a column of a DataFrame, like: 487s 487s df["col"][row_indexer] = value 487s 487s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 487s 487s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 487s 487s segments.start.iat[0] = bins_start 487s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 487s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 487s A typical example is when you are setting values in a column of a DataFrame, like: 487s 487s df["col"][row_indexer] = value 487s 487s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 487s 487s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 487s 487s segments.end.iat[-1] = bins_end 487s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 487s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 487s A typical example is when you are setting values in a column of a DataFrame, like: 487s 487s df["col"][row_indexer] = value 487s 487s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 487s 487s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 487s 487s segments.start.iat[0] = bins_start 487s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 487s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 487s A typical example is when you are setting values in a column of a DataFrame, like: 487s 487s df["col"][row_indexer] = value 487s 487s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 487s 487s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 487s 487s segments.end.iat[-1] = bins_end 487s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 487s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 487s A typical example is when you are setting values in a column of a DataFrame, like: 487s 487s df["col"][row_indexer] = value 487s 487s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 487s 487s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 487s 487s segments.start.iat[0] = bins_start 487s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 487s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 487s A typical example is when you are setting values in a column of a DataFrame, like: 487s 487s df["col"][row_indexer] = value 487s 487s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 487s 487s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 487s 487s segments.end.iat[-1] = bins_end 488s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 488s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 488s A typical example is when you are setting values in a column of a DataFrame, like: 488s 488s df["col"][row_indexer] = value 488s 488s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 488s 488s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 488s 488s segments.start.iat[0] = bins_start 488s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 488s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 488s A typical example is when you are setting values in a column of a DataFrame, like: 488s 488s df["col"][row_indexer] = value 488s 488s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 488s 488s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 488s 488s segments.end.iat[-1] = bins_end 488s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 488s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 488s A typical example is when you are setting values in a column of a DataFrame, like: 488s 488s df["col"][row_indexer] = value 488s 488s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 488s 488s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 488s 488s segments.start.iat[0] = bins_start 488s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 488s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 488s A typical example is when you are setting values in a column of a DataFrame, like: 488s 488s df["col"][row_indexer] = value 488s 488s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 488s 488s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 488s 488s segments.end.iat[-1] = bins_end 489s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 489s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 489s A typical example is when you are setting values in a column of a DataFrame, like: 489s 489s df["col"][row_indexer] = value 489s 489s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 489s 489s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 489s 489s segments.start.iat[0] = bins_start 489s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 489s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 489s A typical example is when you are setting values in a column of a DataFrame, like: 489s 489s df["col"][row_indexer] = value 489s 489s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 489s 489s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 489s 489s segments.end.iat[-1] = bins_end 489s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 489s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 489s A typical example is when you are setting values in a column of a DataFrame, like: 489s 489s df["col"][row_indexer] = value 489s 489s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 489s 489s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 489s 489s segments.start.iat[0] = bins_start 489s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 489s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 489s A typical example is when you are setting values in a column of a DataFrame, like: 489s 489s df["col"][row_indexer] = value 489s 489s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 489s 489s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 489s 489s segments.end.iat[-1] = bins_end 489s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 489s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 489s A typical example is when you are setting values in a column of a DataFrame, like: 489s 489s df["col"][row_indexer] = value 489s 489s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 489s 489s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 489s 489s segments.start.iat[0] = bins_start 489s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 489s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 489s A typical example is when you are setting values in a column of a DataFrame, like: 489s 489s df["col"][row_indexer] = value 489s 489s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 489s 489s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 489s 489s segments.end.iat[-1] = bins_end 490s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 490s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 490s A typical example is when you are setting values in a column of a DataFrame, like: 490s 490s df["col"][row_indexer] = value 490s 490s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 490s 490s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 490s 490s segments.start.iat[0] = bins_start 490s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 490s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 490s A typical example is when you are setting values in a column of a DataFrame, like: 490s 490s df["col"][row_indexer] = value 490s 490s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 490s 490s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 490s 490s segments.end.iat[-1] = bins_end 490s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 490s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 490s A typical example is when you are setting values in a column of a DataFrame, like: 490s 490s df["col"][row_indexer] = value 490s 490s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 490s 490s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 490s 490s segments.start.iat[0] = bins_start 490s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 490s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 490s A typical example is when you are setting values in a column of a DataFrame, like: 490s 490s df["col"][row_indexer] = value 490s 490s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 490s 490s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 490s 490s segments.end.iat[-1] = bins_end 490s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 490s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 490s A typical example is when you are setting values in a column of a DataFrame, like: 490s 490s df["col"][row_indexer] = value 490s 490s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 490s 490s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 490s 490s segments.start.iat[0] = bins_start 490s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 490s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 490s A typical example is when you are setting values in a column of a DataFrame, like: 490s 490s df["col"][row_indexer] = value 490s 490s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 490s 490s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 490s 490s segments.end.iat[-1] = bins_end 491s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 491s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 491s A typical example is when you are setting values in a column of a DataFrame, like: 491s 491s df["col"][row_indexer] = value 491s 491s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 491s 491s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 491s 491s segments.start.iat[0] = bins_start 491s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 491s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 491s A typical example is when you are setting values in a column of a DataFrame, like: 491s 491s df["col"][row_indexer] = value 491s 491s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 491s 491s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 491s 491s segments.end.iat[-1] = bins_end 491s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 491s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 491s A typical example is when you are setting values in a column of a DataFrame, like: 491s 491s df["col"][row_indexer] = value 491s 491s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 491s 491s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 491s 491s segments.start.iat[0] = bins_start 491s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 491s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 491s A typical example is when you are setting values in a column of a DataFrame, like: 491s 491s df["col"][row_indexer] = value 491s 491s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 491s 491s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 491s 491s segments.end.iat[-1] = bins_end 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.start.iat[0] = bins_start 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.end.iat[-1] = bins_end 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.start.iat[0] = bins_start 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.end.iat[-1] = bins_end 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.start.iat[0] = bins_start 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.end.iat[-1] = bins_end 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.start.iat[0] = bins_start 492s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 492s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 492s A typical example is when you are setting values in a column of a DataFrame, like: 492s 492s df["col"][row_indexer] = value 492s 492s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 492s 492s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 492s 492s segments.end.iat[-1] = bins_end 493s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 493s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 493s A typical example is when you are setting values in a column of a DataFrame, like: 493s 493s df["col"][row_indexer] = value 493s 493s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 493s 493s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 493s 493s segments.start.iat[0] = bins_start 493s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 493s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 493s A typical example is when you are setting values in a column of a DataFrame, like: 493s 493s df["col"][row_indexer] = value 493s 493s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 493s 493s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 493s 493s segments.end.iat[-1] = bins_end 493s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 493s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 493s A typical example is when you are setting values in a column of a DataFrame, like: 493s 493s df["col"][row_indexer] = value 493s 493s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 493s 493s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 493s 493s segments.start.iat[0] = bins_start 493s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 493s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 493s A typical example is when you are setting values in a column of a DataFrame, like: 493s 493s df["col"][row_indexer] = value 493s 493s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 493s 493s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 493s 493s segments.end.iat[-1] = bins_end 493s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 493s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 493s A typical example is when you are setting values in a column of a DataFrame, like: 493s 493s df["col"][row_indexer] = value 493s 493s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 493s 493s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 493s 493s segments.start.iat[0] = bins_start 493s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 493s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 493s A typical example is when you are setting values in a column of a DataFrame, like: 493s 493s df["col"][row_indexer] = value 493s 493s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 493s 493s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 493s 493s segments.end.iat[-1] = bins_end 494s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 494s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 494s A typical example is when you are setting values in a column of a DataFrame, like: 494s 494s df["col"][row_indexer] = value 494s 494s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 494s 494s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 494s 494s segments.start.iat[0] = bins_start 494s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 494s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 494s A typical example is when you are setting values in a column of a DataFrame, like: 494s 494s df["col"][row_indexer] = value 494s 494s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 494s 494s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 494s 494s segments.end.iat[-1] = bins_end 494s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 494s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 494s A typical example is when you are setting values in a column of a DataFrame, like: 494s 494s df["col"][row_indexer] = value 494s 494s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 494s 494s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 494s 494s segments.start.iat[0] = bins_start 494s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 494s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 494s A typical example is when you are setting values in a column of a DataFrame, like: 494s 494s df["col"][row_indexer] = value 494s 494s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 494s 494s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 494s 494s segments.end.iat[-1] = bins_end 494s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 494s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 494s A typical example is when you are setting values in a column of a DataFrame, like: 494s 494s df["col"][row_indexer] = value 494s 494s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 494s 494s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 494s 494s segments.start.iat[0] = bins_start 494s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 494s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 494s A typical example is when you are setting values in a column of a DataFrame, like: 494s 494s df["col"][row_indexer] = value 494s 494s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 494s 494s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 494s 494s segments.end.iat[-1] = bins_end 495s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 495s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 495s A typical example is when you are setting values in a column of a DataFrame, like: 495s 495s df["col"][row_indexer] = value 495s 495s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 495s 495s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 495s 495s segments.start.iat[0] = bins_start 495s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 495s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 495s A typical example is when you are setting values in a column of a DataFrame, like: 495s 495s df["col"][row_indexer] = value 495s 495s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 495s 495s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 495s 495s segments.end.iat[-1] = bins_end 495s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 495s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 495s A typical example is when you are setting values in a column of a DataFrame, like: 495s 495s df["col"][row_indexer] = value 495s 495s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 495s 495s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 495s 495s segments.start.iat[0] = bins_start 495s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 495s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 495s A typical example is when you are setting values in a column of a DataFrame, like: 495s 495s df["col"][row_indexer] = value 495s 495s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 495s 495s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 495s 495s segments.end.iat[-1] = bins_end 495s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 495s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 495s A typical example is when you are setting values in a column of a DataFrame, like: 495s 495s df["col"][row_indexer] = value 495s 495s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 495s 495s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 495s 495s segments.start.iat[0] = bins_start 495s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 495s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 495s A typical example is when you are setting values in a column of a DataFrame, like: 495s 495s df["col"][row_indexer] = value 495s 495s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 495s 495s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 495s 495s segments.end.iat[-1] = bins_end 496s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 496s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 496s A typical example is when you are setting values in a column of a DataFrame, like: 496s 496s df["col"][row_indexer] = value 496s 496s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 496s 496s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 496s 496s segments.start.iat[0] = bins_start 496s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 496s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 496s A typical example is when you are setting values in a column of a DataFrame, like: 496s 496s df["col"][row_indexer] = value 496s 496s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 496s 496s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 496s 496s segments.end.iat[-1] = bins_end 496s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 496s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 496s A typical example is when you are setting values in a column of a DataFrame, like: 496s 496s df["col"][row_indexer] = value 496s 496s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 496s 496s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 496s 496s segments.start.iat[0] = bins_start 496s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 496s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 496s A typical example is when you are setting values in a column of a DataFrame, like: 496s 496s df["col"][row_indexer] = value 496s 496s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 496s 496s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 496s 496s segments.end.iat[-1] = bins_end 496s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 496s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 496s A typical example is when you are setting values in a column of a DataFrame, like: 496s 496s df["col"][row_indexer] = value 496s 496s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 496s 496s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 496s 496s segments.start.iat[0] = bins_start 496s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 496s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 496s A typical example is when you are setting values in a column of a DataFrame, like: 496s 496s df["col"][row_indexer] = value 496s 496s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 496s 496s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 496s 496s segments.end.iat[-1] = bins_end 497s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 497s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 497s A typical example is when you are setting values in a column of a DataFrame, like: 497s 497s df["col"][row_indexer] = value 497s 497s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 497s 497s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 497s 497s segments.start.iat[0] = bins_start 497s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 497s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 497s A typical example is when you are setting values in a column of a DataFrame, like: 497s 497s df["col"][row_indexer] = value 497s 497s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 497s 497s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 497s 497s segments.end.iat[-1] = bins_end 497s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 497s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 497s A typical example is when you are setting values in a column of a DataFrame, like: 497s 497s df["col"][row_indexer] = value 497s 497s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 497s 497s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 497s 497s segments.start.iat[0] = bins_start 497s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 497s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 497s A typical example is when you are setting values in a column of a DataFrame, like: 497s 497s df["col"][row_indexer] = value 497s 497s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 497s 497s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 497s 497s segments.end.iat[-1] = bins_end 497s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 497s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 497s A typical example is when you are setting values in a column of a DataFrame, like: 497s 497s df["col"][row_indexer] = value 497s 497s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 497s 497s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 497s 497s segments.start.iat[0] = bins_start 497s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 497s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 497s A typical example is when you are setting values in a column of a DataFrame, like: 497s 497s df["col"][row_indexer] = value 497s 497s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 497s 497s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 497s 497s segments.end.iat[-1] = bins_end 498s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 498s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 498s A typical example is when you are setting values in a column of a DataFrame, like: 498s 498s df["col"][row_indexer] = value 498s 498s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 498s 498s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 498s 498s segments.start.iat[0] = bins_start 498s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 498s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 498s A typical example is when you are setting values in a column of a DataFrame, like: 498s 498s df["col"][row_indexer] = value 498s 498s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 498s 498s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 498s 498s segments.end.iat[-1] = bins_end 498s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 498s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 498s A typical example is when you are setting values in a column of a DataFrame, like: 498s 498s df["col"][row_indexer] = value 498s 498s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 498s 498s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 498s 498s segments.start.iat[0] = bins_start 498s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 498s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 498s A typical example is when you are setting values in a column of a DataFrame, like: 498s 498s df["col"][row_indexer] = value 498s 498s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 498s 498s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 498s 498s segments.end.iat[-1] = bins_end 498s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 498s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 498s A typical example is when you are setting values in a column of a DataFrame, like: 498s 498s df["col"][row_indexer] = value 498s 498s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 498s 498s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 498s 498s segments.start.iat[0] = bins_start 498s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 498s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 498s A typical example is when you are setting values in a column of a DataFrame, like: 498s 498s df["col"][row_indexer] = value 498s 498s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 498s 498s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 498s 498s segments.end.iat[-1] = bins_end 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.start.iat[0] = bins_start 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.end.iat[-1] = bins_end 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.start.iat[0] = bins_start 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.end.iat[-1] = bins_end 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.start.iat[0] = bins_start 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.end.iat[-1] = bins_end 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.start.iat[0] = bins_start 499s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 499s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 499s A typical example is when you are setting values in a column of a DataFrame, like: 499s 499s df["col"][row_indexer] = value 499s 499s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 499s 499s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 499s 499s segments.end.iat[-1] = bins_end 499s Dropped 8 / 49 bins on chromosome chrY 500s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 500s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 500s A typical example is when you are setting values in a column of a DataFrame, like: 500s 500s df["col"][row_indexer] = value 500s 500s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 500s 500s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 500s 500s segments.start.iat[0] = bins_start 500s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 500s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 500s A typical example is when you are setting values in a column of a DataFrame, like: 500s 500s df["col"][row_indexer] = value 500s 500s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 500s 500s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 500s 500s segments.end.iat[-1] = bins_end 500s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 500s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 500s A typical example is when you are setting values in a column of a DataFrame, like: 500s 500s df["col"][row_indexer] = value 500s 500s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 500s 500s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 500s 500s segments.start.iat[0] = bins_start 500s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 500s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 500s A typical example is when you are setting values in a column of a DataFrame, like: 500s 500s df["col"][row_indexer] = value 500s 500s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 500s 500s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 500s 500s segments.end.iat[-1] = bins_end 500s Wrote build/p2-20_4.cns with 143 regions 501s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_5.cnr -o build/p2-20_5.cns 504s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 505s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 505s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 505s A typical example is when you are setting values in a column of a DataFrame, like: 505s 505s df["col"][row_indexer] = value 505s 505s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 505s 505s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 505s 505s segments.start.iat[0] = bins_start 505s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 505s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 505s A typical example is when you are setting values in a column of a DataFrame, like: 505s 505s df["col"][row_indexer] = value 505s 505s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 505s 505s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 505s 505s segments.end.iat[-1] = bins_end 505s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 505s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 505s A typical example is when you are setting values in a column of a DataFrame, like: 505s 505s df["col"][row_indexer] = value 505s 505s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 505s 505s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 505s 505s segments.start.iat[0] = bins_start 505s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 505s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 505s A typical example is when you are setting values in a column of a DataFrame, like: 505s 505s df["col"][row_indexer] = value 505s 505s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 505s 505s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 505s 505s segments.end.iat[-1] = bins_end 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.start.iat[0] = bins_start 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.end.iat[-1] = bins_end 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.start.iat[0] = bins_start 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.end.iat[-1] = bins_end 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.start.iat[0] = bins_start 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.end.iat[-1] = bins_end 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.start.iat[0] = bins_start 506s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 506s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 506s A typical example is when you are setting values in a column of a DataFrame, like: 506s 506s df["col"][row_indexer] = value 506s 506s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 506s 506s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 506s 506s segments.end.iat[-1] = bins_end 507s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 507s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 507s A typical example is when you are setting values in a column of a DataFrame, like: 507s 507s df["col"][row_indexer] = value 507s 507s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 507s 507s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 507s 507s segments.start.iat[0] = bins_start 507s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 507s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 507s A typical example is when you are setting values in a column of a DataFrame, like: 507s 507s df["col"][row_indexer] = value 507s 507s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 507s 507s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 507s 507s segments.end.iat[-1] = bins_end 507s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 507s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 507s A typical example is when you are setting values in a column of a DataFrame, like: 507s 507s df["col"][row_indexer] = value 507s 507s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 507s 507s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 507s 507s segments.start.iat[0] = bins_start 507s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 507s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 507s A typical example is when you are setting values in a column of a DataFrame, like: 507s 507s df["col"][row_indexer] = value 507s 507s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 507s 507s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 507s 507s segments.end.iat[-1] = bins_end 507s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 507s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 507s A typical example is when you are setting values in a column of a DataFrame, like: 507s 507s df["col"][row_indexer] = value 507s 507s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 507s 507s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 507s 507s segments.start.iat[0] = bins_start 507s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 507s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 507s A typical example is when you are setting values in a column of a DataFrame, like: 507s 507s df["col"][row_indexer] = value 507s 507s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 507s 507s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 507s 507s segments.end.iat[-1] = bins_end 508s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 508s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 508s A typical example is when you are setting values in a column of a DataFrame, like: 508s 508s df["col"][row_indexer] = value 508s 508s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 508s 508s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 508s 508s segments.start.iat[0] = bins_start 508s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 508s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 508s A typical example is when you are setting values in a column of a DataFrame, like: 508s 508s df["col"][row_indexer] = value 508s 508s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 508s 508s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 508s 508s segments.end.iat[-1] = bins_end 508s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 508s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 508s A typical example is when you are setting values in a column of a DataFrame, like: 508s 508s df["col"][row_indexer] = value 508s 508s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 508s 508s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 508s 508s segments.start.iat[0] = bins_start 508s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 508s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 508s A typical example is when you are setting values in a column of a DataFrame, like: 508s 508s df["col"][row_indexer] = value 508s 508s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 508s 508s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 508s 508s segments.end.iat[-1] = bins_end 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.start.iat[0] = bins_start 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.end.iat[-1] = bins_end 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.start.iat[0] = bins_start 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.end.iat[-1] = bins_end 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.start.iat[0] = bins_start 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.end.iat[-1] = bins_end 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.start.iat[0] = bins_start 509s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 509s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 509s A typical example is when you are setting values in a column of a DataFrame, like: 509s 509s df["col"][row_indexer] = value 509s 509s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 509s 509s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 509s 509s segments.end.iat[-1] = bins_end 510s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 510s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 510s A typical example is when you are setting values in a column of a DataFrame, like: 510s 510s df["col"][row_indexer] = value 510s 510s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 510s 510s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 510s 510s segments.start.iat[0] = bins_start 510s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 510s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 510s A typical example is when you are setting values in a column of a DataFrame, like: 510s 510s df["col"][row_indexer] = value 510s 510s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 510s 510s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 510s 510s segments.end.iat[-1] = bins_end 510s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 510s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 510s A typical example is when you are setting values in a column of a DataFrame, like: 510s 510s df["col"][row_indexer] = value 510s 510s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 510s 510s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 510s 510s segments.start.iat[0] = bins_start 510s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 510s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 510s A typical example is when you are setting values in a column of a DataFrame, like: 510s 510s df["col"][row_indexer] = value 510s 510s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 510s 510s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 510s 510s segments.end.iat[-1] = bins_end 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.start.iat[0] = bins_start 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.end.iat[-1] = bins_end 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.start.iat[0] = bins_start 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.end.iat[-1] = bins_end 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.start.iat[0] = bins_start 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.end.iat[-1] = bins_end 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.start.iat[0] = bins_start 511s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 511s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 511s A typical example is when you are setting values in a column of a DataFrame, like: 511s 511s df["col"][row_indexer] = value 511s 511s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 511s 511s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 511s 511s segments.end.iat[-1] = bins_end 512s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 512s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 512s A typical example is when you are setting values in a column of a DataFrame, like: 512s 512s df["col"][row_indexer] = value 512s 512s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 512s 512s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 512s 512s segments.start.iat[0] = bins_start 512s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 512s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 512s A typical example is when you are setting values in a column of a DataFrame, like: 512s 512s df["col"][row_indexer] = value 512s 512s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 512s 512s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 512s 512s segments.end.iat[-1] = bins_end 512s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 512s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 512s A typical example is when you are setting values in a column of a DataFrame, like: 512s 512s df["col"][row_indexer] = value 512s 512s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 512s 512s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 512s 512s segments.start.iat[0] = bins_start 512s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 512s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 512s A typical example is when you are setting values in a column of a DataFrame, like: 512s 512s df["col"][row_indexer] = value 512s 512s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 512s 512s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 512s 512s segments.end.iat[-1] = bins_end 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.start.iat[0] = bins_start 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.end.iat[-1] = bins_end 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.start.iat[0] = bins_start 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.end.iat[-1] = bins_end 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.start.iat[0] = bins_start 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.end.iat[-1] = bins_end 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.start.iat[0] = bins_start 513s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 513s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 513s A typical example is when you are setting values in a column of a DataFrame, like: 513s 513s df["col"][row_indexer] = value 513s 513s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 513s 513s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 513s 513s segments.end.iat[-1] = bins_end 514s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 514s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 514s A typical example is when you are setting values in a column of a DataFrame, like: 514s 514s df["col"][row_indexer] = value 514s 514s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 514s 514s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 514s 514s segments.start.iat[0] = bins_start 514s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 514s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 514s A typical example is when you are setting values in a column of a DataFrame, like: 514s 514s df["col"][row_indexer] = value 514s 514s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 514s 514s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 514s 514s segments.end.iat[-1] = bins_end 514s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 514s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 514s A typical example is when you are setting values in a column of a DataFrame, like: 514s 514s df["col"][row_indexer] = value 514s 514s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 514s 514s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 514s 514s segments.start.iat[0] = bins_start 514s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 514s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 514s A typical example is when you are setting values in a column of a DataFrame, like: 514s 514s df["col"][row_indexer] = value 514s 514s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 514s 514s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 514s 514s segments.end.iat[-1] = bins_end 514s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 514s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 514s A typical example is when you are setting values in a column of a DataFrame, like: 514s 514s df["col"][row_indexer] = value 514s 514s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 514s 514s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 514s 514s segments.start.iat[0] = bins_start 514s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 514s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 514s A typical example is when you are setting values in a column of a DataFrame, like: 514s 514s df["col"][row_indexer] = value 514s 514s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 514s 514s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 514s 514s segments.end.iat[-1] = bins_end 515s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 515s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 515s A typical example is when you are setting values in a column of a DataFrame, like: 515s 515s df["col"][row_indexer] = value 515s 515s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 515s 515s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 515s 515s segments.start.iat[0] = bins_start 515s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 515s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 515s A typical example is when you are setting values in a column of a DataFrame, like: 515s 515s df["col"][row_indexer] = value 515s 515s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 515s 515s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 515s 515s segments.end.iat[-1] = bins_end 515s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 515s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 515s A typical example is when you are setting values in a column of a DataFrame, like: 515s 515s df["col"][row_indexer] = value 515s 515s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 515s 515s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 515s 515s segments.start.iat[0] = bins_start 515s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 515s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 515s A typical example is when you are setting values in a column of a DataFrame, like: 515s 515s df["col"][row_indexer] = value 515s 515s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 515s 515s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 515s 515s segments.end.iat[-1] = bins_end 515s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 515s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 515s A typical example is when you are setting values in a column of a DataFrame, like: 515s 515s df["col"][row_indexer] = value 515s 515s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 515s 515s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 515s 515s segments.start.iat[0] = bins_start 515s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 515s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 515s A typical example is when you are setting values in a column of a DataFrame, like: 515s 515s df["col"][row_indexer] = value 515s 515s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 515s 515s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 515s 515s segments.end.iat[-1] = bins_end 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.start.iat[0] = bins_start 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.end.iat[-1] = bins_end 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.start.iat[0] = bins_start 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.end.iat[-1] = bins_end 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.start.iat[0] = bins_start 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.end.iat[-1] = bins_end 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.start.iat[0] = bins_start 516s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 516s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 516s A typical example is when you are setting values in a column of a DataFrame, like: 516s 516s df["col"][row_indexer] = value 516s 516s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 516s 516s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 516s 516s segments.end.iat[-1] = bins_end 517s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 517s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 517s A typical example is when you are setting values in a column of a DataFrame, like: 517s 517s df["col"][row_indexer] = value 517s 517s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 517s 517s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 517s 517s segments.start.iat[0] = bins_start 517s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 517s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 517s A typical example is when you are setting values in a column of a DataFrame, like: 517s 517s df["col"][row_indexer] = value 517s 517s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 517s 517s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 517s 517s segments.end.iat[-1] = bins_end 517s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 517s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 517s A typical example is when you are setting values in a column of a DataFrame, like: 517s 517s df["col"][row_indexer] = value 517s 517s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 517s 517s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 517s 517s segments.start.iat[0] = bins_start 517s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 517s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 517s A typical example is when you are setting values in a column of a DataFrame, like: 517s 517s df["col"][row_indexer] = value 517s 517s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 517s 517s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 517s 517s segments.end.iat[-1] = bins_end 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.start.iat[0] = bins_start 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.end.iat[-1] = bins_end 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.start.iat[0] = bins_start 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.end.iat[-1] = bins_end 518s Smoothing overshot at 1 / 123 indices: (-0.30060035404086427, 0.3250888018519485) vs. original (-0.363302, 0.311218) 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.start.iat[0] = bins_start 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.end.iat[-1] = bins_end 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.start.iat[0] = bins_start 518s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 518s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 518s A typical example is when you are setting values in a column of a DataFrame, like: 518s 518s df["col"][row_indexer] = value 518s 518s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 518s 518s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 518s 518s segments.end.iat[-1] = bins_end 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.start.iat[0] = bins_start 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.end.iat[-1] = bins_end 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.start.iat[0] = bins_start 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.end.iat[-1] = bins_end 519s Dropped 6 / 49 bins on chromosome chrY 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.start.iat[0] = bins_start 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.end.iat[-1] = bins_end 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.start.iat[0] = bins_start 519s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 519s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 519s A typical example is when you are setting values in a column of a DataFrame, like: 519s 519s df["col"][row_indexer] = value 519s 519s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 519s 519s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 519s 519s segments.end.iat[-1] = bins_end 519s Wrote build/p2-20_5.cns with 120 regions 520s cnvkit.py scatter -s build/p2-5_5.cns build/p2-5_5.cnr -t --y-min=-2.5 -o p2-5_5-scatter.pdf 527s Wrote p2-5_5-scatter.pdf 527s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -t --y-min=-2.5 -o p2-9_2-scatter.pdf 531s Smoothing overshot at 3 / 97 indices: (-19.900105868621704, -3.3099069223720528) vs. original (-19.294, -0.16431) 534s Wrote p2-9_2-scatter.pdf 534s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -c chr1 -t -o p2-9_2-chr1-scatter.pdf 538s Showing 1480 probes and 0 selected genes in region chr1 538s Wrote p2-9_2-chr1-scatter.pdf 539s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -c chr21 -t -o p2-9_2-chr21-scatter.pdf 542s Showing 201 probes and 0 selected genes in region chr21 543s Wrote p2-9_2-chr21-scatter.pdf 545s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -c 'chr9:150000-45000000' -o p2-9_2-chr9p-scatter.pdf 547s Showing 330 probes and 13 selected genes in region chr9:149999-45000000 547s Wrote p2-9_2-chr9p-scatter.pdf 548s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -g SMARCA2,PTPRD -w 4e6 -o p2-9_2-SMARCA2-PTPRD-scatter.pdf 552s Showing 179 probes and 2 selected genes in region chr9:0-14504268.0 552s Wrote p2-9_2-SMARCA2-PTPRD-scatter.pdf 553s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -l regions.bed -o p2-9_2-bed_regions-scatter.pdf 556s Detected file format: bed 556s Showing 41 probes and 1 selected genes in region chr9:2000000-4000000 557s Showing 53 probes and 1 selected genes in region chr9:8000000-12000000 557s which pdfunite && pdfunite p2-5_5-scatter.pdf p2-9_2-scatter.pdf p2-9_2-chr1-scatter.pdf p2-9_2-chr21-scatter.pdf p2-9_2-chr9p-scatter.pdf p2-9_2-SMARCA2-PTPRD-scatter.pdf p2-9_2-bed_regions-scatter.pdf all-scatters.pdf || touch all-scatters.pdf 557s /usr/bin/pdfunite 558s cnvkit.py diagram -y build/p2-5_5.cnr -o p2-5_5-diagram.pdf 561s Treating sample p2-5_5 as female 581s Wrote p2-5_5-diagram.pdf 582s cnvkit.py diagram -y build/p2-9_2.cnr -o p2-9_2-diagram.pdf 585s Treating sample p2-9_2 as female 605s Wrote p2-9_2-diagram.pdf 605s cnvkit.py diagram -y build/p2-20_1.cnr -o p2-20_1-diagram.pdf 609s Treating sample p2-20_1 as female 627s Wrote p2-20_1-diagram.pdf 628s cnvkit.py diagram -y build/p2-20_2.cnr -o p2-20_2-diagram.pdf 631s Treating sample p2-20_2 as female 649s Wrote p2-20_2-diagram.pdf 649s cnvkit.py diagram -y --segment=build/p2-5_5.cns -o p2-5_5-cbs-diagram.pdf 653s Treating sample p2-5_5 as female 653s Wrote p2-5_5-cbs-diagram.pdf 653s cnvkit.py diagram -y --segment=build/p2-9_2.cns -o p2-9_2-cbs-diagram.pdf 657s Treating sample p2-9_2 as female 657s Wrote p2-9_2-cbs-diagram.pdf 657s cnvkit.py diagram -y --segment=build/p2-20_1.cns -o p2-20_1-cbs-diagram.pdf 661s Treating sample p2-20_1 as female 661s Wrote p2-20_1-cbs-diagram.pdf 661s cnvkit.py diagram -y --segment=build/p2-20_2.cns -o p2-20_2-cbs-diagram.pdf 665s Treating sample p2-20_2 as female 665s Wrote p2-20_2-cbs-diagram.pdf 665s cnvkit.py diagram -y --segment=build/p2-5_5.cns build/p2-5_5.cnr -t 0.3 -o p2-5_5-both-diagram.pdf 669s Treating sample p2-5_5 as female 679s Wrote p2-5_5-both-diagram.pdf 680s cnvkit.py diagram -y --segment=build/p2-9_2.cns build/p2-9_2.cnr -t 0.3 -o p2-9_2-both-diagram.pdf 683s Treating sample p2-9_2 as female 694s Wrote p2-9_2-both-diagram.pdf 695s cnvkit.py diagram -y --segment=build/p2-20_1.cns build/p2-20_1.cnr -t 0.3 -o p2-20_1-both-diagram.pdf 698s Treating sample p2-20_1 as female 709s Wrote p2-20_1-both-diagram.pdf 709s cnvkit.py diagram -y --segment=build/p2-20_2.cns build/p2-20_2.cnr -t 0.3 -o p2-20_2-both-diagram.pdf 713s Treating sample p2-20_2 as female 724s Wrote p2-20_2-both-diagram.pdf 724s which pdfunite && pdfunite p2-5_5-diagram.pdf p2-9_2-diagram.pdf p2-20_1-diagram.pdf p2-20_2-diagram.pdf p2-5_5-cbs-diagram.pdf p2-9_2-cbs-diagram.pdf p2-20_1-cbs-diagram.pdf p2-20_2-cbs-diagram.pdf p2-5_5-both-diagram.pdf p2-9_2-both-diagram.pdf p2-20_1-both-diagram.pdf p2-20_2-both-diagram.pdf all-diagrams.pdf || touch all-diagrams.pdf 724s /usr/bin/pdfunite 725s cnvkit.py heatmap build/p2-5_5.cns build/p2-9_2.cns build/p2-20_1.cns build/p2-20_2.cns build/p2-20_3.cns build/p2-20_4.cns build/p2-20_5.cns -y -o heatmap-picard.pdf 728s Treating sample p2-5_5 as female 728s Treating sample p2-9_2 as female 728s Treating sample p2-20_1 as female 728s Treating sample p2-20_2 as female 728s Treating sample p2-20_3 as female 728s Treating sample p2-20_4 as female 728s Treating sample p2-20_5 as female 732s Wrote heatmap-picard.pdf 732s cnvkit.py breaks build/p2-9_2.cnr build/p2-9_2.cns -o p2-9_2-breaks.txt 736s Found 14 gene breakpoints 736s Wrote p2-9_2-breaks.txt 736s cnvkit.py genemetrics -y -m 2 -s build/p2-9_2.cns build/p2-9_2.cnr -o p2-9_2-genemetrics.txt 740s Treating sample p2-9_2 as female 743s Found 323 gene-level gains and losses 743s Wrote p2-9_2-genemetrics.txt 744s cnvkit.py sex -y build/p2-5_5.cnr build/p2-5_5.cns build/p2-9_2.cnr build/p2-9_2.cns build/p2-20_5.cnr build/p2-20_5.cns -o gender-picard.txt 748s Wrote gender-picard.txt 748s cnvkit.py export cdt build/p2-5_5.cnr build/p2-9_2.cnr build/p2-20_1.cnr build/p2-20_2.cnr build/p2-20_3.cnr build/p2-20_4.cnr build/p2-20_5.cnr -o p2-all.cdt 757s Wrote p2-all.cdt 757s cnvkit.py export jtv build/p2-5_5.cnr build/p2-9_2.cnr build/p2-20_1.cnr build/p2-20_2.cnr build/p2-20_3.cnr build/p2-20_4.cnr build/p2-20_5.cnr -o p2-all-jtv.txt 766s Wrote p2-all-jtv.txt 766s cnvkit.py export seg build/p2-5_5.cns build/p2-9_2.cns build/p2-20_1.cns build/p2-20_2.cns build/p2-20_3.cns build/p2-20_4.cns build/p2-20_5.cns -o p2-all.seg 770s Wrote p2-all.seg 770s cnvkit.py export nexus-basic build/p2-9_2.cnr -o p2-9_2.nexus 774s Wrote p2-9_2.nexus 775s cnvkit.py export nexus-ogt build/p2-9_2.cnr formats/na12878_na12882_mix.vcf -o p2-9_2.nexus-ogt 778s Selected test sample NA12882 and control sample NA12878 778s Loaded 3654 records; skipped: 514 somatic, 394 depth 778s Kept 2631 heterozygous of 3654 VCF records 783s Placed 705 variants into 18763 bins 784s Wrote p2-9_2.nexus-ogt 784s cnvkit.py call build/p2-5_5.cns -y -m clonal --purity 0.65 -o build/p2-5_5.call.cns 787s Treating sample p2-5_5 as female 787s Rescaling sample with purity 0.65, ploidy 2 787s Wrote build/p2-5_5.call.cns with 71 regions 788s cnvkit.py call build/p2-9_2.cns -y -m clonal --purity 0.65 -o build/p2-9_2.call.cns 791s Treating sample p2-9_2 as female 791s Rescaling sample with purity 0.65, ploidy 2 791s Wrote build/p2-9_2.call.cns with 103 regions 791s cnvkit.py call build/p2-20_1.cns -y -m clonal --purity 0.65 -o build/p2-20_1.call.cns 794s Treating sample p2-20_1 as female 794s Rescaling sample with purity 0.65, ploidy 2 794s Wrote build/p2-20_1.call.cns with 121 regions 795s cnvkit.py call build/p2-20_2.cns -y -m clonal --purity 0.65 -o build/p2-20_2.call.cns 798s Treating sample p2-20_2 as female 798s Rescaling sample with purity 0.65, ploidy 2 798s Wrote build/p2-20_2.call.cns with 117 regions 799s cnvkit.py call build/p2-20_3.cns -y -m clonal --purity 0.65 -o build/p2-20_3.call.cns 802s Treating sample p2-20_3 as female 802s Rescaling sample with purity 0.65, ploidy 2 802s Wrote build/p2-20_3.call.cns with 64 regions 803s cnvkit.py call build/p2-20_4.cns -y -m clonal --purity 0.65 -o build/p2-20_4.call.cns 806s Treating sample p2-20_4 as female 806s Rescaling sample with purity 0.65, ploidy 2 806s Wrote build/p2-20_4.call.cns with 143 regions 807s cnvkit.py call build/p2-20_5.cns -y -m clonal --purity 0.65 -o build/p2-20_5.call.cns 810s Treating sample p2-20_5 as female 810s Rescaling sample with purity 0.65, ploidy 2 810s Wrote build/p2-20_5.call.cns with 120 regions 811s cnvkit.py export bed build/p2-5_5.call.cns build/p2-9_2.call.cns build/p2-20_1.call.cns build/p2-20_2.call.cns build/p2-20_3.call.cns build/p2-20_4.call.cns build/p2-20_5.call.cns -y --show variant -o p2-all.bed 814s Treating sample p2-5_5.call as female 814s Treating sample p2-9_2.call as female 814s Treating sample p2-20_1.call as female 814s Treating sample p2-20_2.call as female 814s Treating sample p2-20_3.call as female 814s Treating sample p2-20_4.call as female 814s Treating sample p2-20_5.call as female 814s Wrote p2-all.bed 814s cnvkit.py export vcf -o p2-9_2.vcf -y --cnr build/p2-9_2.cnr build/p2-9_2.call.cns -y -o p2-9_2.vcf 818s Treating sample p2-9_2.call as female 818s Wrote p2-9_2.vcf 818s cnvkit.py export theta build/p2-9_2.cns -r build/reference-picard.cnn -o p2-9_2.theta2.input 822s Wrote p2-9_2.theta2.input 823s cnvkit.py segmetrics -s build/p2-9_2.cns build/p2-9_2.cnr -o p2-9_2-segmetrics.cns \ 823s --mean --median --mode --t-test \ 823s --stdev --mad --mse --iqr --bivar \ 823s --ci --pi --sem --smooth-bootstrap 827s Wrote p2-9_2-segmetrics.cns with 103 regions 828s cnvkit.py segmetrics -s build/p2-5_5.cns build/p2-5_5.cnr -o p2-5_5-segmetrics.cns \ 828s --ci -b 50 -a 0.5 832s Wrote p2-5_5-segmetrics.cns with 71 regions 832s cnvkit.py metrics build/p2-5_5.cnr -s build/p2-5_5.cns -o p2-5_5-metrics.tsv 835s Wrote p2-5_5-metrics.tsv 836s cnvkit.py metrics build/p2-9_2.cnr -s build/p2-9_2.cns -o p2-9_2-metrics.tsv 839s Wrote p2-9_2-metrics.tsv 840s cnvkit.py metrics build/p2-20_1.cnr build/p2-20_2.cnr build/p2-20_3.cnr build/p2-20_4.cnr build/p2-20_5.cnr -s build/p2-20_1.cns build/p2-20_2.cns build/p2-20_3.cns build/p2-20_4.cns build/p2-20_5.cns -o p2-20-metrics.tsv 844s Wrote p2-20-metrics.tsv 844s PASS 845s autopkgtest [14:16:39]: test run-unit-test: -----------------------] 846s autopkgtest [14:16:40]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 846s run-unit-test PASS 846s autopkgtest [14:16:40]: test pybuild-autopkgtest: preparing testbed 866s Creating nova instance adt-resolute-arm64-cnvkit-20251117-133314-juju-7f2275-prod-proposed-migration-environment-15-972eee6d-ce5c-4197-baf8-6b5deecbddea from image adt/ubuntu-resolute-arm64-server-20251117.img (UUID 1cd33fbb-18df-4c5a-b8f0-2dcb25269485)... 944s autopkgtest [14:18:18]: testbed dpkg architecture: arm64 944s autopkgtest [14:18:18]: testbed apt version: 3.1.11 945s autopkgtest [14:18:19]: @@@@@@@@@@@@@@@@@@@@ test bed setup 945s autopkgtest [14:18:19]: testbed release detected to be: resolute 946s autopkgtest [14:18:20]: updating testbed package index (apt update) 946s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [87.8 kB] 947s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 947s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 947s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 947s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [22.9 kB] 947s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/restricted Sources [9848 B] 947s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [839 kB] 947s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [81.5 kB] 947s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 Packages [149 kB] 947s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 c-n-f Metadata [3084 B] 947s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/restricted arm64 Packages [107 kB] 947s Get:12 http://ftpmaster.internal/ubuntu resolute-proposed/restricted arm64 c-n-f Metadata [324 B] 947s Get:13 http://ftpmaster.internal/ubuntu resolute-proposed/universe arm64 Packages [557 kB] 947s Get:14 http://ftpmaster.internal/ubuntu resolute-proposed/universe arm64 c-n-f Metadata [17.3 kB] 947s Get:15 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse arm64 Packages [12.5 kB] 947s Get:16 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse arm64 c-n-f Metadata [576 B] 950s Fetched 1888 kB in 1s (1466 kB/s) 952s Reading package lists... 953s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease 953s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 953s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 953s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 955s Reading package lists... 955s Reading package lists... 956s Building dependency tree... 956s Reading state information... 956s Calculating upgrade... 957s The following packages will be upgraded: 957s libpython3-stdlib python3 python3-minimal usbutils 957s 4 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 957s Need to get 144 kB of archives. 957s After this operation, 0 B of additional disk space will be used. 957s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 python3-minimal arm64 3.13.7-2 [27.8 kB] 957s Get:2 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 python3 arm64 3.13.7-2 [23.9 kB] 957s Get:3 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 libpython3-stdlib arm64 3.13.7-2 [10.6 kB] 957s Get:4 http://ftpmaster.internal/ubuntu resolute/main arm64 usbutils arm64 1:019-1 [81.7 kB] 958s dpkg-preconfigure: unable to re-open stdin: No such file or directory 958s Fetched 144 kB in 0s (383 kB/s) 959s (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 ... 88137 files and directories currently installed.) 959s Preparing to unpack .../python3-minimal_3.13.7-2_arm64.deb ... 959s Unpacking python3-minimal (3.13.7-2) over (3.13.7-1) ... 959s Setting up python3-minimal (3.13.7-2) ... 959s (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 ... 88137 files and directories currently installed.) 959s Preparing to unpack .../python3_3.13.7-2_arm64.deb ... 960s running python pre-rtupdate hooks for python3.13... 960s Unpacking python3 (3.13.7-2) over (3.13.7-1) ... 960s Preparing to unpack .../libpython3-stdlib_3.13.7-2_arm64.deb ... 960s Unpacking libpython3-stdlib:arm64 (3.13.7-2) over (3.13.7-1) ... 960s Preparing to unpack .../usbutils_1%3a019-1_arm64.deb ... 960s Unpacking usbutils (1:019-1) over (1:018-2) ... 960s Setting up usbutils (1:019-1) ... 960s Setting up libpython3-stdlib:arm64 (3.13.7-2) ... 960s Setting up python3 (3.13.7-2) ... 960s running python rtupdate hooks for python3.13... 960s running python post-rtupdate hooks for python3.13... 961s Processing triggers for man-db (2.13.1-1) ... 963s autopkgtest [14:18:37]: upgrading testbed (apt dist-upgrade and autopurge) 964s Reading package lists... 964s Building dependency tree... 964s Reading state information... 965s Calculating upgrade... 965s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 965s Reading package lists... 966s Building dependency tree... 966s Reading state information... 966s Solving dependencies... 967s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 971s Reading package lists... 971s Building dependency tree... 971s Reading state information... 971s Solving dependencies... 972s The following NEW packages will be installed: 972s autoconf automake autopoint autotools-dev blt build-essential cnvkit cpp 972s cpp-15 cpp-15-aarch64-linux-gnu cpp-aarch64-linux-gnu cython3 debhelper 972s debugedit dh-autoreconf dh-python dh-strip-nondeterminism dwz fontconfig 972s fontconfig-config fonts-dejavu-core fonts-dejavu-mono fonts-lyx 972s fonts-urw-base35 g++ g++-15 g++-15-aarch64-linux-gnu g++-aarch64-linux-gnu 972s gcc gcc-15 gcc-15-aarch64-linux-gnu gcc-aarch64-linux-gnu gettext 972s intltool-debian libarchive-zip-perl libasan8 libblas3 libcairo2 libcc1-0 972s libdatrie1 libdebhelper-perl libdeflate0 libfile-stripnondeterminism-perl 972s libfontconfig1 libfontenc1 libgcc-15-dev libgfortran5 libgomp1 972s libgpgmepp6t64 libgraphite2-3 libharfbuzz0b libhts3t64 libhtscodecs2 972s libhwasan0 libice6 libimagequant0 libisl23 libitm1 libjbig0 libjpeg-turbo8 972s libjpeg8 liblapack3 liblcms2-2 liblerc4 liblsan0 libmpc3 libopenjp2-7 972s libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpaper-utils 972s libpaper2 libpixman-1-0 libpoppler147 libpython3.14-minimal 972s libpython3.14-stdlib libqhull-r8.0 libraqm0 libsharpyuv0 libsm6 972s libstdc++-15-dev libtcl8.6 libthai-data libthai0 libtiff6 libtk8.6 libtool 972s libtsan2 libubsan1 libwebp7 libwebpdemux2 libwebpmux3 libxcb-render0 972s libxcb-shm0 libxft2 libxrender1 libxslt1.1 libxss1 libxt6t64 libzopfli1 m4 972s po-debconf poppler-utils pybuild-plugin-autopkgtest pybuild-plugin-pyproject 972s python-matplotlib-data python3-all python3-biopython python3-brotli 972s python3-build python3-cairo python3-charset-normalizer python3-contourpy 972s python3-cycler python3-decorator python3-fonttools python3-freetype 972s python3-fs python3-iniconfig python3-installer python3-joblib 972s python3-kiwisolver python3-lxml python3-lz4 python3-matplotlib 972s python3-mpmath python3-networkx python3-numpy python3-numpy-dev 972s python3-pandas python3-pandas-lib python3-pil python3-pil.imagetk 972s python3-platformdirs python3-pluggy python3-pomegranate python3-pyfaidx 972s python3-pyproject-hooks python3-pysam python3-pytest python3-pytz 972s python3-reportlab python3-rlpycairo python3-scipy python3-sklearn 972s python3-sklearn-lib python3-sympy python3-threadpoolctl python3-tk 972s python3-ufolib2 python3-wheel python3-zopfli python3.13-tk python3.14 972s python3.14-minimal python3.14-tk r-base-core r-bioc-biocgenerics 972s r-bioc-dnacopy sgml-base tk8.6-blt2.5 unicode-data unzip w3c-sgml-lib 972s x11-common xdg-utils xfonts-encodings xfonts-utils xml-core zip 972s 0 upgraded, 170 newly installed, 0 to remove and 0 not upgraded. 972s Need to get 260 MB of archives. 972s After this operation, 1109 MB of additional disk space will be used. 972s Get:1 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-numpy-dev arm64 1:2.2.4+ds-1ubuntu1 [146 kB] 973s Get:2 http://ftpmaster.internal/ubuntu resolute/main arm64 libblas3 arm64 3.12.1-7 [181 kB] 973s Get:3 http://ftpmaster.internal/ubuntu resolute/main arm64 libgfortran5 arm64 15.2.0-7ubuntu1 [450 kB] 973s Get:4 http://ftpmaster.internal/ubuntu resolute/main arm64 liblapack3 arm64 3.12.1-7 [2300 kB] 974s Get:5 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-numpy arm64 1:2.2.4+ds-1ubuntu1 [3986 kB] 975s Get:6 http://ftpmaster.internal/ubuntu resolute/main arm64 libpython3.14-minimal arm64 3.14.0-4 [903 kB] 975s Get:7 http://ftpmaster.internal/ubuntu resolute/main arm64 python3.14-minimal arm64 3.14.0-4 [2543 kB] 976s Get:8 http://ftpmaster.internal/ubuntu resolute/main arm64 m4 arm64 1.4.20-2 [213 kB] 976s Get:9 http://ftpmaster.internal/ubuntu resolute/main arm64 autoconf all 2.72-3.1ubuntu1 [384 kB] 976s Get:10 http://ftpmaster.internal/ubuntu resolute/main arm64 autotools-dev all 20240727.1 [43.4 kB] 976s Get:11 http://ftpmaster.internal/ubuntu resolute/main arm64 automake all 1:1.18.1-2 [581 kB] 976s Get:12 http://ftpmaster.internal/ubuntu resolute/main arm64 autopoint all 0.23.2-1 [620 kB] 976s Get:13 http://ftpmaster.internal/ubuntu resolute/main arm64 libtcl8.6 arm64 8.6.17+dfsg-1 [1024 kB] 976s Get:14 http://ftpmaster.internal/ubuntu resolute/main arm64 fonts-dejavu-mono all 2.37-8 [502 kB] 976s Get:15 http://ftpmaster.internal/ubuntu resolute/main arm64 fonts-dejavu-core all 2.37-8 [835 kB] 976s Get:16 http://ftpmaster.internal/ubuntu resolute/main arm64 libfontenc1 arm64 1:1.1.8-1build1 [13.9 kB] 976s Get:17 http://ftpmaster.internal/ubuntu resolute/main arm64 x11-common all 1:7.7+24ubuntu1 [22.4 kB] 976s Get:18 http://ftpmaster.internal/ubuntu resolute/main arm64 xfonts-encodings all 1:1.0.5-0ubuntu2 [578 kB] 976s Get:19 http://ftpmaster.internal/ubuntu resolute/main arm64 xfonts-utils arm64 1:7.7+7 [95.6 kB] 976s Get:20 http://ftpmaster.internal/ubuntu resolute/main arm64 fonts-urw-base35 all 20200910-8 [11.0 MB] 977s Get:21 http://ftpmaster.internal/ubuntu resolute/main arm64 fontconfig-config arm64 2.15.0-2.3ubuntu1 [38.1 kB] 977s Get:22 http://ftpmaster.internal/ubuntu resolute/main arm64 libfontconfig1 arm64 2.15.0-2.3ubuntu1 [144 kB] 977s Get:23 http://ftpmaster.internal/ubuntu resolute/main arm64 libxrender1 arm64 1:0.9.12-1 [19.5 kB] 977s Get:24 http://ftpmaster.internal/ubuntu resolute/main arm64 libxft2 arm64 2.3.6-1build1 [44.1 kB] 977s Get:25 http://ftpmaster.internal/ubuntu resolute/main arm64 libxss1 arm64 1:1.2.3-1build3 [7244 B] 977s Get:26 http://ftpmaster.internal/ubuntu resolute/main arm64 libtk8.6 arm64 8.6.17-1 [811 kB] 977s Get:27 http://ftpmaster.internal/ubuntu resolute/main arm64 tk8.6-blt2.5 arm64 2.5.3+dfsg-8 [624 kB] 977s Get:28 http://ftpmaster.internal/ubuntu resolute/main arm64 blt arm64 2.5.3+dfsg-8 [4824 B] 977s Get:29 http://ftpmaster.internal/ubuntu resolute/main arm64 libisl23 arm64 0.27-1 [676 kB] 977s Get:30 http://ftpmaster.internal/ubuntu resolute/main arm64 libmpc3 arm64 1.3.1-2 [55.6 kB] 977s Get:31 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp-15-aarch64-linux-gnu arm64 15.2.0-7ubuntu1 [11.7 MB] 978s Get:32 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp-15 arm64 15.2.0-7ubuntu1 [1026 B] 978s Get:33 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp-aarch64-linux-gnu arm64 4:15.2.0-4ubuntu1 [5736 B] 978s Get:34 http://ftpmaster.internal/ubuntu resolute/main arm64 cpp arm64 4:15.2.0-4ubuntu1 [22.4 kB] 978s Get:35 http://ftpmaster.internal/ubuntu resolute/main arm64 libcc1-0 arm64 15.2.0-7ubuntu1 [49.0 kB] 978s Get:36 http://ftpmaster.internal/ubuntu resolute/main arm64 libgomp1 arm64 15.2.0-7ubuntu1 [147 kB] 978s Get:37 http://ftpmaster.internal/ubuntu resolute/main arm64 libitm1 arm64 15.2.0-7ubuntu1 [27.9 kB] 978s Get:38 http://ftpmaster.internal/ubuntu resolute/main arm64 libasan8 arm64 15.2.0-7ubuntu1 [2923 kB] 978s Get:39 http://ftpmaster.internal/ubuntu resolute/main arm64 liblsan0 arm64 15.2.0-7ubuntu1 [1316 kB] 978s Get:40 http://ftpmaster.internal/ubuntu resolute/main arm64 libtsan2 arm64 15.2.0-7ubuntu1 [2689 kB] 978s Get:41 http://ftpmaster.internal/ubuntu resolute/main arm64 libubsan1 arm64 15.2.0-7ubuntu1 [1176 kB] 978s Get:42 http://ftpmaster.internal/ubuntu resolute/main arm64 libhwasan0 arm64 15.2.0-7ubuntu1 [1638 kB] 978s Get:43 http://ftpmaster.internal/ubuntu resolute/main arm64 libgcc-15-dev arm64 15.2.0-7ubuntu1 [2600 kB] 978s Get:44 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc-15-aarch64-linux-gnu arm64 15.2.0-7ubuntu1 [23.1 MB] 979s Get:45 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc-15 arm64 15.2.0-7ubuntu1 [513 kB] 979s Get:46 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc-aarch64-linux-gnu arm64 4:15.2.0-4ubuntu1 [1206 B] 979s Get:47 http://ftpmaster.internal/ubuntu resolute/main arm64 gcc arm64 4:15.2.0-4ubuntu1 [5016 B] 979s Get:48 http://ftpmaster.internal/ubuntu resolute/main arm64 libstdc++-15-dev arm64 15.2.0-7ubuntu1 [2546 kB] 979s Get:49 http://ftpmaster.internal/ubuntu resolute/main arm64 g++-15-aarch64-linux-gnu arm64 15.2.0-7ubuntu1 [13.2 MB] 980s Get:50 http://ftpmaster.internal/ubuntu resolute/main arm64 g++-15 arm64 15.2.0-7ubuntu1 [23.7 kB] 980s Get:51 http://ftpmaster.internal/ubuntu resolute/main arm64 g++-aarch64-linux-gnu arm64 4:15.2.0-4ubuntu1 [956 B] 980s Get:52 http://ftpmaster.internal/ubuntu resolute/main arm64 g++ arm64 4:15.2.0-4ubuntu1 [1080 B] 980s Get:53 http://ftpmaster.internal/ubuntu resolute/main arm64 build-essential arm64 12.12ubuntu1 [5082 B] 980s Get:54 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-charset-normalizer arm64 3.4.3-1 [165 kB] 980s Get:55 http://ftpmaster.internal/ubuntu resolute/main arm64 python3.14-tk arm64 3.14.0-4 [107 kB] 980s Get:56 http://ftpmaster.internal/ubuntu resolute/main arm64 python3.13-tk arm64 3.13.9-1 [106 kB] 980s Get:57 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-tk arm64 3.13.9-1 [8946 B] 980s Get:58 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pil.imagetk arm64 11.3.0-1ubuntu2 [9582 B] 980s Get:59 http://ftpmaster.internal/ubuntu resolute/main arm64 libimagequant0 arm64 2.18.0-1build1 [37.1 kB] 980s Get:60 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg-turbo8 arm64 2.1.5-4ubuntu2 [165 kB] 980s Get:61 http://ftpmaster.internal/ubuntu resolute/main arm64 libjpeg8 arm64 8c-2ubuntu11 [2148 B] 980s Get:62 http://ftpmaster.internal/ubuntu resolute/main arm64 liblcms2-2 arm64 2.17-1 [170 kB] 980s Get:63 http://ftpmaster.internal/ubuntu resolute/main arm64 libopenjp2-7 arm64 2.5.3-2.1 [179 kB] 980s Get:64 http://ftpmaster.internal/ubuntu resolute/main arm64 libgraphite2-3 arm64 1.3.14-2ubuntu1 [70.6 kB] 980s Get:65 http://ftpmaster.internal/ubuntu resolute/main arm64 libharfbuzz0b arm64 12.1.0-1 [523 kB] 980s Get:66 http://ftpmaster.internal/ubuntu resolute/main arm64 libraqm0 arm64 0.10.3-1 [15.0 kB] 980s Get:67 http://ftpmaster.internal/ubuntu resolute/main arm64 libdeflate0 arm64 1.23-2 [46.4 kB] 980s Get:68 http://ftpmaster.internal/ubuntu resolute/main arm64 libjbig0 arm64 2.1-6.1ubuntu2 [29.3 kB] 980s Get:69 http://ftpmaster.internal/ubuntu resolute/main arm64 liblerc4 arm64 4.0.0+ds-5ubuntu1 [167 kB] 980s Get:70 http://ftpmaster.internal/ubuntu resolute/main arm64 libsharpyuv0 arm64 1.5.0-0.1 [16.9 kB] 980s Get:71 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebp7 arm64 1.5.0-0.1 [194 kB] 980s Get:72 http://ftpmaster.internal/ubuntu resolute/main arm64 libtiff6 arm64 4.7.0-3ubuntu3 [196 kB] 980s Get:73 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebpdemux2 arm64 1.5.0-0.1 [12.5 kB] 980s Get:74 http://ftpmaster.internal/ubuntu resolute/main arm64 libwebpmux3 arm64 1.5.0-0.1 [25.4 kB] 980s Get:75 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-pil arm64 11.3.0-1ubuntu2 [492 kB] 980s Get:76 http://ftpmaster.internal/ubuntu resolute/main arm64 libpixman-1-0 arm64 0.46.4-1 [204 kB] 980s Get:77 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-render0 arm64 1.17.0-2build1 [18.1 kB] 980s Get:78 http://ftpmaster.internal/ubuntu resolute/main arm64 libxcb-shm0 arm64 1.17.0-2build1 [6234 B] 980s Get:79 http://ftpmaster.internal/ubuntu resolute/main arm64 libcairo2 arm64 1.18.4-1build1 [592 kB] 980s Get:80 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-cairo arm64 1.27.0-2build1 [141 kB] 980s Get:81 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-freetype all 2.5.1-2 [92.2 kB] 980s Get:82 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-rlpycairo all 0.3.0-4 [9332 B] 980s Get:83 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-reportlab all 4.4.4-2 [1147 kB] 980s Get:84 http://ftpmaster.internal/ubuntu resolute/main arm64 sgml-base all 1.31+nmu1 [11.0 kB] 980s Get:85 http://ftpmaster.internal/ubuntu resolute/main arm64 xml-core all 0.19 [20.3 kB] 980s Get:86 http://ftpmaster.internal/ubuntu resolute/universe arm64 w3c-sgml-lib all 1.3-3 [280 kB] 980s Get:87 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-biopython arm64 1.85+dfsg-4 [1710 kB] 980s Get:88 http://ftpmaster.internal/ubuntu resolute/universe arm64 fonts-lyx all 2.4.4-2 [171 kB] 980s Get:89 http://ftpmaster.internal/ubuntu resolute/universe arm64 python-matplotlib-data all 3.10.7+dfsg1-1 [2930 kB] 980s Get:90 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-contourpy arm64 1.3.1-2 [240 kB] 980s Get:91 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-cycler all 0.12.1-2 [9850 B] 980s Get:92 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-brotli arm64 1.1.0-2build6 [343 kB] 980s Get:93 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-platformdirs all 4.3.7-1 [16.9 kB] 980s Get:94 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-fs all 2.4.16-9ubuntu1 [91.5 kB] 980s Get:95 http://ftpmaster.internal/ubuntu resolute/main arm64 libxslt1.1 arm64 1.1.43-0.3 [172 kB] 980s Get:96 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-lxml arm64 6.0.2-1 [2155 kB] 980s Get:97 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-lz4 arm64 4.4.4+dfsg-3 [27.6 kB] 980s Get:98 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-decorator all 5.2.1-2 [28.1 kB] 980s Get:99 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-scipy arm64 1.15.3-1ubuntu1 [18.7 MB] 981s Get:100 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-mpmath all 1.3.0-2 [423 kB] 981s Get:101 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-sympy all 1.14.0-2 [4306 kB] 981s Get:102 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-ufolib2 all 0.17.1+dfsg1-1 [33.5 kB] 981s Get:103 http://ftpmaster.internal/ubuntu resolute/main arm64 libpython3.14-stdlib arm64 3.14.0-4 [2349 kB] 982s Get:104 http://ftpmaster.internal/ubuntu resolute/main arm64 python3.14 arm64 3.14.0-4 [805 kB] 982s Get:105 http://ftpmaster.internal/ubuntu resolute-proposed/main arm64 python3-all arm64 3.13.7-2 [890 B] 982s Get:106 http://ftpmaster.internal/ubuntu resolute/universe arm64 libzopfli1 arm64 1.0.3-3 [108 kB] 982s Get:107 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-zopfli arm64 0.4.0-1 [10.8 kB] 982s Get:108 http://ftpmaster.internal/ubuntu resolute/universe arm64 unicode-data all 16.0.0-1 [9513 kB] 982s Get:109 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-fonttools arm64 4.57.0-2build1 [1648 kB] 983s Get:110 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-kiwisolver arm64 1.4.10~rc0-1 [60.1 kB] 983s Get:111 http://ftpmaster.internal/ubuntu resolute/universe arm64 libqhull-r8.0 arm64 2020.2-7 [190 kB] 983s Get:112 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-matplotlib arm64 3.10.7+dfsg1-1 [17.1 MB] 983s Get:113 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-pytz all 2025.2-4 [32.3 kB] 983s Get:114 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pandas-lib arm64 2.3.3+dfsg-1ubuntu1 [6979 kB] 983s Get:115 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pandas all 2.3.3+dfsg-1ubuntu1 [2948 kB] 983s Get:116 http://ftpmaster.internal/ubuntu resolute/universe arm64 cython3 arm64 3.1.6+dfsg-1ubuntu1 [3180 kB] 983s Get:117 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-joblib all 1.4.2-4 [205 kB] 983s Get:118 http://ftpmaster.internal/ubuntu resolute/main arm64 python3-networkx all 3.2.1-4ubuntu1 [11.5 MB] 984s Get:119 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pomegranate arm64 0.15.0-2 [4260 kB] 984s Get:120 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pyfaidx all 0.8.1.3-2 [29.7 kB] 984s Get:121 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhtscodecs2 arm64 1.6.1-2 [82.7 kB] 984s Get:122 http://ftpmaster.internal/ubuntu resolute/universe arm64 libhts3t64 arm64 1.22.1+ds2-1 [442 kB] 984s Get:123 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pysam arm64 0.23.3+ds-2 [4348 kB] 984s Get:124 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-sklearn-lib arm64 1.7.2+dfsg-3ubuntu1 [6003 kB] 984s Get:125 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-threadpoolctl all 3.1.0-1 [21.3 kB] 984s Get:126 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-sklearn all 1.7.2+dfsg-3ubuntu1 [2616 kB] 984s Get:127 http://ftpmaster.internal/ubuntu resolute/main arm64 zip arm64 3.0-15ubuntu2 [175 kB] 984s Get:128 http://ftpmaster.internal/ubuntu resolute/main arm64 unzip arm64 6.0-28ubuntu7 [176 kB] 984s Get:129 http://ftpmaster.internal/ubuntu resolute/main arm64 libpaper2 arm64 2.2.5-0.3 [17.3 kB] 984s Get:130 http://ftpmaster.internal/ubuntu resolute/main arm64 libpaper-utils arm64 2.2.5-0.3 [15.4 kB] 984s Get:131 http://ftpmaster.internal/ubuntu resolute/main arm64 xdg-utils all 1.2.1-2ubuntu1 [66.0 kB] 984s Get:132 http://ftpmaster.internal/ubuntu resolute/main arm64 fontconfig arm64 2.15.0-2.3ubuntu1 [191 kB] 984s Get:133 http://ftpmaster.internal/ubuntu resolute/main arm64 libthai-data all 0.1.29-2build1 [158 kB] 984s Get:134 http://ftpmaster.internal/ubuntu resolute/main arm64 libdatrie1 arm64 0.2.13-4 [19.1 kB] 984s Get:135 http://ftpmaster.internal/ubuntu resolute/main arm64 libthai0 arm64 0.1.29-2build1 [18.2 kB] 984s Get:136 http://ftpmaster.internal/ubuntu resolute/main arm64 libpango-1.0-0 arm64 1.56.3-2 [237 kB] 984s Get:137 http://ftpmaster.internal/ubuntu resolute/main arm64 libpangoft2-1.0-0 arm64 1.56.3-2 [50.2 kB] 984s Get:138 http://ftpmaster.internal/ubuntu resolute/main arm64 libpangocairo-1.0-0 arm64 1.56.3-2 [27.7 kB] 984s Get:139 http://ftpmaster.internal/ubuntu resolute/main arm64 libice6 arm64 2:1.1.1-1 [42.3 kB] 984s Get:140 http://ftpmaster.internal/ubuntu resolute/main arm64 libsm6 arm64 2:1.2.6-1 [16.6 kB] 984s Get:141 http://ftpmaster.internal/ubuntu resolute/main arm64 libxt6t64 arm64 1:1.2.1-1.3 [168 kB] 984s Get:142 http://ftpmaster.internal/ubuntu resolute/universe arm64 r-base-core arm64 4.5.2-1 [28.6 MB] 985s Get:143 http://ftpmaster.internal/ubuntu resolute/universe arm64 r-bioc-biocgenerics all 0.52.0-2 [624 kB] 985s Get:144 http://ftpmaster.internal/ubuntu resolute/universe arm64 r-bioc-dnacopy arm64 1.80.0-2 [497 kB] 985s Get:145 http://ftpmaster.internal/ubuntu resolute/universe arm64 cnvkit all 0.9.12-1 [20.6 MB] 986s Get:146 http://ftpmaster.internal/ubuntu resolute/main arm64 libdebhelper-perl all 13.24.2ubuntu1 [95.7 kB] 986s Get:147 http://ftpmaster.internal/ubuntu resolute/main arm64 libtool all 2.5.4-7 [169 kB] 986s Get:148 http://ftpmaster.internal/ubuntu resolute/main arm64 dh-autoreconf all 21 [12.5 kB] 986s Get:149 http://ftpmaster.internal/ubuntu resolute/main arm64 libarchive-zip-perl all 1.68-1 [90.2 kB] 986s Get:150 http://ftpmaster.internal/ubuntu resolute/main arm64 libfile-stripnondeterminism-perl all 1.15.0-1 [20.5 kB] 986s Get:151 http://ftpmaster.internal/ubuntu resolute/main arm64 dh-strip-nondeterminism all 1.15.0-1 [5090 B] 986s Get:152 http://ftpmaster.internal/ubuntu resolute/main arm64 debugedit arm64 1:5.2-3 [49.1 kB] 986s Get:153 http://ftpmaster.internal/ubuntu resolute/main arm64 dwz arm64 0.16-2 [113 kB] 986s Get:154 http://ftpmaster.internal/ubuntu resolute/main arm64 gettext arm64 0.23.2-1 [998 kB] 986s Get:155 http://ftpmaster.internal/ubuntu resolute/main arm64 intltool-debian all 0.35.0+20060710.6 [23.2 kB] 986s Get:156 http://ftpmaster.internal/ubuntu resolute/main arm64 po-debconf all 1.0.21+nmu1 [233 kB] 986s Get:157 http://ftpmaster.internal/ubuntu resolute/main arm64 debhelper all 13.24.2ubuntu1 [896 kB] 986s Get:158 http://ftpmaster.internal/ubuntu resolute/universe arm64 dh-python all 6.20250414 [119 kB] 986s Get:159 http://ftpmaster.internal/ubuntu resolute/main arm64 libgpgmepp6t64 arm64 1.24.2-3ubuntu2 [117 kB] 986s Get:160 http://ftpmaster.internal/ubuntu resolute/main arm64 libpoppler147 arm64 25.03.0-11.1 [1149 kB] 986s Get:161 http://ftpmaster.internal/ubuntu resolute/main arm64 poppler-utils arm64 25.03.0-11.1 [213 kB] 986s Get:162 http://ftpmaster.internal/ubuntu resolute/universe arm64 pybuild-plugin-autopkgtest all 6.20250414 [1746 B] 986s Get:163 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pyproject-hooks all 1.2.0-1 [10.2 kB] 986s Get:164 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-wheel all 0.46.1-2 [22.1 kB] 986s Get:165 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-build all 1.2.2-4 [31.0 kB] 986s Get:166 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-installer all 0.7.0+dfsg1-3 [17.4 kB] 986s Get:167 http://ftpmaster.internal/ubuntu resolute/universe arm64 pybuild-plugin-pyproject all 6.20250414 [1728 B] 986s Get:168 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-iniconfig all 2.1.0-1 [6840 B] 986s Get:169 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pluggy all 1.6.0-1 [21.0 kB] 986s Get:170 http://ftpmaster.internal/ubuntu resolute/universe arm64 python3-pytest all 8.3.5-2 [252 kB] 987s Preconfiguring packages ... 987s Fetched 260 MB in 14s (18.8 MB/s) 987s Selecting previously unselected package python3-numpy-dev:arm64. 987s (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 ... 88137 files and directories currently installed.) 987s Preparing to unpack .../000-python3-numpy-dev_1%3a2.2.4+ds-1ubuntu1_arm64.deb ... 987s Unpacking python3-numpy-dev:arm64 (1:2.2.4+ds-1ubuntu1) ... 987s Selecting previously unselected package libblas3:arm64. 987s Preparing to unpack .../001-libblas3_3.12.1-7_arm64.deb ... 987s Unpacking libblas3:arm64 (3.12.1-7) ... 987s Selecting previously unselected package libgfortran5:arm64. 987s Preparing to unpack .../002-libgfortran5_15.2.0-7ubuntu1_arm64.deb ... 987s Unpacking libgfortran5:arm64 (15.2.0-7ubuntu1) ... 987s Selecting previously unselected package liblapack3:arm64. 987s Preparing to unpack .../003-liblapack3_3.12.1-7_arm64.deb ... 987s Unpacking liblapack3:arm64 (3.12.1-7) ... 987s Selecting previously unselected package python3-numpy. 987s Preparing to unpack .../004-python3-numpy_1%3a2.2.4+ds-1ubuntu1_arm64.deb ... 987s Unpacking python3-numpy (1:2.2.4+ds-1ubuntu1) ... 988s Selecting previously unselected package libpython3.14-minimal:arm64. 988s Preparing to unpack .../005-libpython3.14-minimal_3.14.0-4_arm64.deb ... 988s Unpacking libpython3.14-minimal:arm64 (3.14.0-4) ... 988s Selecting previously unselected package python3.14-minimal. 988s Preparing to unpack .../006-python3.14-minimal_3.14.0-4_arm64.deb ... 988s Unpacking python3.14-minimal (3.14.0-4) ... 988s Selecting previously unselected package m4. 988s Preparing to unpack .../007-m4_1.4.20-2_arm64.deb ... 988s Unpacking m4 (1.4.20-2) ... 988s Selecting previously unselected package autoconf. 988s Preparing to unpack .../008-autoconf_2.72-3.1ubuntu1_all.deb ... 988s Unpacking autoconf (2.72-3.1ubuntu1) ... 988s Selecting previously unselected package autotools-dev. 988s Preparing to unpack .../009-autotools-dev_20240727.1_all.deb ... 988s Unpacking autotools-dev (20240727.1) ... 988s Selecting previously unselected package automake. 988s Preparing to unpack .../010-automake_1%3a1.18.1-2_all.deb ... 988s Unpacking automake (1:1.18.1-2) ... 988s Selecting previously unselected package autopoint. 988s Preparing to unpack .../011-autopoint_0.23.2-1_all.deb ... 988s Unpacking autopoint (0.23.2-1) ... 988s Selecting previously unselected package libtcl8.6:arm64. 988s Preparing to unpack .../012-libtcl8.6_8.6.17+dfsg-1_arm64.deb ... 988s Unpacking libtcl8.6:arm64 (8.6.17+dfsg-1) ... 989s Selecting previously unselected package fonts-dejavu-mono. 989s Preparing to unpack .../013-fonts-dejavu-mono_2.37-8_all.deb ... 989s Unpacking fonts-dejavu-mono (2.37-8) ... 989s Selecting previously unselected package fonts-dejavu-core. 989s Preparing to unpack .../014-fonts-dejavu-core_2.37-8_all.deb ... 989s Unpacking fonts-dejavu-core (2.37-8) ... 989s Selecting previously unselected package libfontenc1:arm64. 989s Preparing to unpack .../015-libfontenc1_1%3a1.1.8-1build1_arm64.deb ... 989s Unpacking libfontenc1:arm64 (1:1.1.8-1build1) ... 989s Selecting previously unselected package x11-common. 989s Preparing to unpack .../016-x11-common_1%3a7.7+24ubuntu1_all.deb ... 989s Unpacking x11-common (1:7.7+24ubuntu1) ... 989s Selecting previously unselected package xfonts-encodings. 989s Preparing to unpack .../017-xfonts-encodings_1%3a1.0.5-0ubuntu2_all.deb ... 989s Unpacking xfonts-encodings (1:1.0.5-0ubuntu2) ... 989s Selecting previously unselected package xfonts-utils. 989s Preparing to unpack .../018-xfonts-utils_1%3a7.7+7_arm64.deb ... 989s Unpacking xfonts-utils (1:7.7+7) ... 989s Selecting previously unselected package fonts-urw-base35. 989s Preparing to unpack .../019-fonts-urw-base35_20200910-8_all.deb ... 989s Unpacking fonts-urw-base35 (20200910-8) ... 990s Selecting previously unselected package fontconfig-config. 990s Preparing to unpack .../020-fontconfig-config_2.15.0-2.3ubuntu1_arm64.deb ... 990s Unpacking fontconfig-config (2.15.0-2.3ubuntu1) ... 990s Selecting previously unselected package libfontconfig1:arm64. 990s Preparing to unpack .../021-libfontconfig1_2.15.0-2.3ubuntu1_arm64.deb ... 990s Unpacking libfontconfig1:arm64 (2.15.0-2.3ubuntu1) ... 990s Selecting previously unselected package libxrender1:arm64. 990s Preparing to unpack .../022-libxrender1_1%3a0.9.12-1_arm64.deb ... 990s Unpacking libxrender1:arm64 (1:0.9.12-1) ... 990s Selecting previously unselected package libxft2:arm64. 990s Preparing to unpack .../023-libxft2_2.3.6-1build1_arm64.deb ... 990s Unpacking libxft2:arm64 (2.3.6-1build1) ... 991s Selecting previously unselected package libxss1:arm64. 991s Preparing to unpack .../024-libxss1_1%3a1.2.3-1build3_arm64.deb ... 991s Unpacking libxss1:arm64 (1:1.2.3-1build3) ... 991s Selecting previously unselected package libtk8.6:arm64. 991s Preparing to unpack .../025-libtk8.6_8.6.17-1_arm64.deb ... 991s Unpacking libtk8.6:arm64 (8.6.17-1) ... 991s Selecting previously unselected package tk8.6-blt2.5. 991s Preparing to unpack .../026-tk8.6-blt2.5_2.5.3+dfsg-8_arm64.deb ... 991s Unpacking tk8.6-blt2.5 (2.5.3+dfsg-8) ... 991s Selecting previously unselected package blt. 991s Preparing to unpack .../027-blt_2.5.3+dfsg-8_arm64.deb ... 991s Unpacking blt (2.5.3+dfsg-8) ... 991s Selecting previously unselected package libisl23:arm64. 991s Preparing to unpack .../028-libisl23_0.27-1_arm64.deb ... 991s Unpacking libisl23:arm64 (0.27-1) ... 991s Selecting previously unselected package libmpc3:arm64. 991s Preparing to unpack .../029-libmpc3_1.3.1-2_arm64.deb ... 991s Unpacking libmpc3:arm64 (1.3.1-2) ... 991s Selecting previously unselected package cpp-15-aarch64-linux-gnu. 991s Preparing to unpack .../030-cpp-15-aarch64-linux-gnu_15.2.0-7ubuntu1_arm64.deb ... 991s Unpacking cpp-15-aarch64-linux-gnu (15.2.0-7ubuntu1) ... 991s Selecting previously unselected package cpp-15. 991s Preparing to unpack .../031-cpp-15_15.2.0-7ubuntu1_arm64.deb ... 991s Unpacking cpp-15 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package cpp-aarch64-linux-gnu. 992s Preparing to unpack .../032-cpp-aarch64-linux-gnu_4%3a15.2.0-4ubuntu1_arm64.deb ... 992s Unpacking cpp-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ... 992s Selecting previously unselected package cpp. 992s Preparing to unpack .../033-cpp_4%3a15.2.0-4ubuntu1_arm64.deb ... 992s Unpacking cpp (4:15.2.0-4ubuntu1) ... 992s Selecting previously unselected package libcc1-0:arm64. 992s Preparing to unpack .../034-libcc1-0_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libcc1-0:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package libgomp1:arm64. 992s Preparing to unpack .../035-libgomp1_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libgomp1:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package libitm1:arm64. 992s Preparing to unpack .../036-libitm1_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libitm1:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package libasan8:arm64. 992s Preparing to unpack .../037-libasan8_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libasan8:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package liblsan0:arm64. 992s Preparing to unpack .../038-liblsan0_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking liblsan0:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package libtsan2:arm64. 992s Preparing to unpack .../039-libtsan2_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libtsan2:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package libubsan1:arm64. 992s Preparing to unpack .../040-libubsan1_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libubsan1:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package libhwasan0:arm64. 992s Preparing to unpack .../041-libhwasan0_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libhwasan0:arm64 (15.2.0-7ubuntu1) ... 992s Selecting previously unselected package libgcc-15-dev:arm64. 992s Preparing to unpack .../042-libgcc-15-dev_15.2.0-7ubuntu1_arm64.deb ... 992s Unpacking libgcc-15-dev:arm64 (15.2.0-7ubuntu1) ... 993s Selecting previously unselected package gcc-15-aarch64-linux-gnu. 993s Preparing to unpack .../043-gcc-15-aarch64-linux-gnu_15.2.0-7ubuntu1_arm64.deb ... 993s Unpacking gcc-15-aarch64-linux-gnu (15.2.0-7ubuntu1) ... 994s Selecting previously unselected package gcc-15. 994s Preparing to unpack .../044-gcc-15_15.2.0-7ubuntu1_arm64.deb ... 994s Unpacking gcc-15 (15.2.0-7ubuntu1) ... 994s Selecting previously unselected package gcc-aarch64-linux-gnu. 994s Preparing to unpack .../045-gcc-aarch64-linux-gnu_4%3a15.2.0-4ubuntu1_arm64.deb ... 994s Unpacking gcc-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ... 994s Selecting previously unselected package gcc. 994s Preparing to unpack .../046-gcc_4%3a15.2.0-4ubuntu1_arm64.deb ... 994s Unpacking gcc (4:15.2.0-4ubuntu1) ... 994s Selecting previously unselected package libstdc++-15-dev:arm64. 994s Preparing to unpack .../047-libstdc++-15-dev_15.2.0-7ubuntu1_arm64.deb ... 994s Unpacking libstdc++-15-dev:arm64 (15.2.0-7ubuntu1) ... 994s Selecting previously unselected package g++-15-aarch64-linux-gnu. 994s Preparing to unpack .../048-g++-15-aarch64-linux-gnu_15.2.0-7ubuntu1_arm64.deb ... 994s Unpacking g++-15-aarch64-linux-gnu (15.2.0-7ubuntu1) ... 995s Selecting previously unselected package g++-15. 995s Preparing to unpack .../049-g++-15_15.2.0-7ubuntu1_arm64.deb ... 995s Unpacking g++-15 (15.2.0-7ubuntu1) ... 995s Selecting previously unselected package g++-aarch64-linux-gnu. 995s Preparing to unpack .../050-g++-aarch64-linux-gnu_4%3a15.2.0-4ubuntu1_arm64.deb ... 995s Unpacking g++-aarch64-linux-gnu (4:15.2.0-4ubuntu1) ... 995s Selecting previously unselected package g++. 995s Preparing to unpack .../051-g++_4%3a15.2.0-4ubuntu1_arm64.deb ... 995s Unpacking g++ (4:15.2.0-4ubuntu1) ... 995s Selecting previously unselected package build-essential. 995s Preparing to unpack .../052-build-essential_12.12ubuntu1_arm64.deb ... 995s Unpacking build-essential (12.12ubuntu1) ... 995s Selecting previously unselected package python3-charset-normalizer. 995s Preparing to unpack .../053-python3-charset-normalizer_3.4.3-1_arm64.deb ... 995s Unpacking python3-charset-normalizer (3.4.3-1) ... 995s Selecting previously unselected package python3.14-tk. 995s Preparing to unpack .../054-python3.14-tk_3.14.0-4_arm64.deb ... 995s Unpacking python3.14-tk (3.14.0-4) ... 995s Selecting previously unselected package python3.13-tk. 995s Preparing to unpack .../055-python3.13-tk_3.13.9-1_arm64.deb ... 995s Unpacking python3.13-tk (3.13.9-1) ... 995s Selecting previously unselected package python3-tk:arm64. 995s Preparing to unpack .../056-python3-tk_3.13.9-1_arm64.deb ... 995s Unpacking python3-tk:arm64 (3.13.9-1) ... 995s Selecting previously unselected package python3-pil.imagetk:arm64. 995s Preparing to unpack .../057-python3-pil.imagetk_11.3.0-1ubuntu2_arm64.deb ... 995s Unpacking python3-pil.imagetk:arm64 (11.3.0-1ubuntu2) ... 995s Selecting previously unselected package libimagequant0:arm64. 995s Preparing to unpack .../058-libimagequant0_2.18.0-1build1_arm64.deb ... 995s Unpacking libimagequant0:arm64 (2.18.0-1build1) ... 996s Selecting previously unselected package libjpeg-turbo8:arm64. 996s Preparing to unpack .../059-libjpeg-turbo8_2.1.5-4ubuntu2_arm64.deb ... 996s Unpacking libjpeg-turbo8:arm64 (2.1.5-4ubuntu2) ... 996s Selecting previously unselected package libjpeg8:arm64. 996s Preparing to unpack .../060-libjpeg8_8c-2ubuntu11_arm64.deb ... 996s Unpacking libjpeg8:arm64 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996s Preparing to unpack .../066-libdeflate0_1.23-2_arm64.deb ... 996s Unpacking libdeflate0:arm64 (1.23-2) ... 996s Selecting previously unselected package libjbig0:arm64. 996s Preparing to unpack .../067-libjbig0_2.1-6.1ubuntu2_arm64.deb ... 996s Unpacking libjbig0:arm64 (2.1-6.1ubuntu2) ... 996s Selecting previously unselected package liblerc4:arm64. 996s Preparing to unpack .../068-liblerc4_4.0.0+ds-5ubuntu1_arm64.deb ... 996s Unpacking liblerc4:arm64 (4.0.0+ds-5ubuntu1) ... 996s Selecting previously unselected package libsharpyuv0:arm64. 996s Preparing to unpack .../069-libsharpyuv0_1.5.0-0.1_arm64.deb ... 996s Unpacking libsharpyuv0:arm64 (1.5.0-0.1) ... 996s Selecting previously unselected package libwebp7:arm64. 996s Preparing to unpack .../070-libwebp7_1.5.0-0.1_arm64.deb ... 996s Unpacking libwebp7:arm64 (1.5.0-0.1) ... 996s Selecting previously unselected package libtiff6:arm64. 996s Preparing to unpack .../071-libtiff6_4.7.0-3ubuntu3_arm64.deb ... 996s Unpacking libtiff6:arm64 (4.7.0-3ubuntu3) ... 996s Selecting previously unselected package libwebpdemux2:arm64. 996s Preparing to unpack .../072-libwebpdemux2_1.5.0-0.1_arm64.deb ... 996s Unpacking libwebpdemux2:arm64 (1.5.0-0.1) ... 996s Selecting previously unselected package libwebpmux3:arm64. 996s Preparing to unpack .../073-libwebpmux3_1.5.0-0.1_arm64.deb ... 996s Unpacking libwebpmux3:arm64 (1.5.0-0.1) ... 997s Selecting previously unselected package python3-pil:arm64. 997s Preparing to unpack .../074-python3-pil_11.3.0-1ubuntu2_arm64.deb ... 997s Unpacking python3-pil:arm64 (11.3.0-1ubuntu2) ... 997s Selecting previously unselected package libpixman-1-0:arm64. 997s Preparing to unpack .../075-libpixman-1-0_0.46.4-1_arm64.deb ... 997s Unpacking libpixman-1-0:arm64 (0.46.4-1) ... 997s Selecting previously unselected package libxcb-render0:arm64. 997s Preparing to unpack .../076-libxcb-render0_1.17.0-2build1_arm64.deb ... 997s Unpacking libxcb-render0:arm64 (1.17.0-2build1) ... 997s 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python3-all. 1001s Preparing to unpack .../104-python3-all_3.13.7-2_arm64.deb ... 1001s Unpacking python3-all (3.13.7-2) ... 1001s Selecting previously unselected package libzopfli1. 1001s Preparing to unpack .../105-libzopfli1_1.0.3-3_arm64.deb ... 1001s Unpacking libzopfli1 (1.0.3-3) ... 1001s Selecting previously unselected package python3-zopfli. 1001s Preparing to unpack .../106-python3-zopfli_0.4.0-1_arm64.deb ... 1001s Unpacking python3-zopfli (0.4.0-1) ... 1001s Selecting previously unselected package unicode-data. 1001s Preparing to unpack .../107-unicode-data_16.0.0-1_all.deb ... 1001s Unpacking unicode-data (16.0.0-1) ... 1002s Selecting previously unselected package python3-fonttools. 1002s Preparing to unpack .../108-python3-fonttools_4.57.0-2build1_arm64.deb ... 1002s Unpacking python3-fonttools (4.57.0-2build1) ... 1002s Selecting previously unselected package python3-kiwisolver. 1002s Preparing to unpack .../109-python3-kiwisolver_1.4.10~rc0-1_arm64.deb ... 1002s 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python3-pandas (2.3.3+dfsg-1ubuntu1) ... 1004s Selecting previously unselected package cython3. 1004s Preparing to unpack .../115-cython3_3.1.6+dfsg-1ubuntu1_arm64.deb ... 1004s Unpacking cython3 (3.1.6+dfsg-1ubuntu1) ... 1004s Selecting previously unselected package python3-joblib. 1004s Preparing to unpack .../116-python3-joblib_1.4.2-4_all.deb ... 1004s Unpacking python3-joblib (1.4.2-4) ... 1004s Selecting previously unselected package python3-networkx. 1004s Preparing to unpack .../117-python3-networkx_3.2.1-4ubuntu1_all.deb ... 1004s Unpacking python3-networkx (3.2.1-4ubuntu1) ... 1005s Selecting previously unselected package python3-pomegranate. 1005s Preparing to unpack .../118-python3-pomegranate_0.15.0-2_arm64.deb ... 1005s Unpacking python3-pomegranate (0.15.0-2) ... 1006s Selecting previously unselected package python3-pyfaidx. 1006s Preparing to unpack .../119-python3-pyfaidx_0.8.1.3-2_all.deb ... 1006s Unpacking python3-pyfaidx (0.8.1.3-2) ... 1006s Selecting previously unselected package libhtscodecs2:arm64. 1006s Preparing to unpack .../120-libhtscodecs2_1.6.1-2_arm64.deb ... 1006s Unpacking libhtscodecs2:arm64 (1.6.1-2) ... 1006s Selecting previously unselected package libhts3t64:arm64. 1006s Preparing to unpack .../121-libhts3t64_1.22.1+ds2-1_arm64.deb ... 1006s Unpacking libhts3t64:arm64 (1.22.1+ds2-1) ... 1006s Selecting previously unselected package python3-pysam. 1006s Preparing to unpack .../122-python3-pysam_0.23.3+ds-2_arm64.deb ... 1006s Unpacking python3-pysam (0.23.3+ds-2) ... 1007s Selecting previously unselected package python3-sklearn-lib:arm64. 1007s Preparing to unpack .../123-python3-sklearn-lib_1.7.2+dfsg-3ubuntu1_arm64.deb ... 1007s Unpacking python3-sklearn-lib:arm64 (1.7.2+dfsg-3ubuntu1) ... 1007s Selecting previously unselected package python3-threadpoolctl. 1007s Preparing to unpack .../124-python3-threadpoolctl_3.1.0-1_all.deb ... 1007s Unpacking python3-threadpoolctl (3.1.0-1) ... 1007s Selecting previously unselected package python3-sklearn. 1007s Preparing to unpack .../125-python3-sklearn_1.7.2+dfsg-3ubuntu1_all.deb ... 1007s Unpacking python3-sklearn (1.7.2+dfsg-3ubuntu1) ... 1007s Selecting previously unselected package zip. 1007s Preparing to unpack .../126-zip_3.0-15ubuntu2_arm64.deb ... 1007s Unpacking zip (3.0-15ubuntu2) ... 1008s Selecting previously unselected package unzip. 1008s Preparing to unpack .../127-unzip_6.0-28ubuntu7_arm64.deb ... 1008s Unpacking unzip (6.0-28ubuntu7) ... 1008s Selecting previously unselected package libpaper2:arm64. 1008s Preparing to unpack .../128-libpaper2_2.2.5-0.3_arm64.deb ... 1008s Unpacking libpaper2:arm64 (2.2.5-0.3) ... 1008s Selecting previously unselected package libpaper-utils. 1008s Preparing to unpack .../129-libpaper-utils_2.2.5-0.3_arm64.deb ... 1008s Unpacking libpaper-utils (2.2.5-0.3) ... 1008s Selecting previously unselected package xdg-utils. 1008s Preparing to unpack .../130-xdg-utils_1.2.1-2ubuntu1_all.deb ... 1008s Unpacking xdg-utils 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(257.9-0ubuntu2) ... 1136s Processing triggers for man-db (2.13.1-1) ... 1138s Processing triggers for install-info (7.2-5) ... 1139s Processing triggers for sgml-base (1.31+nmu1) ... 1139s Setting up w3c-sgml-lib (1.3-3) ... 1192s Setting up python3-biopython (1.85+dfsg-4) ... 1196s Setting up cnvkit (0.9.12-1) ... 1209s autopkgtest [14:22:43]: test pybuild-autopkgtest: pybuild-autopkgtest 1209s autopkgtest [14:22:43]: test pybuild-autopkgtest: [----------------------- 1210s pybuild-autopkgtest 1210s I: pybuild base:311: cd /tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build; python3.14 -m pytest test 1212s ============================= test session starts ============================== 1212s platform linux -- Python 3.14.0, pytest-8.3.5, pluggy-1.6.0 1212s rootdir: /tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build 1212s configfile: pyproject.toml 1212s plugins: typeguard-4.4.2 1212s collected 0 items / 5 errors 1212s 1212s ==================================== ERRORS ==================================== 1212s _____________________ ERROR collecting test/test_cnvlib.py _____________________ 1212s ImportError while importing test module '/tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build/test/test_cnvlib.py'. 1212s Hint: make sure your test modules/packages have valid Python names. 1212s Traceback: 1212s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:23: in 1212s from . import multiarray 1212s /usr/lib/python3/dist-packages/numpy/_core/multiarray.py:10: in 1212s from . import overrides 1212s /usr/lib/python3/dist-packages/numpy/_core/overrides.py:7: in 1212s from numpy._core._multiarray_umath import ( 1212s E ModuleNotFoundError: No module named 'numpy._core._multiarray_umath' 1212s 1212s During handling of the above exception, another exception occurred: 1212s /usr/lib/python3/dist-packages/numpy/__init__.py:114: in 1212s from numpy.__config__ import show_config 1212s /usr/lib/python3/dist-packages/numpy/__config__.py:4: in 1212s from numpy._core._multiarray_umath import ( 1212s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:49: in 1212s raise ImportError(msg) 1212s E ImportError: 1212s E 1212s E IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! 1212s E 1212s E Importing the numpy C-extensions failed. This error can happen for 1212s E many reasons, often due to issues with your setup or how NumPy was 1212s E installed. 1212s E 1212s E We have compiled some common reasons and troubleshooting tips at: 1212s E 1212s E https://numpy.org/devdocs/user/troubleshooting-importerror.html 1212s E 1212s E Please note and check the following: 1212s E 1212s E * The Python version is: Python3.14 from "/usr/bin/python3.14" 1212s E * The NumPy version is: "2.2.4" 1212s E 1212s E and make sure that they are the versions you expect. 1212s E Please carefully study the documentation linked above for further help. 1212s E 1212s E Original error was: No module named 'numpy._core._multiarray_umath' 1212s 1212s The above exception was the direct cause of the following exception: 1212s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 1212s return _bootstrap._gcd_import(name[level:], package, level) 1212s test/test_cnvlib.py:14: in 1212s import numpy as np 1212s /usr/lib/python3/dist-packages/numpy/__init__.py:119: in 1212s raise ImportError(msg) from e 1212s E ImportError: Error importing numpy: you should not try to import numpy from 1212s E its source directory; please exit the numpy source tree, and relaunch 1212s E your python interpreter from there. 1212s ____________________ ERROR collecting test/test_commands.py ____________________ 1212s ImportError while importing test module '/tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build/test/test_commands.py'. 1212s Hint: make sure your test modules/packages have valid Python names. 1212s Traceback: 1212s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:23: in 1212s from . import multiarray 1212s /usr/lib/python3/dist-packages/numpy/_core/multiarray.py:10: in 1212s from . import overrides 1212s /usr/lib/python3/dist-packages/numpy/_core/overrides.py:7: in 1212s from numpy._core._multiarray_umath import ( 1212s E ModuleNotFoundError: No module named 'numpy._core._multiarray_umath' 1212s 1212s During handling of the above exception, another exception occurred: 1212s /usr/lib/python3/dist-packages/numpy/__init__.py:114: in 1212s from numpy.__config__ import show_config 1212s /usr/lib/python3/dist-packages/numpy/__config__.py:4: in 1212s from numpy._core._multiarray_umath import ( 1212s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:49: in 1212s raise ImportError(msg) 1212s E ImportError: 1212s E 1212s E IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! 1212s E 1212s E Importing the numpy C-extensions failed. This error can happen for 1212s E many reasons, often due to issues with your setup or how NumPy was 1212s E installed. 1212s E 1212s E We have compiled some common reasons and troubleshooting tips at: 1212s E 1212s E https://numpy.org/devdocs/user/troubleshooting-importerror.html 1212s E 1212s E Please note and check the following: 1212s E 1212s E * The Python version is: Python3.14 from "/usr/bin/python3.14" 1212s E * The NumPy version is: "2.2.4" 1212s E 1212s E and make sure that they are the versions you expect. 1212s E Please carefully study the documentation linked above for further help. 1212s E 1212s E Original error was: No module named 'numpy._core._multiarray_umath' 1212s 1212s The above exception was the direct cause of the following exception: 1212s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 1212s return _bootstrap._gcd_import(name[level:], package, level) 1212s test/test_commands.py:18: in 1212s import numpy as np 1212s /usr/lib/python3/dist-packages/numpy/__init__.py:119: in 1212s raise ImportError(msg) from e 1212s E ImportError: Error importing numpy: you should not try to import numpy from 1212s E its source directory; please exit the numpy source tree, and relaunch 1212s E your python interpreter from there. 1212s _____________________ ERROR collecting test/test_genome.py _____________________ 1212s ImportError while importing test module '/tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build/test/test_genome.py'. 1212s Hint: make sure your test modules/packages have valid Python names. 1212s Traceback: 1212s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:23: in 1212s from . import multiarray 1212s /usr/lib/python3/dist-packages/numpy/_core/multiarray.py:10: in 1212s from . import overrides 1212s /usr/lib/python3/dist-packages/numpy/_core/overrides.py:7: in 1212s from numpy._core._multiarray_umath import ( 1212s E ModuleNotFoundError: No module named 'numpy._core._multiarray_umath' 1212s 1212s During handling of the above exception, another exception occurred: 1212s /usr/lib/python3/dist-packages/numpy/__init__.py:114: in 1212s from numpy.__config__ import show_config 1212s /usr/lib/python3/dist-packages/numpy/__config__.py:4: in 1212s from numpy._core._multiarray_umath import ( 1212s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:49: in 1212s raise ImportError(msg) 1212s E ImportError: 1212s E 1212s E IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! 1212s E 1212s E Importing the numpy C-extensions failed. This error can happen for 1212s E many reasons, often due to issues with your setup or how NumPy was 1212s E installed. 1212s E 1212s E We have compiled some common reasons and troubleshooting tips at: 1212s E 1212s E https://numpy.org/devdocs/user/troubleshooting-importerror.html 1212s E 1212s E Please note and check the following: 1212s E 1212s E * The Python version is: Python3.14 from "/usr/bin/python3.14" 1212s E * The NumPy version is: "2.2.4" 1212s E 1212s E and make sure that they are the versions you expect. 1212s E Please carefully study the documentation linked above for further help. 1212s E 1212s E Original error was: No module named 'numpy._core._multiarray_umath' 1212s 1212s The above exception was the direct cause of the following exception: 1212s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 1212s return _bootstrap._gcd_import(name[level:], package, level) 1212s test/test_genome.py:12: in 1212s import numpy as np 1212s /usr/lib/python3/dist-packages/numpy/__init__.py:119: in 1212s raise ImportError(msg) from e 1212s E ImportError: Error importing numpy: you should not try to import numpy from 1212s E its source directory; please exit the numpy source tree, and relaunch 1212s E your python interpreter from there. 1212s _______________________ ERROR collecting test/test_io.py _______________________ 1212s ImportError while importing test module '/tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build/test/test_io.py'. 1212s Hint: make sure your test modules/packages have valid Python names. 1212s Traceback: 1212s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 1212s return _bootstrap._gcd_import(name[level:], package, level) 1212s test/test_io.py:11: in 1212s from skgenome import tabio 1212s /usr/lib/python3/dist-packages/skgenome/__init__.py:1: in 1212s from . import tabio 1212s /usr/lib/python3/dist-packages/skgenome/tabio/__init__.py:10: in 1212s import pandas as pd 1212s /usr/lib/python3/dist-packages/pandas/__init__.py:19: in 1212s raise ImportError( 1212s E ImportError: Unable to import required dependencies: 1212s E numpy: Error importing numpy: you should not try to import numpy from 1212s E its source directory; please exit the numpy source tree, and relaunch 1212s E your python interpreter from there. 1212s _______________________ ERROR collecting test/test_r.py ________________________ 1212s ImportError while importing test module '/tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build/test/test_r.py'. 1212s Hint: make sure your test modules/packages have valid Python names. 1212s Traceback: 1212s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 1212s return _bootstrap._gcd_import(name[level:], package, level) 1212s test/test_r.py:14: in 1212s import cnvlib 1212s /usr/lib/python3/dist-packages/cnvlib/__init__.py:1: in 1212s from skgenome.tabio import write 1212s /usr/lib/python3/dist-packages/skgenome/__init__.py:1: in 1212s from . import tabio 1212s /usr/lib/python3/dist-packages/skgenome/tabio/__init__.py:10: in 1212s import pandas as pd 1212s /usr/lib/python3/dist-packages/pandas/__init__.py:19: in 1212s raise ImportError( 1212s E ImportError: Unable to import required dependencies: 1212s E numpy: Error importing numpy: you should not try to import numpy from 1212s E its source directory; please exit the numpy source tree, and relaunch 1212s E your python interpreter from there. 1212s =========================== short test summary info ============================ 1212s ERROR test/test_cnvlib.py 1212s ERROR test/test_commands.py 1212s ERROR test/test_genome.py 1212s ERROR test/test_io.py 1212s ERROR test/test_r.py 1212s !!!!!!!!!!!!!!!!!!! Interrupted: 5 errors during collection !!!!!!!!!!!!!!!!!!!! 1212s ============================== 5 errors in 0.73s =============================== 1212s E: pybuild pybuild:389: test: plugin pyproject failed with: exit code=2: cd /tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build; python3.14 -m pytest test 1212s I: pybuild base:311: cd /tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build; python3.13 -m pytest test 1217s ============================= test session starts ============================== 1217s platform linux -- Python 3.13.9, pytest-8.3.5, pluggy-1.6.0 1217s rootdir: /tmp/autopkgtest.nPG8GW/autopkgtest_tmp/build 1217s configfile: pyproject.toml 1217s plugins: typeguard-4.4.2 1217s collected 70 items 1217s 1228s test/test_cnvlib.py ........... [ 15%] 1454s test/test_commands.py ............................. [ 57%] 1463s test/test_genome.py ................... [ 84%] 1465s test/test_io.py .......... [ 98%] 1486s test/test_r.py . [100%] 1486s 1486s =============================== warnings summary =============================== 1486s test/test_commands.py: 81 warnings 1486s test/test_r.py: 24 warnings 1486s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 1486s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 1486s A typical example is when you are setting values in a column of a DataFrame, like: 1486s 1486s df["col"][row_indexer] = value 1486s 1486s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 1486s 1486s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 1486s 1486s segments.start.iat[0] = bins_start 1486s 1486s test/test_commands.py: 81 warnings 1486s test/test_r.py: 24 warnings 1486s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 1486s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 1486s A typical example is when you are setting values in a column of a DataFrame, like: 1486s 1486s df["col"][row_indexer] = value 1486s 1486s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 1486s 1486s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 1486s 1486s segments.end.iat[-1] = bins_end 1486s 1486s -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html 1486s ================= 70 passed, 210 warnings in 273.37s (0:04:33) ================= 1486s pybuild-autopkgtest: error: pybuild --autopkgtest --test-pytest -i python{version} -p "3.14 3.13" returned exit code 13 1486s make: *** [/tmp/05dqguUrql/run:4: pybuild-autopkgtest] Error 25 1486s pybuild-autopkgtest: error: /tmp/05dqguUrql/run pybuild-autopkgtest returned exit code 2 1487s autopkgtest [14:27:21]: test pybuild-autopkgtest: -----------------------] 1487s pybuild-autopkgtest FAIL non-zero exit status 25 1487s autopkgtest [14:27:21]: test pybuild-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 1488s autopkgtest [14:27:22]: @@@@@@@@@@@@@@@@@@@@ summary 1488s run-unit-test PASS 1488s pybuild-autopkgtest FAIL non-zero exit status 25